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AI-Powered Dashboards: Why AI-Powered Dashboards: Why Enterprises Need Them in 2026

Bold BI by Syncfusion — Tue, 23 Jun 2026 11:26:21 +0000

TL;DR:

Enterprises don’t struggle to collect data, they struggle to turn it into decisions fast.

Traditional dashboards explain what happened; AI-powered dashboards help explain why, predict what’s next, and suggest where to look.

With natural-language exploration and automated insights, teams reduce analyst bottlenecks and scale self-service analytics.

Bold BI® combines AI-assisted analysis, conversational exploration, and embedded dashboards to bring insights into everyday workflows.

Introduction

Organizations have invested heavily in business intelligence tools, yet many teams still struggle to make timely, confident decisions. Product managers depend on analysts for answers, and executives spend valuable time reconciling conflicting metrics across multiple tools.

The challenge isn’t access to data; it’s the speed and clarity of decision-making. Traditional dashboards are designed to show what happened, but they rarely explain why it happened or what actions should follow. As decision windows continue to shrink in fast-moving markets, relying solely on historical visualization is no longer enough.

This is where AI-powered dashboards are reshaping modern analytics. By moving beyond static reports, they enable teams to uncover insights faster, understand underlying drivers, and act with confidence in real time.

What are AI‑powered dashboards?

AI-powered dashboards combine BI with AI techniques (such as anomaly detection, forecasting, and natural-language querying) to surface insights proactively, not just visualize metrics. Unlike traditional dashboards that simply display information, AI-powered dashboards actively assist users by:

Automatically surface notable changes in KPIs (spikes, drops, trend breaks).
Detect anomalies and outliers that warrant investigation.
Provide plain-language explanations for metric movement (drivers, segments, contributing factors).
Forecast likely outcomes based on historical patterns.
Answer questions via natural-language prompts (with governed data access).
Flag risks and opportunities so teams know what to do next.

AI-powered dashboards help users understand what changed, explore potential contributing factors, and uncover insights faster through AI-assisted analysis.

Why AI-powered dashboards matter in 2026

In 2026, organizations need analytics tools that do more than display historical data. With more data, more tools, and shorter decision windows, AI-powered dashboards help teams understand changes faster, reduce analyst dependency, and make confident decisions.

2026 analytics challenge	Why it matters	How AI-powered dashboards help
More data across more tools	Teams struggle to identify what needs attention across disconnected systems.	AI surfaces trends, anomalies, and key changes automatically.
Shorter decision windows	Delayed analysis can slow revenue, operations, and customer decisions.	Users can ask questions, explore data, and get answers faster.
Limited analyst capacity	Business teams often wait for analysts to explain metrics or build follow-up reports.	Natural language exploration helps users investigate insights independently.
Static dashboard limitations	Traditional dashboards show what happened but often do not explain why.	AI helps explain performance changes and highlight possible risks.
Growing self-service expectations	Users need easier ways to understand and act on data without technical support.	AI-powered dashboards turn dashboards into active decision-support tools.

These challenges explain why AI-powered dashboards are becoming more than a reporting upgrade. They help teams move from reviewing data to acting on insights. Here are the key use cases for AI-powered dashboards.

How different industries use AI-powered dashboards

AI-powered dashboards create value in different ways depending on the business function. The following examples show how teams use them to address specific challenges in sales, education, retail, and healthcare.

1. Improve sales forecasting

AI-powered dashboards analyze pipeline activity, revenue trends, order volume, and channel performance to identify risks and forecast future sales more accurately. This helps sales teams prioritize high-value opportunities and respond faster to changes in pipeline health.

AI-powered sales forecasting

Result: More accurate forecasts, better pipeline visibility, and faster revenue decisions.

2. Identify student performance risks earlier

AI-powered dashboards analyze enrollment trends, student performance, graduation rates, and academic outcomes to uncover performance gaps and emerging risks. This helps educational institutions take timely action and improve planning across departments or campuses.

Early student risk detection

Result: Earlier intervention, better academic visibility, and more informed planning.

3. Optimize inventory and merchandising

AI-powered dashboards analyze product sales, inventory movement, store performance, and customer behavior to identify demand patterns and purchasing trends. This helps retail teams reduce stockouts, avoid overstocking, and improve merchandising decisions.

Inventory and merchandising optimization

Result: Smarter inventory planning, better promotions, and faster retail decisions.

4. Improve healthcare operational visibility

AI-powered dashboards analyze patient intake, staffing levels, readmission trends, and departmental performance to identify operational pressure points. This helps healthcare administrators allocate resources more effectively and respond faster to changing operational demands.

Healthcare operations visibility

Result: Better operational visibility, smarter resource planning, and faster response times.

While AI-powered dashboards can help organizations make faster decisions, not every analytics platform delivers these capabilities in the same way.

Many BI tools provide AI features, but organizations often need more than AI-assisted analysis. They also need embedded analytics, conversational data exploration, self-service capabilities, and the flexibility to deliver insights directly within products and business workflows.

This is where Bold BI stands out by combining AI-powered analytics, embedded BI, and SaaS-ready capabilities within a single platform.

How Bold BI delivers AI‑powered dashboards

Organizations evaluating AI-powered dashboards often need more than AI-assisted analysis. They need a platform that combines AI-powered insights, embedded analytics, self-service capabilities, and flexible deployment options. Bold BI brings these capabilities together in a single solution.

1. AI-powered analytics and conversational insights

Many organizations struggle to turn dashboard data into actionable answers. Bold BI addresses this with AI Copilot, AI Agent, Smart Narrations, and Prompt-to-Dashboard capabilities that help users create visualizations, explore data, and understand trends using natural language.

They reduce the amount of manual exploration required by surfacing insights and enabling natural language analysis.

AI copilot in analytics

Smart narrations

Prompt-to-dashboard creation

Learn more about these AI-powered analytics features.

2. Embedded AI analytics for applications and SaaS products

Many BI platforms offer AI capabilities within standalone analytics environments. Bold BI enables organizations to embed dashboards and AI-powered analytics directly into customer portals, internal applications, and SaaS products.

This helps organizations bring analytics closer to where decisions happen, allowing users to access insights directly within the applications they already use.

Embedded AI analytics

Read: How to Embed AI Assistant in Your Software Application to learn more.

3. Unified AI Agent: Analyze data through conversational interfaces

Organizations often need answers that span multiple dashboards, reports, and datasets. Bold BI's Unified AI Agent provides a centralized conversational experience where users can analyze data, review dashboard summaries, explore insights, and interact with analytics from a single interface.

By bringing dashboard exploration, AI-generated insights, and data analysis together in one place, Unified AI Agent helps teams reduce context switching, accelerate discovery, and make faster, more informed decisions across the organization.

Unified AI agent

Watch our Talk to Your Data: Generative AI-Powered Dashboards in Bold BI [Webinar] for detailed insights.

4. Built for SaaS and multi-tenant environments

SaaS organizations need to deliver analytics securely across multiple customers while maintaining data isolation and tenant-level control.

Bold BI supports multi-tenant architecture, role-based access controls, and row-level security that help organizations deliver customer-facing analytics while maintaining security and governance.

Refer to our “Harnessing Multitenancy: Empowering Insights with Embedded BI” blog for more information.

5. Flexible deployment and enterprise readiness

Organizations have different infrastructure, security, and compliance requirements. Bold BI supports cloud and on-premises deployments, allowing teams to align analytics with their technology strategy.

This helps organizations scale analytics while maintaining control over data, users, and infrastructure.

Explore our “Choosing the Right BI Deployment Model: Cloud vs On-Premises“ blog for detailed insights.

Real-world use case of AI-powered dashboards

AI-InteleKt needed an analytics solution that could be embedded in its application and support customizable dashboards and self-service analytics. By adopting Bold BI, the company gained an easy-to-embed platform, simplified dashboard creation, and accelerated product delivery through a more integrated analytics experience. This demonstrates how organizations can combine AI-powered analytics, embedded dashboards, and self-service capabilities within a single platform.

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Ready to accelerate decision-making with AI-powered dashboards?

AI-powered dashboards are transforming analytics from passive reporting to active decision support. Instead of only showing what happened, they help teams understand performance changes, identify risks earlier, and decide what to do next.

For CTOs, CIOs, and product managers, this means less reliance on manual analysis, greater adoption of self-service analytics, and faster access to insights across teams and applications.

Bold BI® supports this shift by combining AI-powered analytics, embedded dashboards, conversational data exploration, and flexible deployment options in one platform. This helps organizations bring insights closer to everyday workflows and makes analytics a more practical part of decision-making.

Ready to accelerate decision-making with AI-powered dashboards?

Start a free trial.
Request a personalized AI-powered dashboards demo.

Related Resources

Frequently asked questions

1.
What are AI-powered dashboards?
AI-powered dashboards combine BI with AI techniques such as anomaly detection, forecasting, natural-language querying, and automated insight generation to help users understand and explore data more effectively.
2.
How do AI-powered dashboards differ from traditional dashboards?
AI-powered dashboards may include capabilities such as anomaly detection, natural language analysis, predictive insights, and AI-assisted recommendations, depending on the platform.
3.
Can AI-powered dashboards predict customer churn?
Yes. Many AI-powered dashboards use predictive analytics models to identify customers who may be at risk of leaving.
4.
Are AI-powered dashboards suitable for small businesses?
Yes. Many modern BI platforms provide AI-powered capabilities that are accessible to organizations of all sizes.
5.
What features should an AI-powered dashboard platform include?
Look for predictive analytics, natural language queries, automated insights, data integration, interactive dashboards, and strong governance capabilities.
6.
Why are AI-powered dashboards becoming important in 2026?
Organizations are managing larger volumes of data and making decisions in increasingly shorter timeframes. AI-powered dashboards help teams identify trends, understand performance changes, and access insights faster without relying heavily on manual analysis.

Integrate MySQL with Elasticsearch using Bold Data Hub

Bold BI by Syncfusion — Tue, 23 Jun 2026 11:23:56 +0000

TL;DR: Many organizations store transactional data in MySQL while search logs and event data live in Elasticsearch. Analyzing these datasets together is often difficult because they exist in separate systems. With Bold Data Hub, you can integrate MySQL and Elasticsearch into a unified dataset and build interactive dashboards in Bold BI® without writing complex data pipelines.

Introduction

Managing data across multiple systems is a common challenge for developers. Key information often lives in MySQL, while logs, search activity, and system events sit separately in Elasticsearch. Without combining these datasets, teams often rely on manual exports, custom scripts, or complex data pipelines. This is where MySQL and Elasticsearch integration are needed.

By integrating MySQL and Elasticsearch sources, organizations can analyze operational data and search behavior together, creating a complete view of business performance.

In this blog, you’ll learn how to use Bold Data Hub to connect MySQL and Elasticsearch, combine them into a unified dataset, and publish the result in Bold BI for efficient data visualization. Let’s get started.

Benefits of integrating MySQL and Elasticsearch data for analytics

Combining MySQL and Elasticsearch data in one dashboard provides the following benefits:

Better search and transaction analysis: By combining MySQL transactional data with Elasticsearch search logs, teams can analyze how users search for products and how those searches translate into purchases.
Unified analytics across operational and search data: MySQL stores structured business data such as orders and users, while Elasticsearch stores indexed search or event data. Integrating both allows dashboards to correlate operational metrics with user behavior.
Reduced manual data pipelines: Without integration tools, developers often export MySQL data and combine it with Elasticsearch using custom scripts. Bold Data Hub simplifies this by combining both sources in a single pipeline.
Build a performance monitor dashboard: Once the datasets are combined, you can create dashboards with real-time insights without writing custom scripts.

Challenges of integrating MySQL and Elasticsearch

Keeping both systems synchronized becomes difficult as data updates occur, causing inconsistencies between stored and indexed data.
Mapping and schema mismatches arise as structures differ, creating indexing issues and unpredictable search results.
Sync failures happen when pipelines break or delays occur, resulting in missing, outdated, or incomplete records.
Query translation is required because SQL logic doesn’t align with Elasticsearch, increasing development time and complexity.

How Bold Data Hub simplifies data integration

Unified data connectivity brings all your sources together, making it easy to connect databases, files, cloud apps, and APIs from one place.
Automated ETL workflows handle data movement from end to end, ensuring accurate and consistent processing without manual effort.
Powerful data transformation lets you clean, format, and reshape information using clear, customizable rules.
Centralized monitoring and management give full visibility into every pipeline, making performance and data quality easy to track.

Now that you understand how Bold Data Hub simplifies data integration, let’s explore how to connect MySQL and Elasticsearch to the Bold Data Hub.

Integrate MySQL with Elasticsearch data using Bold Data Hub

Bold Data Hub lets you easily combine MySQL and Elasticsearch data in one workspace, enabling unified visualizations and reducing manual preparation.

Step 1: Prepare the data sources

Before integration, ensure that:

MySQL database contains the required tables (for example: user data).
Elasticsearch contains relevant indexes (for example: feedback data).
Both datasets include a common field that can be used as a join key. [caption id="" align="alignnone" width="1484"] Sample data in MySQL[/caption]

[caption id="" align="alignnone" width="1484"] Sample data in MySQL[/caption]

This field allows datasets from MySQL and Elasticsearch to be linked together.

Step 2: Create a data pipeline in Bold Data Hub

Open Bold BI and launch Bold Data Hub. [caption id="" align="alignnone" width="1484"] Click the Bold Data Hub icon[/caption]
Next, select the plus (+) icon to create a pipeline. In this tutorial, the pipeline is named elasticsearch_mysql to represent the integration between Elasticsearch and MySQL. [caption id="" align="alignnone" width="1484"] Create the elasticsearch-mysql pipeline [/caption]
Add the MySQL data and configure it with your host, port, database, and credentials to extract the required tables. Example configuration:

version: 1.0.1

plugins:

extractors:

- name: mysql

connectorname: MySQL

config:

host: localhost

port: 3306

database: sales_db

username: root

password: ********

This configuration extracts transactional data from the MySQL database.

[caption id="" align="alignnone" width="1484"] Extract MySQl data in Bold Data Hub[/caption]

Step 3: Add Elasticsearch as a data source

Next, configure the Elasticsearch connector inside the same pipeline with your host, port, database, and credentials to extract the required tables.

Example configuration:

version: 1.0.1

plugins:

extractors:

name:     elastic_search

connectorname: Elasticsearch

config:

host: http://localhost:9200

index: product_feedback

username: elastic

password: ********

This allows the pipeline to retrieve search or event data stored in Elasticsearch.

[caption id="" align="alignnone" width="1484"] Add Elasticsearch data in Bold Data Hub[/caption]

Step 4: Save the datasets

After combining the MySQL and Elasticsearch datasets in the pipeline, select Save to store the unified configuration.

[caption id="" align="alignnone" width="1484"] Click the save button[/caption]

Once saved, a window will appear displaying the newly combined data source, confirming that the integration has been successfully created and is ready for use in the Data Source page.

[caption id="" align="alignnone" width="1484"] Data source listing page[/caption]

With the data source, you can join the tables from both MySQL and Elasticsearch to unify the information into a single, consistent structure.
Next, we will learn how to perform these joins using tables.

Joining tables

Joining tables allows you to combine information from different sources into a single analytical view. This makes it possible to correlate transaction data stored in MySQL with search or event data stored in Elasticsearch, enabling deeper analysis across systems.
The steps below show you how to create the join.

Step 1: Open the data source editor

Open the action menu for the created data source and select Edit Data Source.

[caption id="" align="alignnone" width="1484"] Edit the data source[/caption]

Step 2: Identify the common fields

The design area opens automatically. Drag and drop the tables from the left panel into the design area. In this case, we dragged products and sales_feedback tables.

[caption id="" align="alignnone" width="1484"] Identify common fields[/caption]

Step 3: Define the join condition

Once you drag and drop the second table into the designer, the query joiner window will open. In the Query Joiner window, define a join using a key.

Example:

mysql.products.product_id = elasticsearch.feedback_id

After configuring the join condition, select Update to apply the changes.

[caption id="" align="alignnone" width="1484"] Query Joiner window[/caption]

Step 4: Preview the combined datasets

Once the join is applied, the design page automatically displays a preview of the merged dataset. This allows you to verify that the data from both sources has been combined correctly.

[caption id="" align="alignnone" width="1484"] Preview of the combined datasets[/caption]

Next, let’s explore the key metrics you can build and visualize in the interactive dashboard.

How to create a dashboard using MySQL and Elasticsearch data

New users can begin by exploring our video on how to create a dashboard in Bold BI, which provides a quick overview of the dashboard-building process.

After connecting both MySQL and Elasticsearch as data sources, the next step is to blend the datasets to define meaningful metrics and KPIs. With the combined data, you can build a dashboard that highlights insights such as:

Total purchases.
Average render time.
Sales payment by type and browser.
Abandoned carts.
Abandoned revenue.
Products sold breakdown.
Page visits by traffic and device type.

These insights help organizations understand both customer intent and business outcomes. With Bold BI, you can easily transform the integrated dataset into interactive dashboards for decision-making.

[caption id="" align="alignnone" width="1484"] Preview of the dashboard[/caption]

Integrating MySQL with Elasticsearch data enables users to enjoy faster searches, improved data analysis, and more efficient access to insights from large datasets. To learn tips for designing effective dashboards, explore our article on 10 dashboard design best practices for insights.

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Start building unified analytics today

Integrating MySQL and Elasticsearch enables organizations to combine structured business data with advanced. With Bold Data Hub, developers can build a unified data pipeline without writing complex ETL workflows. Once the data is integrated, Bold BI dashboards make it easy to explore trends, monitor performance, and share insights across teams.

If you want to simplify MySQL and Elasticsearch integration and build powerful analytics dashboards, try Bold BI® today. Start your 30-day free trial or request a personalized demo to see how Bold BI and Bold Data Hub can help your team turn scattered data into actionable insights.

Frequently asked questions

1.
What data sources does Bold Data Hub support?

Bold Data Hub supports a wide range of data sources, including files, web APIs, SQL databases, and NoSQL systems. This includes popular options such as MySQL and Elasticsearch.
2.
Do I need any technical expertise to use Bold Data Hub?

No advanced technical expertise is required. A basic understanding of the data connectors you plan to use is sufficient to get started.
3.
Does Bold Data Hub offer additional features?

Yes. You can configure multiple datastores of the same server type and load data into them for different projects. It also includes an Open API connector and a Python connector for more advanced use cases.
4.
Do I need Bold Data Hub to use Bold BI?

No. Bold BI can connect directly to many supported data sources. Bold Data Hub is optional and is especially useful when you need to prepare, clean, or combine data, such as blending MySQL and Elasticsearch data, before building dashboards.

7 Best Looker Alternatives You Should Explore in 2026

Bold BI by Syncfusion — Mon, 22 Jun 2026 12:43:33 +0000

TL;DR: Looking for Looker alternatives in 2026? As analytics needs evolve, many teams evaluate the BI tools that support embedded analytics, customization, and different deployment models. This guide highlights seven options to consider and what teams often review when choosing a platform for their data, users, and business goals.

Introduction

Business intelligence today is increasingly focused on delivering fast, accessible, and actionable insights, not only on building dashboards. Looker is often used for governed, semantic modeling and analytics, including in data stacks built around SQL warehouses and LookML.

Depending on your deployment preferences, customization needs, embedded analytics requirements, or pricing considerations, you may want to evaluate alternative BI platforms. As analytics strategies continue to evolve, many teams in 2026 are exploring solutions that support different modeling approaches, deployment options, and workflow-oriented user experiences.

In this guide, we highlight seven Looker alternatives and outline the types of capabilities organizations commonly assess when selecting a business intelligence platform.

What to consider when choosing Looker alternatives

Choosing the right Looker alternative requires knowing which capabilities will help future‑proof your analytics stack. Here are the essential features to look for:

Effortless self-service features: Choose a platform that allows business users to explore data, apply filters, and build dashboards with minimal SQL or technical assistance for most common workflows.
AI‑assisted analytics: Look for solutions that offer AI‑assisted features, such as natural‑language querying, suggested visualizations, or automated summaries, where available, to help teams analyze data faster (capabilities may vary by edition and configuration).
Flexible embedded analytics: Select a BI tool that supports embedding dashboards, KPIs, and reports into your applications or SaaS products so insights fit directly into user workflows.
Easy and broad data integration: Ensure the platform offers connectors and APIs for cloud warehouses, SaaS tools, databases, spreadsheets, REST APIs, and (where needed) streaming sources to support unified reporting.
Tailored pricing: Choose a BI solution with transparent pricing that clearly outlines costs for connectors, embedding, or collaboration, helping reduce surprises as usage grows.
Faster setup and lower learning curve: Choose analytics tools with intuitive interfaces, drag‑and‑drop dashboards, and straightforward deployment options that can reduce onboarding time across teams.
Strong collaboration and governance: Look for built-in collaboration features like secure sharing, role-based access, and workspace governance to keep teams aligned and data protected.
Real-time, high-performance analytics: Select a platform that supports live data connections and fast dashboard rendering, with performance that can remain responsive as data volumes and concurrency grow (depending on data model and infrastructure).

Here are some leading embedded analytics platforms to consider in 2026.

7 best Looker alternatives you should explore in 2026

Many teams explore alternatives to Looker to find the right fit for their embedding requirements, modeling approach, deployment preferences, and budget. Below are seven platforms to consider in 2026.

1. Bold BI

Bold BI® is an embedded analytics platform used by teams that want to build, share, and embed dashboards inside apps or websites. Bold BI is often evaluated by organizations looking for scalable, customizable dashboards and configurable deployment options.

Key features of Bold BI:

Tailored pricing: Bold BI offers pricing options based on factors such as number of users and deployment type.
Embedding flexibility: Bold BI supports embedding across many popular frameworks, helping teams integrate analytics into existing web applications with control over UI, behavior, and user experience.
AI-assisted dashboard creation: Bold BI can generate dashboards using natural language, reducing the time spent on manual modeling and configuration so teams can move from questions to visuals faster.
Anonymous user embedding: Bold BI supports anonymous user embedding with group-based permissions, which can be useful for public portals, customer-facing apps, and scenarios where users aren’t required to use individual accounts.
Broader deployment options: Bold BI supports multiple deployment options, including Windows, Linux, Docker, Kubernetes, and Azure App Service.
Better dashboard navigation: Bold BI includes tab widgets that help organize complex dashboards into structured sections.
Live web connectors: Bold BI supports live web data connections, allowing dashboards to read from web-based sources in real time, where supported.
Custom email templates: Bold BI lets you create and manage custom email templates within the platform, supporting more consistent branding and better control over scheduled reports and notification workflows.

Explore our comparison page to learn more about why you should choose Bold BI over Looker.

2. Zoho Analytics

Zoho Analytics is a self-service BI platform known for intuitive dashboards, strong AI assistance, and broad data connectivity. It’s popular with SMBs thanks to its affordability and ease of use.

Key features of Zoho Analytics:

Easy drag-and-drop visualizations: Build dashboards quickly using 50+ chart types with a simple visual builder.
Built-in data preparation: Clean and transform data with native prep tools to improve reporting accuracy.
Automatic data blending: Combine data from multiple sources automatically for unified analysis.
Advanced AI with Zia: Ask questions in natural language, get automated insights, and detect anomalies faster.

3. Sisense

Sisense is an end-to-end analytics platform built for scalable, customizable embedded analytics. It excels at handling complex datasets and powering tailored analytics experiences.

Key features of Sisense:

Flexible pricing models: Choose pricing options that better match your scale, usage, and deployment needs.
Advanced customization across environments: Customize embedded analytics consistently across dev, staging, and production.
AI throughout the analytics lifecycle: Use AI across preparation, analysis, and insights to surface patterns faster.
Open, flexible data modeling: Adapt modeling to different data architectures for smoother integration and growth.

4. Domo

Domo is a cloud‑native BI platform offering end-to-end data integration, analytics, automation, and collaboration. It centralizes large, diverse data sources and supports deep embedding use cases.

Key features of Domo:

Native integrations: Connect to a broad library of prebuilt connectors across common business applications and data sources (availability varies).
Collaboration and alerts: Collaboration features and alerting options are available, depending on deployment and configuration.
Predictive AI capabilities: AI-assisted and predictive features are supported in certain scenarios.
Visualization options: Use a wide set of visualization types and dashboard design options.

5. Tableau

Tableau is a global leader in visual analytics that’s known for expressive, interactive dashboards and strong enterprise adoption. It’s ideal for teams that prioritize visual storytelling.

Key features of Tableau:

User satisfaction: Tableau consistently earns strong user satisfaction scores due to its easy-to-use interface and highly intuitive drag-and-drop dashboard creation.
Simpler implementation: Users frequently highlight Tableau’s smooth and straightforward setup process, making onboarding faster and less complex.
Data connectivity: Tableau offers wide-ranging support for diverse data sources, enabling seamless integration without limitations.
Rich visualizations: Tableau excels in interactive, dynamic, and rich data visualization, providing a more engaging analytics experience.

6. Qlik Sense

Qlik Sense uses an associative analytics engine that lets users explore data freely, moving beyond linear, SQL-based querying. This enables deeper, more flexible discovery.

Key features of Qlik Sense:

Data visualization: Qlik Sense delivers powerful and visually engaging dashboards, making it the stronger choice for teams that prioritize rich visual analytics.
Data preparation: Qlik Sense offers robust data preparation capabilities and broader connector support, enabling smoother integration across diverse data sources.
Real-time reporting: Qlik Sense provides real-time insights, helping organizations act on data more immediately.
Easier to use: Qlik Sense is generally easier for teams to adopt and navigate, offering an intuitive user experience.

7. ThoughtSpot

ThoughtSpot is redefining business intelligence with its Agentic Analytics Platform, which uses AI agents to deliver live, explainable insights directly into workflows. ThoughtSpot supports search-driven analytics and AI-assisted insights and is often evaluated by organizations looking to embed analytics and enable guided exploration across cloud data sources.

Key features of ThoughtSpot:

AI-powered search analytics: Ask complex questions in natural language and get instant, insight-rich answers.
AI-augmented dashboards: Explore dynamic dashboards that update automatically and support AI-assisted insights, depending on data connectivity and configuration.
Flexible data modeling: Model data with strong governance controls to maintain accuracy and consistency.
Unified analytics across platforms: Connect to cloud platforms and business apps without heavy ecosystem lock-in.

Why Bold BI is the right choice for you

Choosing a Looker alternative often depends on your embedding needs, integration requirements, deployment preferences, governance, and the level of customization required. Bold BI is designed to support embedded analytics scenarios along with configurable deployment and branding options. Feature availability, performance, and cost outcomes can vary based on data volume, usage patterns, and configuration.

Customer example:

Faster integrations without rebuilding workflows

Keyrenter needed stronger business analytics beyond what they were getting from AppFolio, especially for tracking property performance and operational work across markets. They chose Bold BI for its wide data integrations (including AppFolio) and the ability to personalize widgets for clearer market-level reporting.

Outcome: Faster rollout with smoother data connections.
How: Broad integrations and customizable widgets for tailored reporting.
Why it matters: Teams keep momentum and avoid slow, complex rebuilds.

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Get started with Bold BI® by signing up for a free trial and exploring its embedded analytics capabilities firsthand. With guided onboarding, comprehensive resources, and expert support, your team can start delivering insights faster, without a steep learning curve.

Want a closer look before committing? Request a personalized demo to see how Bold BI integrates with your tech stack, supports your deployment needs, and scales with your business. Whether you’re evaluating options or planning a migration, Bold BI makes it easy to take the next step with confidence.

Frequently asked questions

1.
What makes Bold BI a leading Looker alternative?
Bold BI supports embedded analytics, interactive dashboards, a broad range of data connectors, and configurable deployment options. Feature availability can vary by plan and deployment model.
2.
What can you build with Bold BI?
You can build interactive dashboards, KPI reports, embedded analytics views, and data-rich visualizations tailored to any business workflow.
3.
Do I need technical skills to use Bold BI?
No. Bold BI supports drag‑and‑drop dashboard creation, making it easy for non-technical users while still offering advanced options for developers.
4.
Can I share dashboards with my team?
Yes. Bold BI allows secure sharing, scheduling, and collaboration so teams can access insights in real time.
5.
Are Looker alternatives good for startups?
Yes. Many offer flexible pricing, quick setup, and scalable features suited for growing teams.

Live Dashboards: Why Enterprises Are Outgrowing Spreadsheets

Bold BI by Syncfusion — Fri, 19 Jun 2026 13:50:36 +0000

TL;DR: Spreadsheets are effective for early-stage reporting but become difficult to manage as data, users, and business needs grow. Live dashboards provide automated updates, centralized visibility, and scalable analytics. With platforms like Bold BI, organizations can move from manual reporting workflows to real-time, trusted decision-making.

Introduction

For many organizations, reporting starts with spreadsheets. A sales manager tracks pipeline performance in Excel. Finance teams build forecasts using formulas and pivot tables. Operations teams monitor inventory through shared worksheets. Marketing teams maintain campaign reporting spreadsheets.

Initially, everything works. Data volumes are manageable. Reporting happens weekly or monthly. Only a few people need access to information. Then the business grows. More teams depend on data. More systems generate information. More decisions require timely insights. And suddenly, the spreadsheets that once made reporting easy become one of the biggest obstacles to scaling analytics. The issue isn't that spreadsheets stop working.

The issue is that businesses eventually outgrow what spreadsheets were designed to do. In this article, you'll learn:

Why spreadsheet reporting becomes difficult at scale
The hidden business costs of manual reporting
How to know when you've outgrown spreadsheets
Why organizations are moving to live dashboards
What to look for in a spreadsheet alternative
How Bold BI helps organizations modernize analytics

Why Growing Businesses Move Beyond Spreadsheet Reporting

As businesses grow, spreadsheet usage often expands beyond its original purpose. Over time, spreadsheets evolve from reporting tools into the reporting infrastructure itself.

As businesses grow, organizations commonly encounter challenges such as:

Reporting cycles taking longer due to manual data collection, validation, and consolidation.
Inconsistent KPI definitions across departments, leading to conflicting reports.
Delayed visibility into business performance because reports represent a point-in-time snapshot.
Spreadsheet errors affecting revenue forecasts, budgeting, inventory planning, and operational decisions.
Reporting bottlenecks created by a small number of users responsible for maintaining critical spreadsheets.

While these issues may seem operational at first, their broader impact often remains hidden until they affect business outcomes. Let’s explore them in detail.

The Hidden Cost of Spreadsheet-Based Reporting

Spreadsheet reporting doesn't just create extra work. Over time, spreadsheets stop being simple reporting tools and start holding critical business logic through formulas, pivot tables, manual data exports, custom calculations, and user-managed reporting processes.

As reporting requirements grow, teams often find themselves:

Spending more time preparing reports than analyzing results.
Reconciling conflicting metrics across departments.
Waiting for manual updates before making decisions.
Managing multiple versions of the same report.
Relying on a small group of users to maintain critical spreadsheets.

What makes this challenging is that the impact extends beyond reporting itself. When reports depend on manual updates and disconnected files, visibility becomes harder to maintain, metrics become inconsistent, and even small errors can affect planning, forecasting, and operational decisions.

To move beyond these limitations, organizations need a reporting approach that keeps pace with changing data, growing teams, and faster decision-making demands.

Why Organizations Are Moving to Live Data Dashboards

Unlike spreadsheets, live data dashboards connect directly to data sources and automatically update information as data changes. This turns reporting from a weekly snapshot into a continuously updated view of performance.

Live dashboards help organizations achieve this by replacing manual reporting workflows with automated, continuously updated analytics.

Faster decision-making: Leaders gain access to up-to-date business metrics without waiting for scheduled reports.
A single source of truth: Everyone works from the same metrics, definitions, and data sources.
Reduced reporting effort: Automated refreshes reduce repetitive reporting tasks and free teams to focus on insights.
Interactive analytics: Users can filter, drill down, and explore data without rebuilding reports.
Scalable analytics: Data dashboards support growing data volumes, users, and reporting needs without increasing complexity.

By moving from static spreadsheets to live dashboards, organizations can improve visibility, strengthen trust in reporting, and support faster, more consistent decision-making across teams.

Spreadsheet Reporting vs Live Dashboards: What’s the Difference

While spreadsheets remain useful for analysis and ad hoc reporting, live dashboards provide a more scalable approach to monitoring performance, sharing insights, and supporting real-time decision-making.

Business Need	Spreadsheet Reporting	Live Dashboards	Business Impact
Data updates	Manual updates required	Automated refreshes	Reduces reporting effort and delays
Visibility into performance	Point-in-time reporting	Near real-time visibility	Faster decision-making
KPI consistency	Metrics often vary by report owner	Standardized KPI definitions	Greater trust in reporting
Collaboration	Multiple file versions	Centralized data	Improved alignment across teams
Governance and security	Difficult to manage at scale	Role-based access controls	Better compliance and control
Scalability	Complexity grows with users and data	Designed to scale across teams	Supports long-term growth
Decision support	Historical review	Continuous monitoring and analysis	More proactive decision-making
Reporting workload	Significant manual effort	Automated reporting workflows	Frees teams to focus on insights

While live dashboards address many reporting challenges, they don't necessarily replace every use case for spreadsheets.

So, Should Live Dashboards Replace Spreadsheets?

Yes, if reporting has become difficult to maintain, scale, or trust.

However, this doesn't mean spreadsheets need to disappear entirely.

Many organizations use spreadsheets and data dashboards for different purposes:

Spreadsheets support ad hoc analysis, financial modeling, and planning.
Live dashboards provide centralized visibility, automated reporting, and shared business metrics.

A common approach is:

Use spreadsheets for analysis and what-if scenarios.
Use live dashboards for operational reporting, KPI monitoring, and decision-making.

This helps organizations reduce manual reporting effort while improving visibility, consistency, and scalability across teams.

While live dashboards can address many of the limitations associated with spreadsheet-based reporting, the benefits ultimately depend on the platform you choose. To support long-term needs, it's important to evaluate different dashboard software platforms to find solutions that can grow alongside your organization.

What Should You Look for When Evaluating a Dashboard Software Platform?

If your organization has outgrown spreadsheet-based reporting, look for a platform that can:

Connect multiple business systems
Automate reporting workflows
Standardize KPI definitions
Support governance and security
Enable self-service analytics
Scale with organizational growth
Support embedded analytics if customer-facing reporting is required

The goal isn’t simply replacing spreadsheets. It’s building an analytics foundation that supports long-term growth.

These capabilities are especially important when reporting needs extend across departments, data sources, and applications. This is where an embedded analytics platform like Bold BI can help organizations move from spreadsheet-dependent reporting to a more connected, scalable, and governed analytics environment.

Where Bold BI Fits as a Dashboard and Analytics Platform

Organizations moving beyond spreadsheet-based reporting often need more than automated dashboards. They need a platform that helps centralize data, standardize metrics, enable self-service analytics, and deliver insights across teams, applications, and business workflows.

Bold BI supports:

Centralized data connectivity: Connect data from databases, cloud applications, and enterprise systems into a unified analytics environment, reducing dependency on disconnected spreadsheet files.
Automated reporting workflows: Replace manual report preparation with automatically refreshed dashboards that help teams work with current information.
Standardized KPI definitions: Ensure teams use consistent business metrics, reducing confusion caused by different spreadsheet versions, formulas, and report owners.
Self-service analytics: Empower business users to explore data independently without relying on technical teams for every report request.
Governance and security: Manage access through role-based controls, centralized administration, and secure data visibility across users and teams.
Embedded and white-label analytics: Embed data dashboards directly into applications, portals, and software products, and deliver branded analytics experiences for SaaS and customer-facing use cases.
Flexible deployment and scalability: Deploy Bold BI in cloud, self-hosted, private cloud, or hybrid environments based on business, security, and compliance needs while supporting growing users, data volumes, and reporting use cases.

Let's look at how these capabilities help solve common reporting challenges and deliver measurable business value.

From Spreadsheet Challenges to Business Outcomes with Bold BI

Spreadsheet Challenge	Bold BI Capability	Business Outcome
Multiple reporting files	Centralized dashboards	Single source of truth
Manual reporting effort	Automated refresh	Increased productivity
KPI inconsistencies	Standardized metrics	Better alignment
Delayed reporting	Near real-time dashboards	Faster decisions
Reporting bottlenecks	Self-service analytics	Reduced dependency
Governance concerns	Role-based security	Improved control
Growing complexity	Scalable architecture	Long-term scalability
Customer-facing reporting needs	Embedded analytics	Better user experiences

These benefits become even clearer when we look at how organizations apply Bold BI in real-world reporting and analytics scenarios.

How Organizations Use Bold BI in Real World

Moving beyond spreadsheet-based reporting helps organizations reduce manual work, improve visibility, and make faster decisions with trusted data.

Bold BI supports this shift through centralized dashboards, automated reporting, and scalable analytics, as shown in the following customer examples.

Persistent systems improved enterprise analytics delivery

Persistent systems needed to embed analytics into enterprise applications while handling large-scale data.

Outcome: Faster analytics delivery and improved user experience for enterprise applications.
How: Used Bold BI’s embedding capabilities, flexible APIs, and scalable dashboard infrastructure.
Why it matters: Organizations can seamlessly integrate analytics into products without building everything from scratch.

Traffic data exchange enhanced real-time visibility

Traffic data exchange required real-time monitoring of transportation data.

Outcome: Improved visibility into traffic patterns and faster, data-driven decision-making.
How: Leveraged Bold BI’s real-time data connectivity and interactive visualizations.
Why it matters: Live dashboards enable organizations to act on time-sensitive data instead of relying on delayed reports.

Community License users accelerated analytics adoption

Organizations using Bold BI’s Community License moved from spreadsheet-based reporting to centralized dashboards with minimal upfront investment.

Outcome: Faster adoption of modern analytics with less manual reporting.
How: Ready-to-use dashboards, automated refresh, and intuitive design tools.
Why it matters: Teams can modernize reporting quickly and scale as needs grow.

These examples show how organizations transition from manual, spreadsheet-driven processes to scalable, real-time analytics environments using Bold BI.

Ready to Scale Beyond Spreadsheet Reporting with Bold BI?

Moving beyond spreadsheet reporting isn't about abandoning spreadsheets entirely. It's about recognizing when manual reporting workflows can no longer support the speed, visibility, and consistency a growing business needs.

If your organization:

Spends too much time preparing and validating reports.
Struggles with inconsistent KPIs across teams.
Relies on outdated spreadsheet snapshots for business decisions.
Needs centralized dashboards, automated reporting, or embedded analytics.

Then continuing to rely on spreadsheets as your primary reporting system may not be the most scalable approach.

In these scenarios, Bold BI can help organizations modernize their reporting with interactive dashboards, automated data refreshes, self-service analytics, and embedded BI.

Ready to eliminate manual reporting bottlenecks and gain real-time visibility across your business?

Start a free trial or schedule a personalized demo to see how Bold BI can help you move from spreadsheet-based workflows to scalable business intelligence.

Frequently asked questions

1. 1.
  Why does spreadsheet reporting break down as businesses grow?
  As organizations scale, reporting becomes more complex. More users, more systems, larger datasets, and increased reporting demands make spreadsheet-based workflows difficult to maintain efficiently.
2. 2.
  What are the biggest limitations of spreadsheet reporting?
  Common limitations include manual updates, inconsistent metrics, reporting delays, governance challenges, and limited scalability.
3. 3.
  When should a company move from spreadsheets to dashboards?
  Organizations should consider live dashboards when reporting becomes time-consuming, multiple teams depend on shared metrics, or business decisions require faster access to data.
4. 4.
  Do dashboards replace spreadsheets entirely?
  No. Spreadsheets remain valuable for modeling and ad hoc analysis. Dashboards improve ongoing reporting, visibility, collaboration, and decision-making.
5. 5.
  How does Bold BI help organizations modernize reporting?
  Bold BI centralizes data, automates reporting, standardizes KPIs, enables self-service analytics, supports embedded analytics, and scales dashboard delivery

Embed Analytics in a Blazor Application Using JavaScript

Bold BI by Syncfusion — Fri, 19 Jun 2026 13:01:26 +0000

TL;DR: Bold BI® lets you embed interactive dashboards in Blazor Server apps using the Bold BI JavaScript SDK and token-based embed authentication, so users can explore analytics without leaving your application.

Introduction

Users expect analytics to be available inside the tools they use every day, but building dashboard functionality from scratch is time-consuming and complex. Bold BI supports dashboard embedding in Blazor applications, enabling developers to quickly integrate interactive analytics via JavaScript embedding. In this guide, you will learn how to embed a Bold BI dashboard in a Blazor application using JavaScript embedding.

Let’s get started.

Why Embed Dashboards in Blazor Applications?

Embedded analytics brings insights directly into the application experience and closes workflow gaps. When dashboards exist in separate tools, users switch between systems, which slows workflows and reduces efficiency. Key advantages include:

Unified user experience: Keeping dashboards within the application provides a seamless interface where users can access data without leaving their workflow.
Faster decision-making: Real-time access to analytics enables users to take immediate action based on up-to-date insights.
Centralized security control: Authentication and access can be managed within the application, ensuring consistent and secure data governance.
Higher user engagement: Embedding analytics into daily workflows encourages users to interact more frequently with dashboards and insights.
Scalability and flexibility: Embedded analytics makes it easier to extend insights across users, teams, and applications without rebuilding systems.

Next, let's walk through the process of integrating a Bold BI dashboard into a Blazor Server application.

How to Embed a Dashboard in a Blazor Application

The following steps demonstrate how to configure embedding, set up the required components, and render dashboards in a Blazor Server application using JavaScript embedding.

Prerequisites

Before you begin, ensure the following requirements are met:

Step 1: Set Up a Blazor Server App

Start by creating a new Blazor Server project.

Create a folder in your desired location and open it in Visual Studio Code. Open the terminal and run:

dotnet new blazorserver

This will serve as the application where your dashboard will be embedded.

Step 2: Enable Embedding in Bold BI

Next, prepare your dashboard for embedding:

Select the dashboard you want to embed

Go to Settings → Embed

Enable Embed Authentication
Click Embed Configuration.
Download embedConfig.json file.

This file contains all the essential details needed to connect your app to Bold BI.

Step 3: Add the Embed Configuration File

Copy the downloaded embedConfig.json file and place it in the root directory of your Blazor Server application, as shown below.

Your application will read this file to retrieve values such as:

Server URL
Dashboard ID
User email
Embed secret

Step 4: Create Model Classes

The application uses model classes to store the embedding configuration and dashboard details retrieved from the Bold BI server. Create a new folder named Models and add the following classes:

EmbedClass.cs: Handles the request payload from the client.
EmbedDetails.cs: Stores values loaded from embedConfig.json.
GlobalAppSettings.cs: Maintains and reuses the EmbedDetails settings throughout the application.

Before creating the classes, install the required packages:

dotnet add package Newtonsoft.Json 
dotnet add package System.Runtime.Serialization.Primitives

Make sure to include the required namespaces such as Newtonsoft.Json and System.Runtime.Serialization.

[DataContract]
public class EmbedClass
{
[DataMember]
public string embedQueryString { get; set; }
[DataMember]
public string DashboardServerApiUrl { get; set; }
}

public class EmbedDetails
{
public string Environment { get; set; }

public string SiteIdentifier { get; set; }

public string ServerUrl { get; set; }

public string EmbedSecret { get; set; }

public string UserEmail { get; set; }

public string EmbedType { get; set; }

public string DashboardId { get; set; }
}

Step 5: Add the Bold BI JavaScript SDK

To render dashboards, add the Bold BI JavaScript SDK and supporting scripts to the _Host.cshtml page.

Configure the following values:

serverUrl
dashboardId
siteIdentifier
environment
embedType
authorizationServerUrl

Then call the renderDashboard() method to initialize and load the dashboard.

<head>
<title>BoldBI Blazor Embed</title>
<script type="text/javascript" src="https://cdn.boldbi.com/embedded-sdk/latest/boldbi-embed.js"></script>
<script type="text/javascript" src="~/js/Index.js"></script>
<link rel="stylesheet" href="~/css/site.css" />
<script type="text/javascript">
var rootUrl = "@ViewBag.ServerUrl";
var dashboardId = "@ViewBag.DashboardId";
var siteIdentifier = "@ViewBag.SiteIdentifier";
var environment = "@ViewBag.Environment";
var embedType = "@ViewBag.EmbedType";
var authorizationServerUrl = "@Url.Action("AuthorizationServer", "EmbedData")";
</script>
</head>

<body onload="renderDashboard(dashboardId)">
<div id="viewer-section" style="width: 100%;">
<div id="dashboard"></div>
</div>
</body>

Step 6: Create the Authorization API

Create a Controllers folder and add a controller named EmbedDataController.cs.

This controller performs three important tasks:

Reads the embed query string.
Generates a secure signature using the Embed Secret.
Requests an access token from the Bold BI server and returns it to the Blazor application.

Keeping token generation on the server ensures that the Embed Secret is never exposed to the browser.

[HttpPost("[action]")]
[Route("AuthorizationServer")]
public string AuthorizationServer([FromBody] object embedQuerString)
{
var embedClass = JsonConvert.DeserializeObject<EmbedClass>(embedQuerString.ToString());
var embedQuery = embedClass.embedQuerString;
// Use your user email as embed_user_email
embedQuery += "&embed_user_email=" + GlobalAppSettings.EmbedDetails.UserEmail;
//To set embed_server_timestamp to overcome the EmbedCodeValidation failing while the client application is in different time zone.
double timeStamp = (int)DateTime.UtcNow.Subtract(new DateTime(1970, 1, 1)).TotalSeconds;
embedQuery += "&embed_server_timestamp=" + timeStamp;
var embedDetailsUrl = "/embed/authorize?" + embedQuery + "&embed_signature=" + GetSignatureUrl(embedQuery);

using (var client = new HttpClient())
{
client.BaseAddress = new Uri(embedClass.dashboardServerApiUrl);
client.DefaultRequestHeaders.Accept.Clear();

var result = client.GetAsync(embedClass.dashboardServerApiUrl + embedDetailsUrl).Result;
string resultContent = result.Content.ReadAsStringAsync().Result;
return resultContent;
}
}

public string GetSignatureUrl(string message)
{
var encoding = new System.Text.UTF8Encoding();
var keyBytes = encoding.GetBytes(GlobalAppSettings.EmbedDetails.EmbedSecret);
var messageBytes = encoding.GetBytes(message);
using (var hmac = new System.Security.Cryptography.HMACSHA256(keyBytes))
{
var hashMessage = hmacsha1.ComputeHash(messageBytes);
return Convert.ToBase64String(hashMessage);
}
}

Step 7: Configure Program.cs

Before calling app.Run(), add the controller route configuration:

Register the EmbedDataController
Configure the default routing pattern
Load and deserialize the embedConfig.json file

The configuration values are stored in GlobalAppSettings and used throughout the embedding process.

app.MapControllerRoute(
name: "default",
pattern: "{controller=EmbedData}/{action=EmbedConfigErrorLog}/{id?}");

string basePath = AppDomain.CurrentDomain.BaseDirectory;
string jsonString = System.IO.File.ReadAllText(Path.Combine(basePath, "embedConfig.json"));
GlobalAppSettings.EmbedDetails = JsonConvert.DeserializeObject<EmbedDetails>(jsonString);

Step 8: Run the Application

Once the configuration is complete, run the application using:

dotnet watch run

Step 9: Verify the Dashboard

After running the application, the dashboard is rendered securely inside the Blazor Server application.

To learn more about implementation details and explore sample projects, refer to the official Bold BI guide for ''How to Embed a Bold BI Dashboard in a Blazor Server Application''.

How Dashboard Embedding Works in a Blazor Server Application

The Blazor Server sample follows a sequence of configuration loading, secure token generation, token handling, and dashboard rendering.

How It Works

Blazor application requests a secure embed token.
Authorization API generates the request and signature.
Bold BI server validates the embed secret and request.
Bold BI server returns the embed token.
Authorization API returns the token to the Blazor application.
Blazor application uses the token with the Bold BI JavaScript SDK to render the dashboard.

To learn more, refer to the official Bold BI documentation on how the Blazor Server sample works.

Why Choose Bold BI for Dashboard Embedding?

Bold BI® is well suited for embedding analytics in Blazor applications, enabling secure and flexible integration into existing workflows. With token-based authentication, interactive dashboards, and scalable embedding options, it helps developers deliver seamless analytics experiences. With Bold BI, organizations can:

Embed dashboards directly within application workflows
Maintain secure access through token-based authentication
Deliver interactive analytics experiences to users
Support role-based access control and governance
Accelerate analytics delivery without building analytics infrastructure from scratch
Scale analytics across users, teams, and applications
Support multi-tenant analytics for SaaS applications

Whether you are building SaaS platforms, internal applications, or customer-facing portals, Bold BI provides a reliable way to integrate analytics into your application.

Ready to get started? Try embedding a dashboard in your Blazor Server application and see how Bold BI can bring interactive data insights directly into your workflows. For deeper guidance, start a free trial, explore the documentation, or request a demo to see how it fits your use case.

Frequently Asked Questions

Can I embed multiple dashboards in a Blazor Server application?
Yes. You can embed multiple Bold BI dashboards in the same Blazor Server application by configuring separate dashboard instances.
How can I secure embedded dashboards for different users?
Use authentication and role-based access control to ensure users only access the dashboards and data they are authorized to view.
Is authentication required for embedding?
Yes. Authentication is recommended to protect embedded dashboards from unauthorized access.
Can I customize dashboard appearance?
Yes. You can customize themes, colors, layouts, and other UI elements to match your application’s branding.
Does embedding dashboards affect application performance?
Performance depends on dashboard complexity, data volume, and network conditions. With proper configuration, embedded dashboards can run efficiently in Blazor applications.
What are the prerequisites for embedding a Bold BI dashboard in Blazor?
You need a Bold BI environment, a published dashboard, embedding enabled, required configuration values, and a Blazor application set up for integration.
How do I embed analytics in a Blazor Server application?
Enable dashboard embedding, configure authentication, add the required settings, and render the dashboard using JavaScript interop.

Embed Analytics in a Blazor Application Using JavaScript

Bold BI by Syncfusion — Fri, 19 Jun 2026 13:01:26 +0000

TL;DR: Bold BI® lets you embed interactive dashboards in Blazor Server apps using the Bold BI JavaScript SDK and token-based embed authentication, so users can explore analytics without leaving your application.

Introduction

Let’s get started.

Why Embed Dashboards in Blazor Applications?

Unified user experience: Keeping dashboards within the application provides a seamless interface where users can access data without leaving their workflow.
Faster decision-making: Real-time access to analytics enables users to take immediate action based on up-to-date insights.
Centralized security control: Authentication and access can be managed within the application, ensuring consistent and secure data governance.
Higher user engagement: Embedding analytics into daily workflows encourages users to interact more frequently with dashboards and insights.
Scalability and flexibility: Embedded analytics makes it easier to extend insights across users, teams, and applications without rebuilding systems.

Next, let's walk through the process of integrating a Bold BI dashboard into a Blazor Server application.

How to Embed a Dashboard in a Blazor Application

The following steps demonstrate how to configure embedding, set up the required components, and render dashboards in a Blazor Server application using JavaScript embedding.

Prerequisites

Before you begin, ensure the following requirements are met:

Step 1: Set Up a Blazor Server App

Start by creating a new Blazor Server project.

Create a folder in your desired location and open it in Visual Studio Code. Open the terminal and run:

dotnet new blazorserver

This will serve as the application where your dashboard will be embedded.

Step 2: Enable Embedding in Bold BI

Next, prepare your dashboard for embedding:

Select the dashboard you want to embed

Go to Settings → Embed

Enable Embed Authentication
Click Embed Configuration.
Download embedConfig.json file.

This file contains all the essential details needed to connect your app to Bold BI.

Step 3: Add the Embed Configuration File

Copy the downloaded embedConfig.json file and place it in the root directory of your Blazor Server application, as shown below.

Your application will read this file to retrieve values such as:

Server URL
Dashboard ID
User email
Embed secret

Step 4: Create Model Classes

The application uses model classes to store the embedding configuration and dashboard details retrieved from the Bold BI server. Create a new folder named Models and add the following classes:

EmbedClass.cs: Handles the request payload from the client.
EmbedDetails.cs: Stores values loaded from embedConfig.json.
GlobalAppSettings.cs: Maintains and reuses the EmbedDetails settings throughout the application.

Before creating the classes, install the required packages:

dotnet add package Newtonsoft.Json 
dotnet add package System.Runtime.Serialization.Primitives

Make sure to include the required namespaces such as Newtonsoft.Json and System.Runtime.Serialization.

[DataContract]
public class EmbedClass
{
[DataMember]
public string embedQueryString { get; set; }
[DataMember]
public string DashboardServerApiUrl { get; set; }
}

public class EmbedDetails
{
public string Environment { get; set; }

public string SiteIdentifier { get; set; }

public string ServerUrl { get; set; }

public string EmbedSecret { get; set; }

public string UserEmail { get; set; }

public string EmbedType { get; set; }

public string DashboardId { get; set; }
}

Step 5: Add the Bold BI JavaScript SDK

To render dashboards, add the Bold BI JavaScript SDK and supporting scripts to the _Host.cshtml page.

Configure the following values:

serverUrl
dashboardId
siteIdentifier
environment
embedType
authorizationServerUrl

Then call the renderDashboard() method to initialize and load the dashboard.

<head>
<title>BoldBI Blazor Embed</title>
<script type="text/javascript" src="https://cdn.boldbi.com/embedded-sdk/latest/boldbi-embed.js"></script>
<script type="text/javascript" src="~/js/Index.js"></script>
<link rel="stylesheet" href="~/css/site.css" />
<script type="text/javascript">
var rootUrl = "@ViewBag.ServerUrl";
var dashboardId = "@ViewBag.DashboardId";
var siteIdentifier = "@ViewBag.SiteIdentifier";
var environment = "@ViewBag.Environment";
var embedType = "@ViewBag.EmbedType";
var authorizationServerUrl = "@Url.Action("AuthorizationServer", "EmbedData")";
</script>
</head>

<body onload="renderDashboard(dashboardId)">
<div id="viewer-section" style="width: 100%;">
<div id="dashboard"></div>
</div>
</body>

Step 6: Create the Authorization API

Create a Controllers folder and add a controller named EmbedDataController.cs.

This controller performs three important tasks:

Reads the embed query string.
Generates a secure signature using the Embed Secret.
Requests an access token from the Bold BI server and returns it to the Blazor application.

Keeping token generation on the server ensures that the Embed Secret is never exposed to the browser.

[HttpPost("[action]")]
[Route("AuthorizationServer")]
public string AuthorizationServer([FromBody] object embedQuerString)
{
var embedClass = JsonConvert.DeserializeObject<EmbedClass>(embedQuerString.ToString());
var embedQuery = embedClass.embedQuerString;
// Use your user email as embed_user_email
embedQuery += "&embed_user_email=" + GlobalAppSettings.EmbedDetails.UserEmail;
//To set embed_server_timestamp to overcome the EmbedCodeValidation failing while the client application is in different time zone.
double timeStamp = (int)DateTime.UtcNow.Subtract(new DateTime(1970, 1, 1)).TotalSeconds;
embedQuery += "&embed_server_timestamp=" + timeStamp;
var embedDetailsUrl = "/embed/authorize?" + embedQuery + "&embed_signature=" + GetSignatureUrl(embedQuery);

using (var client = new HttpClient())
{
client.BaseAddress = new Uri(embedClass.dashboardServerApiUrl);
client.DefaultRequestHeaders.Accept.Clear();

var result = client.GetAsync(embedClass.dashboardServerApiUrl + embedDetailsUrl).Result;
string resultContent = result.Content.ReadAsStringAsync().Result;
return resultContent;
}
}

public string GetSignatureUrl(string message)
{
var encoding = new System.Text.UTF8Encoding();
var keyBytes = encoding.GetBytes(GlobalAppSettings.EmbedDetails.EmbedSecret);
var messageBytes = encoding.GetBytes(message);
using (var hmac = new System.Security.Cryptography.HMACSHA256(keyBytes))
{
var hashMessage = hmacsha1.ComputeHash(messageBytes);
return Convert.ToBase64String(hashMessage);
}
}

Step 7: Configure Program.cs

Before calling app.Run(), add the controller route configuration:

Register the EmbedDataController
Configure the default routing pattern
Load and deserialize the embedConfig.json file

The configuration values are stored in GlobalAppSettings and used throughout the embedding process.

app.MapControllerRoute(
name: "default",
pattern: "{controller=EmbedData}/{action=EmbedConfigErrorLog}/{id?}");

string basePath = AppDomain.CurrentDomain.BaseDirectory;
string jsonString = System.IO.File.ReadAllText(Path.Combine(basePath, "embedConfig.json"));
GlobalAppSettings.EmbedDetails = JsonConvert.DeserializeObject<EmbedDetails>(jsonString);

Step 8: Run the Application

Once the configuration is complete, run the application using:

dotnet watch run

Step 9: Verify the Dashboard

After running the application, the dashboard is rendered securely inside the Blazor Server application.

To learn more about implementation details and explore sample projects, refer to the official Bold BI guide for ''How to Embed a Bold BI Dashboard in a Blazor Server Application''.

How Dashboard Embedding Works in a Blazor Server Application

The Blazor Server sample follows a sequence of configuration loading, secure token generation, token handling, and dashboard rendering.

How It Works

Blazor application requests a secure embed token.
Authorization API generates the request and signature.
Bold BI server validates the embed secret and request.
Bold BI server returns the embed token.
Authorization API returns the token to the Blazor application.
Blazor application uses the token with the Bold BI JavaScript SDK to render the dashboard.

To learn more, refer to the official Bold BI documentation on how the Blazor Server sample works.

Why Choose Bold BI for Dashboard Embedding?

Embed dashboards directly within application workflows
Maintain secure access through token-based authentication
Deliver interactive analytics experiences to users
Support role-based access control and governance
Accelerate analytics delivery without building analytics infrastructure from scratch
Scale analytics across users, teams, and applications
Support multi-tenant analytics for SaaS applications

Whether you are building SaaS platforms, internal applications, or customer-facing portals, Bold BI provides a reliable way to integrate analytics into your application.

Frequently Asked Questions

Can I embed multiple dashboards in a Blazor Server application?
Yes. You can embed multiple Bold BI dashboards in the same Blazor Server application by configuring separate dashboard instances.
How can I secure embedded dashboards for different users?
Use authentication and role-based access control to ensure users only access the dashboards and data they are authorized to view.
Is authentication required for embedding?
Yes. Authentication is recommended to protect embedded dashboards from unauthorized access.
Can I customize dashboard appearance?
Yes. You can customize themes, colors, layouts, and other UI elements to match your application’s branding.
Does embedding dashboards affect application performance?
Performance depends on dashboard complexity, data volume, and network conditions. With proper configuration, embedded dashboards can run efficiently in Blazor applications.
What are the prerequisites for embedding a Bold BI dashboard in Blazor?
You need a Bold BI environment, a published dashboard, embedding enabled, required configuration values, and a Blazor application set up for integration.
How do I embed analytics in a Blazor Server application?
Enable dashboard embedding, configure authentication, add the required settings, and render the dashboard using JavaScript interop.

What's New in v16.1.70 for Self-Service & Embedded Analytics

Bold BI by Syncfusion — Thu, 18 Jun 2026 06:40:18 +0000

TL;DR: Bold BI® v16.1.70 introduces a modernized dashboard design experience with next-generation widgets, dashboard-level expressions, and expanded data connectivity, along with enhanced AI customization, stronger security controls, and platform-wide modernization with .NET 10.

Introduction

We're excited to announce the release of Bold BI v16.1.70, a major update focused on helping teams build modern analytics experiences faster while improving scalability, performance, governance, and AI-driven insights.

This release introduces a completely refreshed dashboard design experience with modern widgets and expanded data connectivity, enhances AI-powered analytics with customizable prompts and AI credit management, and strengthens enterprise readiness through platform modernization and security improvements.

Whether you're building self-service analytics for internal users or embedding analytics into customer-facing applications, Bold BI v16.1.70 improves the analytics experience for your users.

Let's explore what's new in Bold BI v16.1.70.

AI enhancements

We've expanded AI capabilities to provide greater control, personalization, and accessibility for users across different regions and use cases.

Customize AI dashboard summaries for your business

Every organization has its own terminology, KPIs, and reporting style. With custom dashboard summarization prompts, administrators can now tailor how AI generates dashboard insights.

By defining custom prompts, teams can ensure that summaries align with business context, highlight the most relevant metrics, and deliver insights that are more meaningful to end users. This helps create a consistent analytics experience while improving the quality and relevance of AI-generated recommendations.

Scale AI adoption with AI credit management

As AI usage grows across teams and departments, enterprises need better visibility and control over consumption. Bold BI v16.1.70 introduces AI credit management that allows teams to purchase, monitor, and manage AI credits directly within the platform.

This enhancement provides greater governance over AI-powered features, helps teams track usage more effectively, and enables enterprises to scale AI initiatives confidently while maintaining operational control.

Improve natural language understanding with centralized synonym management

Business users often refer to the same metric or process using different terms. For example, one team may use "Revenue" while another refers to it as "Sales" or "Income."

With centralized synonym management, administrators can define and manage alternate business terms in a single location. This helps the AI Assistant better understand user intent, improves natural language query accuracy, and ensures users receive more relevant and consistent responses regardless of the terminology they use.

Make complex data retrieval accessible through conversation

Accessing data from stored procedures traditionally requires technical knowledge and database expertise. Bold BI v16.1.70 simplifies this process by enabling conversational access to stored procedures.

Users can now interact with stored procedures using natural language, making advanced data retrieval more accessible to business users. This reduces dependency on technical teams and accelerates access to information.

Right-to-left language support

Bold BI AI experiences now include right-to-left rendering support for layout direction, text rendering, and component alignment. This improves accessibility and delivers a more natural AI experience for users working with RTL languages.

Data Sources and Data Hub highlights

We've expanded connectivity and data preparation capabilities to support modern analytics workloads.

Reuse business logic with dashboard-level expressions

Organizations often use the same calculations for multiple visualizations, making dashboard maintenance time-consuming and prone to inconsistencies. With dashboard-level expressions, users can now create reusable calculations once and leverage them in multiple widgets within a dashboard. This approach helps ensure consistent business metrics, reduces repetitive configuration work, and makes dashboard updates easier to manage as reporting requirements evolve.

Improve large-scale data extraction with enhanced Presto support

Bold BI v16.1.70 enhances the Presto data source with advanced extract mode capabilities, additional configuration parameters, and support for multi-value dashboard parameters within stored procedures.

These improvements provide greater control over data retrieval processes and make it easier to support complex reporting scenarios and enterprise-scale analytics workloads.

Accelerate dashboard development with advanced data sampling

Building dashboards against large datasets can slow down the design process. To address this challenge, Bold BI now supports advanced data sampling in design mode.

By working with representative sample data during dashboard creation, teams can model, validate, and refine visualizations more efficiently while minimizing the overhead associated with processing large volumes of data during development.

Optimize query performance with smart guardrails

Poorly optimized queries can significantly impact dashboard performance and database resources. Bold BI v16.1.70 introduces smart query guardrails that proactively identify potentially expensive queries involving large numbers of rows, multiple joins, and missing filter conditions.

These intelligent alerts help users detect performance risks early, encourage better query design practices, and support faster, more efficient analytics experiences.

Expand connectivity across modern architectures

As organizations adopt cloud, hybrid, and distributed data environments, connectivity flexibility becomes increasingly important. The Bold Data Hub has expanded its ecosystem support with connectivity for Presto and Trino.

These additions allow businesses to connect, manage, and process data more effectively across diverse infrastructure environments while supporting modern data engineering workflows.

Strengthen data reliability with enhanced destination support

Bold BI v16.1.70 also includes important improvements to Bold Data Hub destinations and synchronization workflows.

The Amazon Redshift destination now supports primary key configuration, helping organizations maintain better data consistency and more reliable synchronization processes.

The StarRocks destination has been updated to use the MySQL protocol, improving compatibility with existing systems, simplifying configuration, and providing a more seamless integration experience for teams using StarRocks within their analytics ecosystem.

Designer and Server enhancements

This release delivers significant improvements to both dashboard design and platform management. With a refreshed dashboard-authoring experience, enhanced usability features, and new server-side capabilities, organizations can create analytics faster while simplifying administration and operations.

A new modern panel for next-generation dashboard design

Bold BI now features a dedicated modern panel within the dashboard designer, providing a centralized space for next-generation widgets while preserving access to classic widgets.

This modernized approach makes it easier for users to discover new visualization capabilities, adopt modern dashboard components at their own pace, and transition smoothly without disrupting existing dashboards. By clearly separating classic and modern experiences, organizations can modernize their analytics environment while maintaining backward compatibility.

Better visibility into dashboard loading and rendering

With widget-rendering progress indicators, dashboard authors and viewers can now track widget loading statuses in real time. These visual indicators provide greater transparency into rendering progress, helping users identify loading stages and improving the overall dashboard experience.

Find dashboard settings instantly with property search

As dashboards become more sophisticated, locating specific configuration options can become time-consuming. To simplify dashboard customization, Bold BI v16.1.70 introduces property search within the dashboard designer.

Users can quickly search for dashboard and widget settings instead of manually navigating through multiple configuration panels. This enhancement reduces setup time, improves productivity, and makes the design experience more efficient for both new and experienced dashboard authors.

Build richer dashboards with next-generation widgets

The widget collection continues to expand with new next-generation widgets, including:

Combo Chart
Tree Map
Proportional Chart
Combo Box

These modern visualizations help organizations present data more effectively, support richer storytelling, and improve user interactions. Whether visualizing hierarchical data, comparing multiple metrics, or enhancing filtering experiences, these widgets provide greater flexibility for creating engaging dashboards.

Simplify large-scale data source management

Managing data refresh operations across numerous data sources can be challenging in enterprise analytics environments. To help administrators automate these processes more efficiently, Bold BI v16.1.70 introduces bulk data source refresh APIs.

Instead of refreshing data sources individually, organizations can now trigger refresh operations at scale through APIs. This reduces administrative effort, improves operational efficiency, and helps ensure analytics environments remain up-to-date with minimal manual intervention.

IDP highlights

Bold BI v16.1.70 includes several IDP enhancements that make authentication, configuration, and upgrades easier to manage across enterprise deployments.

Streamline authentication management with new APIs

Managing authentication settings across multiple environments can often involve manual configuration and maintenance. With new default authentication configuration APIs, administrators can now retrieve and update authentication settings programmatically.

This enhancement simplifies identity management, reduces administrative overhead, and helps organizations maintain consistent authentication policies across development, testing, and production environments.

Gain more flexibility with JWT embedding

Embedded analytics deployments often require different authentication approaches depending on application architecture and security requirements. Bold BI 16.1 enhances embedding flexibility by supporting both GET and POST JWT embedding requests through a unified implementation.

This gives developers greater control over how analytics are integrated into applications while ensuring a more consistent and secure authentication experience in embedded environments.

Simplify configuration management across environments

Managing configuration settings through files can become difficult as environments scale and deployment complexity increases. To address this challenge, Bold BI v16.1.70 provides the ability to manage configuration settings directly from the database.

By reducing reliance on configuration files, organizations can simplify maintenance, improve deployment consistency, and make environment management more reliable across enterprise implementations.

Modernized architecture with .NET 10 upgrade

The Bold BI application has been upgraded from .NET 8 to .NET 10, bringing improved performance and future-ready capabilities. Teams benefit from better performance, enhanced security, and long-term platform stability, ensuring a reliable reporting foundation.

Other bug fixes and minor improvements

Along with the major features and enhancements, Bold BI v16.1.70 includes numerous bug fixes, stability improvements, and performance optimizations across server, designer, data source, Bold Data Hub, AI, IDP, and embedding modules.

Reliability: Improved platform stability through fixes related to authentication, data processing, dashboard rendering, filtering, exports, data extraction, and upgrade workflows.
Performance: Enhanced dashboard loading, query execution, data retrieval, widget rendering, connection management, and pipeline processing to deliver a smoother analytics experience.
Compatibility: Addressed issues with multiple databases, connectors, cloud environments, and embedded analytics scenarios to ensure more consistent behavior across deployments.
Security: Strengthened application protection through fixes and hardening related to API rate limiting, CSRF protection, input sanitization, authorization token handling, dependency vulnerabilities, and security scan findings

Explore Bold BI v16.1.70 with advanced AI, faster performance, flexible embedding, and scalable workflows to power smarter analytics experiences.

Real-time use cases enabled by Bold BI v16.1

The Bold BI v16.1.70 release was designed to solve real analytics challenges across dashboard creation, AI adoption, data management, and embedded analytics. Here’s how the new features translate into practical business value.

Create modern, interactive dashboards faster

Challenge: Dashboard authors need modern widgets and easier configuration options to build visually rich dashboards without spending too much time searching through settings.
Bold BI solution: The new modern panel, modern widgets, widget rendering indicators, and property search improve the dashboard design workflow.
Impact: Designers can build polished dashboards more quickly, improve productivity, and deliver a better viewing experience to business users.

Scale data operations across cloud and hybrid environments

Challenge: Organizations need flexible storage and connectivity options to support growing data pipelines across on-premises, cloud, and hybrid environments.
Bold BI solution: Bold BI v16.1.70 adds Presto, Trino for on-premises deployments, Redshift primary key support, and improved StarRocks compatibility.
Impact: Data teams can connect, store, and process data more flexibly while improving reliability across analytics architectures.

Deliver more customized AI analytics

Challenge: Generic AI responses may not always match an organization’s terminology, reporting style, or user expectations.
Bold BI solution: Custom prompts, centralized synonym management, RTL support, AI credits, and conversational stored procedure access make AI experiences more flexible and business-aware.
Impact: Users can get more relevant AI insights, improve adoption across global teams, and access advanced data through natural language.

Strengthen enterprise analytics deployments

Challenge: Large-scale analytics environments require strong security, reliable monitoring, and simplified administration.
Bold BI solution: v16.1.70 includes .NET 10 upgrade, authentication APIs, bulk data source refresh APIs, activity log maintenance, and security hardening.
Impact: Organizations can manage deployments more efficiently, improve reliability, and strengthen security for enterprise analytics workloads.

Conclusion

Bold BI® v16.1.70 marks a step forward in delivering faster, smarter, and more scalable analytics experiences. This release introduces a modern dashboard design experience with next-generation widgets, reusable dashboard-level expressions, enhanced AI capabilities, and significant platform modernization powered by .NET 10.

From customizable, AI-powered insights and improved data management to stronger security controls and expanded embedding capabilities, v16.1.70 helps organizations build analytics solutions that are more intuitive, efficient, and enterprise-ready.

Ready to explore what's new? Download Bold BI v16.1.70 today or start a free trial to experience the latest enhancements in self-service and embedded analytics. For a complete list of features, improvements, and fixes included in this release, visit our release history page. If you have any questions or need help getting started, our support team is always here to help.

5 Customer Experience Dashboard Examples to Reduce Churn

Bold BI by Syncfusion — Mon, 15 Jun 2026 14:23:53 +0000

TL;DR: Customer churn often starts with early warning signs such as declining satisfaction, unresolved support issues, and reduced engagement. Customer experience dashboards help teams monitor these signals in real time, identify risks sooner, and improve retention. In this blog, you'll explore five customer experience dashboard examples and the key metrics that help reduce churn.

Introduction

Customer churn rarely happens without warning. Customers often show signs such as declining satisfaction, negative feedback, increased support requests, or reduced product usage before they leave. The challenge is that these signals are usually scattered across multiple systems, making them difficult to identify and act quickly. Customer experience dashboards bring these insights together into a single view, helping teams detect risks earlier and improve retention. In this blog, you'll explore five customer experience dashboards and the key metrics that help organizations deliver better customer experiences and reduce churn

What Is a Customer Experience Dashboard?

A customer experience dashboard is a centralized analytics dashboard that tracks customer satisfaction, engagement, support performance, and feedback metrics in one place. It helps organizations monitor customer experience, identify potential issues early, and make data-driven decisions to improve retention and loyalty.

Common Mistakes Teams Make When Tracking Customer Experience

Many organizations invest in customer experience dashboards yet still struggle to improve retention. The issue is not just access to data, but how that data is tracked, structured, and used.

Common challenges include:

Tracking too many metrics: Teams collect a large number of data points without focusing on the ones that directly impact retention.
Measuring activity instead of outcomes: Metrics such as ticket volume or feature usage are tracked without linking them to customer satisfaction or churn risk.
Ignoring customer sentiment: Support performance is often monitored independently, without considering how customers feel about the experience.
Disconnected KPIs: Operational metrics are analyzed in isolation instead of being tied to retention or business outcomes.
Fragmented data across systems: Customer data is spread across CRM platforms, support tools, surveys, and analytics systems, making it difficult to create a unified view.
Inconsistent metric definitions: Different teams define and measure metrics in different ways, which leads to unreliable insights.
Siloed reporting structures: Data is often owned by individual teams, limiting visibility across the organization.
Lack of real-time visibility: Without up-to-date insights, teams are unable to act on emerging risks quickly.
Difficulty identifying early warning signals: Declining satisfaction, reduced engagement, or support delays are often missed because they are not analyzed together.

How Customer Experience Dashboards Help Reduce Churn

Customer experience dashboards play a critical role in turning scattered data into actionable insights. By connecting satisfaction, support performance, feedback, and engagement metrics, teams can monitor the customer experience more effectively and act earlier.

Instead of reacting after churn happens, organizations can identify risk signals such as declining satisfaction, increased support delays, or reduced engagement.

Customer experience dashboards help reduce churn by:

Identifying retention risks early: Monitor warning signs such as declining CSAT scores, lower engagement levels, or increasing support tickets before they impact customer loyalty.
Improving customer satisfaction: Track customer feedback and satisfaction trends to understand where experiences can be improved.
Enhancing support performance: Measure response times, resolution rates, and ticket backlogs to ensure customer issues are resolved quickly and effectively.
Monitoring customer sentiment: Analyze customer feedback and sentiment trends to detect dissatisfaction before it leads to churn.
Increasing customer engagement: Track product adoption and usage patterns to identify customers who may need additional support or guidance.
Supporting proactive decision-making: Provide teams with actionable insights that help prevent problems rather than react to them after customers leave.
Reducing customer churn: Combine customer experience KPIs, support metrics, and engagement data to identify at-risk customers and take proactive retention measures before churn occurs.

To see how these retention insights come together, the following dashboard examples highlight how teams track and act on customer experience data.

5 Customer Experience Dashboard Examples to Reduce Churn

Customer experience dashboards help organizations monitor key signals that influence retention, such as satisfaction, support quality, engagement, and lifecycle progression. Different dashboards provide different levels of visibility, allowing teams to identify risks earlier and act more effectively.

1. Customer satisfaction dashboard

Customer satisfaction is often the earliest indicator of retention risk. This dashboard helps teams track how customer perception changes over time and understand what factors influence those changes. Bold BI enables teams to combine satisfaction, behavior, and support data into a single dashboard. Interactive KPI visualizations and drill-down capabilities allow teams to analyze trends by segment, feature, or period and act faster.

Key customer experience KPIs to track:

Customer satisfaction score (CSAT): Tracks satisfaction levels across interactions to measure overall customer experience.
Net promoter score (NPS): Evaluates customer loyalty and likelihood to recommend the product or service.
Customer effort score (CES): Measures how easy it is for customers to complete key actions or resolve issues.
Satisfaction by product or segment: Breaks down satisfaction across features, customer groups, or products.
Retention and churn trends: Links to satisfaction changes to long-term engagement outcomes.

Why it matters for retention

By analyzing these metrics together, teams can identify early declines in satisfaction and understand what is driving them before they impact retention. A drop in CSAT, NPS, or CES often serves as an early warning sign of churn, allowing teams to take corrective action before customers disengage.

2. Customer ticket traffic management dashboard

Support interactions play a critical role in shaping customer experience. This dashboard helps teams monitor ticket volume, workload distribution, and response efficiency. Bold BI allows teams to filter and analyze support data across channels and timeframes, improving visibility into ticket performance and workflow efficiency.

Key customer experience KPIs to track:

Total tickets created and resolved: Tracks workload trends and completion rates.
Ticket backlog: Identifies unresolved issues that may impact customer experience.
Ticket status breakdown: Shows distribution across open, pending, and resolved categories.
First response time and resolution time: Measures responsiveness and efficiency.
Ticket sources: Identifies high-demand communication channels.
Ticket trends over time: Detects recurring problems or spikes in support demand.

Why it matters for retention

These insights help teams identify bottlenecks and ensure that customer issues are resolved quickly and consistently. Reducing support delays and unresolved issues helps prevent customer frustration, one of the most common contributors to churn.

3. Customer sentiment analysis dashboard

Customer feedback provides important context behind operational metrics. This dashboard focuses on understanding how customers feel and why satisfaction changes. Bold BI combines feedback data from multiple sources into a single view with visualizations that help teams identify sentiment trends and act quickly.

Key customer experience KPIs to track:

Sentiment distribution: Tracks positive, neutral, and negative feedback across interactions.
Feedback trends over time: Monitors changes in customer perception.
Customer reviews and ratings: Aggregates feedback from surveys and external platforms.
Feedback categories or topics: Identifies recurring issues and themes.
Feedback volume: Measures engagement across feedback channels.

Why it matters for retention

By analyzing sentiment alongside other metrics, teams can identify root causes of dissatisfaction and improve experiences more effectively. Negative sentiment trends can highlight churn risks early, helping teams address customer concerns before they lead to customer loss.

4. Nicereply customer satisfaction dashboard

While overall satisfaction metrics provide a broad view, interaction-level feedback gives deeper insight into service quality. This dashboard focuses on satisfaction after individual support interactions. Bold BI enables role-based sharing and automated updates, helping teams track satisfaction trends and compare performance across agents or teams.

Key customer experience KPIs to track:

CSAT at interaction level: Measures satisfaction after each support engagement.
NPS distribution: Evaluates loyalty across different customer groups.
Customer effort score (CES): Assesses ease of resolving issues.
Survey response trends: Tracks engagement with feedback collection.
Agent performance comparison: Identifies variations in service quality across teams.

Why it matters for retention

This level of detail helps teams improve individual interactions and maintain consistent service standards. Consistently positive support experiences strengthen customer loyalty and reduce the likelihood of churn.

5. Customer service performance dashboard

Service performance directly influences customer experience and retention. This dashboard helps teams monitor service delivery and identify performance gaps. Bold BI provides real-time dashboards and drill-through analysis, enabling teams to monitor performance trends and quickly identify areas for improvement.

Key customer experience KPIs to track:

Resolution time by issue type: Highlights complex areas that require attention.
Response time trends: Tracks how quickly support teams respond.
Resolved vs unresolved tickets: Measures consistency in issue resolution.
Ticket distribution by category: Provides insight into workload patterns.
Service performance trends: Tracks improvements or declines over time.
Customer satisfaction linked to service: Connects operational performance with outcomes.

Why it matters for retention

By linking service performance with customer experience metrics, teams can ensure consistent service quality and reduce retention risk. Monitoring these KPIs helps teams proactively address service gaps that may otherwise contribute to customer churn.

Now that you’ve seen how these dashboards work in practice, let’s look at the best practices for an effective customer experience dashboard.

Best Practices for Effective Customer Experience Dashboards

To get the most value from your customer dashboard, it is important to:

Focus on actionable metrics: Prioritize metrics that directly impact customer satisfaction, service quality, and retention.
Keep the design simple: Use clear visuals and limit clutter so insights are easy to understand at a glance.
Use real-time data: Ensure dashboards reflect current data to support faster responses and timely decisions.
Segment your data: Analyze metrics by customer groups, channels, or regions to uncover specific trends.
Enable drill-down analysis: Allow users to explore deeper insights and identify root causes behind key metrics.

These practices help teams move beyond reporting and toward continuous improvement. For a deeper look at how to design effective dashboards, explore our blog 10 Best Practices for Designing Powerful Dashboards. With the right design approach, the next step is choosing a platform that can support these capabilities at scale.

Monitor Customer Experience Metrics with Bold BI

Keeping customers loyal depends on understanding their needs, resolving issues quickly, and delivering consistent experiences across every touchpoint. Without clear visibility into feedback, support activity, and satisfaction trends, it becomes difficult to identify what drives churn or repeat engagement.

Bold BI helps businesses improve retention by bringing customer data from multiple sources into one unified analytics platform. With its interactive dashboard designer, teams can build customer retention dashboards that track satisfaction levels, sentiment, support performance, service quality, and customer behavior trends in real time.

Additionally, Bold BI’s AI-powered analytics help users ask data questions, get instant answers, automatically create visuals, and turn complex customer data into clear insights. Teams can also use simple prompts to generate ready-to-use dashboards faster.

Whether you're monitoring customer feedback, measuring service quality, or identifying early signs of churn, Bold BI gives teams the insights they need to improve customer experiences, strengthen long-term loyalty, and take proactive steps to reduce churn. Check out our blog on “10 Best Practices for Designing Powerful Dashboards” or watch our YouTube video to learn how to create your first dashboard with Bold BI.

Ready to start building customer retention dashboards in Bold BI? Start a free 30-day trial or book a personalized demo to create data-driven experiences that keep customers coming back.

Frequently Asked Questions

What is a customer experience dashboard? A customer experience dashboard is a visual tool that consolidates customer data, feedback, and performance metrics to help teams monitor and improve user experience.
What metrics should a customer experience dashboard include? It should include customer experience metrics such as CSAT, NPS, response time, churn rate, and engagement levels.
How do customer experience dashboards improve retention? They help identify early warning signs of churn, enabling proactive action before customers leave.
What is the difference between a customer experience dashboard and a customer service dashboard? A customer experience dashboard covers the full journey, while a service dashboard focuses on support interactions.
Who uses customer experience dashboards? Customer success teams, support teams, product teams, and leadership use them to monitor performance.
How often should customer experience metrics be reviewed? Metrics should be reviewed regularly or in real time to ensure timely decision-making.

Top 4 Supply Chain Dashboard Examples for Real-Time Insights

Bold BI by Syncfusion — Thu, 11 Jun 2026 13:54:44 +0000

TL;DR: Supply chain teams often struggle with delayed shipments, inventory imbalances, rising logistics costs, and limited operational visibility. Supply chain dashboards help organizations monitor key KPIs in real time, identify bottlenecks faster, and improve operational efficiency. This guide explores four supply chain dashboard examples and the metrics that help teams make better decisions.

Introduction

Supply chain teams often struggle with delayed shipments, inventory imbalances, rising transportation costs, and limited visibility across operations. When data is spread across ERP systems, warehouses, and logistics platforms, identifying issues before they impact customers becomes difficult.

Supply chain dashboards help organizations monitor operations in real time, track critical KPIs, and respond faster to disruptions. In this guide, we'll explore four supply chain dashboard examples, and the key metrics teams use to improve performance.

This guide covers:

Why supply chain dashboards are essential in modern operations.
How to Design an Effective Supply Chain Dashboard.
Four key dashboard examples every organization should implement.
Key metrics and features to include.

What Is a Supply Chain Dashboard?

A supply chain dashboard gives operation teams a centralized view of inventory, shipments, supplier performance, and logistics costs. Instead of switching between ERP systems, spreadsheets, and logistics tools, teams can monitor key metrics in one place and respond faster to disruptions.

Why Supply Chain Dashboards Matter

Supply chain dashboards transform operational data into actionable insights. They help organizations:

Monitor inventory, logistics, and fulfillment performance in real time.
Detect delays and bottlenecks before they affect customers.
Track KPIs across multiple locations and suppliers.
Improve operational efficiency and reduce costs.
Support faster and more informed decision-making.

Ultimately, they empower organizations to stay agile, efficient, and competitive in a rapidly changing supply chain environment.

To fully leverage supply chain dashboards, organizations must focus on designing them for clarity, usability, and actionable insights.

How to Design an Effective Supply Chain Dashboard

To maximize the value of supply chain dashboards:

Define clear supply chain objectives: Align dashboards with broader goals such as improving delivery performance, reducing costs, enhancing supplier reliability, and optimizing inventory across the network.
Prioritize actionable KPIs: Focus on metrics teams can influence regularly, such as on-time delivery (OTIF), days inventory outstanding (DIO), stockout rate, return rate, order rejection rate, and cost per shipment.
Integrate end-to-end data sources: Combine data from ERP systems, warehouse management, transportation/logistics platforms, and supplier systems to provide a unified, real-time view across the supply chain.
Use simple and intuitive visuals: Leverage clear charts like trend lines, geographic maps, and performance comparisons to quickly highlight bottlenecks, delays, and cost drivers.
Enable real-time monitoring: Ensure dashboards update frequently to track shipments, orders, inventory levels, and supplier performance so teams can respond quickly to disruptions.
Design for role-based usability: Structure dashboards with high-level KPIs at the top for executives and detailed operational insights below for analysts and managers, ensuring relevance for all users.
Set alerts for critical risks: Configure thresholds for key metrics like delayed shipments, low service levels, high returns, or rising costs to proactively identify and address issues.
Continuously refine and optimize: Regularly review dashboard usage and performance to ensure metrics stay aligned with evolving business needs and supply chain strategies.

To better understand how these concepts come together in practice, let’s explore real-world dashboard examples that address key supply chain challenges.

4 Best Supply Chain Dashboard Examples in 2026

Supply chain dashboards help organizations monitor operations, identify inefficiencies, and improve decision-making through visual insights. Different supply chain teams require different levels of visibility. The following dashboards address four key supply chain areas: customer shipping performance, overall operational efficiency, inventory management, and distributor delivery performance.

1. Customer Shipping Performance Insights Dashboard Example

Customer shipping plays a key role in maintaining delivery reliability and customer satisfaction. This dashboard helps operations teams identify delayed shipments, compare carrier performance, and monitor shipping costs. By tracking on-time delivery rate, shipping costs, and regional demand, teams can quickly identify fulfillment bottlenecks and improve customer satisfaction.

Key supply chain KPIs to track:

Customer segment distribution: Order breakdown by distributor, wholesale, and retail segments.
Average order value by courier and region: Comparison of revenue across shipping partners and locations.
Shipment distribution by region: Visibility into geographic demand across regions like Asia and Europe.
On-time deliveries by courier and country: Delivery performance across carriers and countries.
Orders and shipping costs trend: Monthly comparison of order volume and shipping costs to track efficiency.
Card widgets: Showing results for total orders (271) and average order value ($1.09K) for a quick performance snapshot.

2. Supply Chain Performance Dashboard Example

Effective supply chain management requires a clear view of operational and financial performance. This dashboard helps organizations track key metrics such as costs, delivery efficiency, order performance, inventory movement, and overall supply chain health. It gives managers the visibility needed to identify inefficiencies and understand where performance improvements are needed.

Key supply chain KPIs to track:

Operational costs by fulfillment process: Cost breakdown across receiving, storage, picking, quality control, and shipping.
Top returned products: Products with the highest return volumes to identify quality or demand issues.
Cash-to-cash cycle time: Time taken to convert inventory investment into cash flow, including monthly trends and forecasts.
Card widgets: Showing results for total net sales ($299.6M), days inventory outstanding (46 days), perfect order rate (96.9%), total costs ($61.0M), on-time delivery rate (65.4%), and order rejection rate (17.7%) for a quick performance overview.

3. Supply Chain Inventory Management Dashboard Example

Inventory management is essential for balancing product availability, storage costs, and customer demand. This dashboard helps teams monitor inventory levels, stock movement, warehouse performance, and product availability across locations. It provides a clear view of inventory health so managers can detect stock imbalances before they affect operations.

Key supply chain KPIs to track:

Product sales details by warehouse and category: Sales breakdown across locations and product categories to identify top-performing warehouses and products.
Inventory carrying costs by warehouse: Cost distribution (loss, administrative, storage, damage, handling) across warehouses to highlight cost-intensive areas.
Days to sell inventory: Inventory turnover trends with forecast insights to improve demand planning and stock optimization.
Card widgets: Showing results for on-time shipments (96%), inventory-to-sales ratio (17.6), inventory efficiency (19.2%), return rate (2%), stockout rate (2%), inventory carry cost (16), turn ratio (7), returned vs ordered quantity (25.4K vs 132.6K), and stock availability (67 out of stock vs 2,829 available) for a quick view of inventory health and performance.

4. Distributor Delivery Performance Dashboard Example

Distributor performance directly affects delivery timelines, service quality, and customer experience. This dashboard helps organizations evaluate distributor efficiency by tracking successful deliveries, on-time performance, delivery costs, and customer feedback. It allows managers to compare distributors and identify where service improvements are needed.

Key supply chain KPIs to track:

Top distributors by successful deliveries: Identifies leading distributors (e.g., Blaze Logistics, Express Logistics) based on completed deliveries.
Top distributors by on-time deliveries: Highlights distributors with the highest on-time performance to evaluate reliability.
Customer satisfaction – delivery feedback count: Tracks feedback categories (Very Good, Good, Average, etc.) to measure service quality.
Distributor delivery details: Provides shipment-level insights including pickup and delivery locations, dates, and costs.
Card widgets: Showing key metrics such as average delivery time (68.5 hrs), total deliveries (20.4K), on-time deliveries (17.8K), on-time delivery rate (87.3%), and successful delivery rate (90.1%) for a quick performance snapshot.

These supply chain dashboard examples highlight how organizations can track shipping performance, monitor inventory health, optimize operational efficiency, and evaluate distributor performance through data-driven insights. Explore our Supply Chain Dashboard Samples to see these dashboards in action and discover how different supply chain metrics can be analyzed through interactive business intelligence dashboards.

Next, let's explore how you can embed dashboards in your application using Bold BI.

Embed Supply Chain Dashboards in Your Application

With Bold BI, you can easily embed interactive supply chain dashboards directly into your applications. Embedded analytics allows users to monitor key metrics, explore real-time insights, set data alerts, and access dashboard views without switching between platforms.

For example, the Supply Chain Performance Dashboard can be embedded into an ASP.NET Core application to help operations teams track order performance, delivery efficiency, inventory movement, and cost trends from within their existing workflow.

To learn more about embedding dashboards into your ASP.NET MVC applications, refer to this blog. You can also download the sample code used in the previous steps from our documentation.

Create Your Own Supply Chain Dashboards with Bold BI

Bold BI® makes it easy to create supply chain dashboards that bring shipping, inventory, order, and cost data into one interactive view. With drag-and-drop dashboard creation, real-time data connectivity, alerts, drill-downs, and secure embedding, teams can quickly turn complex supply chain data into actionable insights.

Whether you want to monitor customer shipping performance, track overall supply chain efficiency, or manage inventory health, Bold BI helps you build dashboards tailored to your business needs. Explore our blog on 10 Best Practices for Designing Powerful Dashboards or watch our YouTube video to learn how to create and embed interactive dashboards with ease.

Ready to create your own supply chain dashboards? Start with Bold BI’s free trial or request a personalized demo.

Related resources:

Frequently asked questions

1.
What is a supply chain dashboard?
A supply chain dashboard is a visual tool that provides real-time insights into supply chain operations, including inventory, logistics, and supplier performance.
2.
Which KPIs should every supply chain dashboard include?
Most supply chain dashboards track on-time delivery rate, inventory turnover, stockout rate, perfect order rate, days inventory outstanding, and cost per shipment to monitor operational performance.
3.
How do supply chain dashboards improve visibility?
Supply chain dashboards consolidate data from ERP systems, warehouses, logistics platforms, and suppliers into a centralized view, helping teams identify bottlenecks and respond faster to disruptions.
4.
How does AI improve supply chain dashboards?
AI improves supply chain dashboards by forecasting demand, detecting risks early, and automatically turning data into actionable insights for faster decision-making.
5.
Can supply chain dashboards reduce inventory costs?
Yes. By monitoring inventory turnover, stockout rates, and inventory carrying costs, dashboards help organizations optimize stock levels and reduce excess inventory expenses.

Embedded Analytics Tools: What to Evaluate Before Buying

Bold BI by Syncfusion — Thu, 11 Jun 2026 10:54:08 +0000

TL;DR: Embedded analytics tools help SaaS companies deliver dashboards and self-service analytics directly within their applications. Before choosing an embedded analytics tool, evaluate factors such as multitenancy, customization, security, scalability, developer experience, AI capabilities, and pricing to ensure the platform can support both current requirements and future growth.

Introduction

Many SaaS companies start with basic analytics to meet customer requirements. Over time, those requests grow into interactive dashboards, self-service analytics, tenant-specific experiences, exports, and role-based access controls.

Building and maintaining these capabilities can quickly become a challenge. Teams must manage security, performance, tenant isolation, and growing data volumes while continuing to develop core product features. This is why many organizations choose embedded analytics tools instead of building analytics from scratch. In this guide, we'll explore the key factors SaaS teams should evaluate before selecting an embedded analytics solution in 2026.

What is embedded analytics?

Embedded analytics refers to the integration of dashboards and data exploration capabilities directly into a SaaS application.

A modern embedded analytics solution enables users to:

Access insights within the application.
Interact with data in real time.
Build custom dashboards without switching tools.

Most platforms provide APIs, SDKs, and embedding options such as iframes and web components.

To learn more about embedded analytics, explore our What Is Embedded Analytics? Benefits, Examples, and Uses blog.

Why evaluating embedded analytics tools matters

Choosing an embedded analytics tool affects more than analytics capabilities. The right platform can reduce development effort, accelerate delivery, improve analytics adoption, and support long-term scalability.
Before selecting a platform, SaaS teams should evaluate whether it can support customer-facing analytics requirements today and continue to meet those needs as the product grows.

Supports SaaS-specific requirements: Features such as tenant isolation, white-labeling, and role-based access are essential for SaaS products but are not available in every platform.
Simplifies integration: Choosing a platform that cannot support future requirements may result in costly migrations and redevelopment.
Supports growth: As customer adoption and data volumes increase, the platform should continue to perform reliably without requiring major architectural changes.
Improves the user experience: Embedded analytics should feel like a natural part of the application rather than a separate tool.

SaaS teams can evaluate platforms based on the capabilities that directly affect implementation and customer adoption.

What to evaluate before buying embedded analytics tools

Adopting the right embedded analytics tool requires evaluating both technical and product-focused criteria. Most SaaS teams compare visible features such as dashboards, security, scalability, AI, and deployment options. However, the strongest evaluations go deeper by asking how each capability works in real SaaS environments.

Before comparing vendors, keep these questions in mind:

How is tenant isolation enforced beyond workspace-level separation?
What happens to dashboard performance during peak customer usage?
Which customization options are configuration-based, and which require development?
How are customer-created dashboards governed, secured, and maintained?
How does pricing change as users, tenants, dashboards, or data volume increase?
Can the platform support future requirements such as embedded analytics, AI-assisted insights, advanced exports, or customer-specific customization?
What support is available for migration, upgrades, and changing analytic needs?

These questions help turn the evaluation from a basic feature checklist into a practical review of scalability, governance, maintainability, and long-term product fit.

With these practical questions in mind, let’s look at the key evaluation areas SaaS teams should prioritize when comparing embedded analytics tools.

1. Multitenant architecture

If customer data is not properly isolated, a single configuration mistake can expose data across tenants. This makes multitenancy one of the most important requirements when evaluating embedded analytics tools.

When comparing platforms, assess whether they provide tenant-level customization, secure access controls, and efficient management across multiple customers.

To learn more about evaluating tenant isolation and scalability, explore our Multitenancy: Empowering Insights with Embedded BI blog.

2. White-labeling and customization

If analytics looks and behaves like a third-party tool, adoption can suffer. White-labeling helps create a consistent product experience for customers. Evaluate whether the platform supports branding, layout flexibility, role-based views, and tenant-specific configurations.

The more seamlessly analytics integrates into the product experience, the more likely customers are to adopt and use it regularly.

To dive deeper into customization and branding capabilities, explore our White-Label Analytics for SaaS: Features & Benefits in 2026 blog.

3. Self-service analytics capabilities

Modern customers expect more than static dashboards. They want to explore data, customize views, and answer questions on their own. With self-service analytics, users can interact with data independently and make faster decisions.

When evaluating SaaS analytics solutions, check whether users can build dashboards, apply filters, create personalized views, and access insights without developer support.

Strong self-service analytics reduces dashboard requests, lowers support costs, improves customer satisfaction, and helps users discover insights faster.

To explore how self-service capabilities improve user-driven data exploration, check out our How Governed Self-Service BI Prevents Data Sprawl blog.

4. Developer experience and integration flexibility

Developer experience directly affects how quickly analytics can be delivered and maintained. A platform should integrate smoothly with your existing application. Assess whether the solution provides robust APIs, SDKs, clear documentation, and flexible embedding options.

A strong developer experience reduces implementation effort, lowers maintenance costs, and enables engineering teams to focus on core product innovation instead of building analytics infrastructure.

To better understand integration and embedding approaches, explore our Embedded Analytics for SaaS Integration Platforms blog.

5. Security and governance

Security is critical for customer-facing analytics, especially when handling sensitive data. The platform should align with your organization’s compliance and governance standards.

Evaluate capabilities such as SAML or OAuth-based authentication, role-based access control, row-level security, encryption, audit logs, and data residency support. These features ensure secure access and consistent data governance across tenants.

Strong governance ensures users access only the data they are authorized to see.

To learn more about securing data and enforcing access control, explore our How to Secure Data with Row-Level Security in Bold BI blog.

6. Scalability and performance

As SaaS products grow, analytics workloads increase in complexity and volume. The platform must maintain consistent performance as usage scales.

Evaluate how the platform handles large datasets, concurrent users, and query performance. Look for support for query caching, pre-aggregation, and horizontal scaling, and verify that tenant workloads remain isolated under high demand.

Consistent performance helps maintain a reliable user experience as adoption increases.

7. Deployment flexibility

Different customers and industries require different deployment models, especially in regulated industries. Evaluate whether the SaaS analytics platform supports cloud and self-hosted environments. Consider data residency options, regional hosting capabilities, and backup and recovery features.

Flexible deployment options help support enterprise customers and regulatory needs.

To learn more about choosing the right deployment approach, explore our Choosing the Right BI Deployment Model: Cloud vs On-Premises blog.

8. AI-powered analytics and future readiness

AI capabilities have quickly become a major consideration when evaluating embedded analytics platforms. However, not all AI features deliver the same value.

Rather than simply asking whether a platform includes AI, evaluate how AI improves the overall analytics experience.

Consider questions such as:

Can users generate dashboards using natural language prompts?
Does AI explain trends, anomalies, and business drivers, or only generate visualizations?
Can users ask follow-up questions conversationally?
Are AI-generated insights governed by existing security and access controls?
Does AI reduce dependency on analysts and support teams?

The most effective AI-powered analytics platforms help users move from questions to decisions faster while maintaining governance, accuracy, and trust.

AI-powered analytics

To explore how AI enhances analytics and decision-making, check out our What is AI Analytics? Why It Matters & 4 Industry Use Cases blog.

9. Transparent and predictable pricing model

Pricing affects long-term scalability and cost predictability. An unclear pricing structure can lead to unexpected expenses as usage grows.

Evaluate whether pricing is based on users, usage, or a combination of the two. Review potential hidden costs such as API usage, data volume, or embedding limits to ensure the model aligns with your growth plans.

After evaluating these criteria, assess whether the platform fits your SaaS product’s technical requirements, customer experience goals, and long-term growth plans. Next, let’s explore the warning signs that may affect implementation, scalability, or customer adoption.

Common risks to watch for during analytics platform evaluation

Not every analytics platform is designed for embedded SaaS use cases. Identifying warning signs early can help teams avoid expensive mistakes.

Recognizing these red flags early can help SaaS teams focus on vendors that align with long-term business goals.

Build vs. buy embedded analytics tool: What is the right approach?

Before selecting an embedded analytics platform, SaaS teams must decide whether to build analytics in-house or use an existing solution.

Building offers full control but requires significant effort in development, scaling, and ongoing maintenance. Buying, on the other hand, allows teams to use an embedded analytics tool with ready-made features, faster implementation, and reduced engineering overhead.

If your team spends more time maintaining dashboards, permissions, and analytics features than building core product capabilities, it may be time to evaluate an embedded analytics platform. The right choice depends on your resources, timeline, and product complexity.

To better understand how to evaluate this decision and its long-term impact, refer to these resources:

Build vs Buy Analytics: Which Is Better for Your Business: Explains the trade-offs between building analytics internally and adopting a pre-built solution, focusing on cost, scalability, and maintenance.
Choosing Embedded BI: Build or Buy: Provides a focused comparison to help SaaS teams evaluate embedded BI solutions based on development effort, flexibility, and time-to-market.
Why Embedded BI is Better than Custom-Built BI Solutions: Compares embedded BI and custom-built BI approaches, highlighting the advantages of embedded solutions in areas such as time-to-market, development costs, scalability, maintenance, integration, and access to continuous innovation.

Understanding the trade-offs between building and buying analytics capabilities helps establish the key criteria for platform selection. However, seeing how these requirements apply in a real SaaS environment can provide additional context for the evaluation process.

Real-world application

The following example shows how these evaluation factors can influence a real-world SaaS company's embedded analytics decision.

ai-InteleKt improves embedded analytics delivery with Bold BI

ai-InteleKt needed a dashboarding and reporting platform that could help its customers build custom dashboards quickly and easily. The team also needed a solution that could be embedded inside its application and remain economical for the business.

Bold BI provided ai-InteleKt with an intuitive, drag-and-drop dashboarding environment that was easy to train users on while still supporting more advanced functions when needed. Its embedding capabilities also aligned with ai-InteleKt’s application requirements.

With support from the Bold BI team, ai-InteleKt was able to move its product to market more quickly.

As a result, ai-InteleKt gained:

A platform that is easy to embed in applications.
Dashboards and reports that are easy to build and customize.
Access to a responsive support team.
Supported flexible dashboard customization.
A solution Ravi Srinivasan recommended for its price, performance, and support.

This example shows why SaaS teams should evaluate more than feature availability. Ease of embedding, dashboard customization, user training, and vendor support can have a significant impact on the success of an embedded analytics initiative.

How Bold BI supports embedded analytics requirements

The following table maps common evaluation criteria to the capabilities SaaS teams typically look for when selecting an embedded analytics platform.

Evaluation area	How Bold BI addresses it
Multitenancy	Helps manage multiple customers with tenant-level separation and administration
White-labeling	Lets teams match dashboards to their product branding, themes, and layouts
Self-service analytics	Allows users to create and explore dashboards without relying on developers for every change
Developer experience	Provides APIs, SDKs, and embedding options for integrating analytics into existing applications
Security and governance	Helps protect customer data with role-based access, row-level security, SSO, and auditing
Deployment flexibility	Supports cloud and on-premises deployment so teams can meet different customer or compliance needs
AI-powered analytics	Helps users explore data with natural language queries and AI-assisted insights
Scalability	Supports growing datasets, user activity, and dashboard workloads as analytics adoption increases
Data connectivity	Allows teams to connect to 140+ data sources, making it easier to combine application, business, and customer data in a single analytics experience
Collaboration	Enables teams to share dashboards and insights across users or departments

By combining these capabilities within a single platform, Bold BI helps SaaS organizations embed analytics while reducing development effort and supporting long-term scalability.

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Get started with the right embedded analytics tools

Choosing the right platform requires more than feature comparison. It must fit your product, scale with your users, and deliver seamless embedded analytics for SaaS.

Bold BI® is built with these needs in mind, combining native multitenant support, flexible embedding, and deep customization. It gives SaaS teams the control and performance needed to deliver analytics as a true part of the product experience.

Ready to find the right embedded analytics platform for your SaaS product? Explore how Bold BI simplifies embedding, scales with your growth, and delivers a seamless analytics experience by signing up for a free trial.

Related resources:

Frequently asked questions

What is embedded analytics for SaaS?
Embedded analytics for SaaS is the integration of dashboards and data exploration features directly into a SaaS application, allowing users to access insights without leaving the product.
Why is embedded analytics important for SaaS products?
It improves user experience by bringing insights into the workflow, increases engagement, and helps SaaS teams deliver more value without requiring users to switch tools.
What should teams look for in a SaaS analytics platform?
Teams should evaluate multitenancy, customization, developer integration, security, scalability, and deployment flexibility when selecting a SaaS analytics platform.
How does an embedded analytics tool improve user experience?
An embedded analytics tool allows users to explore and interact with data directly within the application, which improves decision-making and reduces dependency on external tools.
What are common challenges when implementing embedded analytics?
Common challenges include integration complexity, limited customization, weak multitenant support, and unclear pricing models.
How can SaaS teams evaluate scalability?
Teams should assess how the platform handles concurrent users, large datasets, and query performance, along with features such as caching and workload management.
Should SaaS teams build or buy analytics capabilities?
This depends on available resources, time-to-market goals, and product complexity. Many teams choose to buy to reduce development effort and scale faster.
Can users ask questions in natural language with embedded analytics tools?
Many embedded analytics tools support natural language queries, allowing users to ask questions in plain language and receive relevant insights.

Materialized Views in Dashboards for Faster Insights

Bold BI by Syncfusion — Mon, 08 Jun 2026 07:46:45 +0000

TL;DR: Materialized views store precomputed query results so dashboards can query aggregated data instead of re-running expensive joins and calculations, reducing load times and improving responsiveness for BI workloads.

Introduction 

Modern BI dashboards are interactive, filters, drill-down/drill-up, date ranges, and multiple visualizations on a single dashboard can all trigger database queries. Each interaction can trigger expensive database queries involving joins, aggregations, and large scans.

As data volume and user concurrency grow, performance issues appear quickly. Organizations often experience dashboard abandonment, timeouts during peak hours, and rising database infrastructure costs. Over time, slow dashboards reduce trust in analytics and delay decision-making.

Materialized views solve this by moving repeated computation out of runtime queries. Instead of recalculating the same metrics repeatedly, materialized views store precomputed results that dashboards can query directly. By shifting heavy computation out of runtime queries, teams can achieve consistently fast BI performance without sacrificing interactivity.

In this blog, we’ll explain what materialized views are, their benefits, when to use them, and how they can improve dashboard performance.

What is a materialized view in a dashboard? 

A materialized view is a database object that stores the results of a query on disk (like a table), so reads are faster than recomputing the query each time. Dashboards read from this stored dataset rather than recomputing joins and aggregations on every request.

A standard view stores only the SQL definition and recomputes the results every time it is queried, whereas a materialized view stores both the SQL definition and the computed query results, enabling much faster read performance at the cost of requiring periodic refreshes to keep the data up to date.

For BI dashboards, this matters because workloads are typically read-heavy, repetitive, and focused on the same KPIs across many users.

Next, let’s look at why materialized views matter in dashboards.

Benefits of materialized views in dashboards 

Materialized views offer several advantages for dashboard performance, including: 

Faster dashboard load times: Since results are precomputed and stored, dashboards avoid executing expensive queries repeatedly, improving user adoption and reducing wait times.
Consistent performance at scale: By reducing the volume of data processed at runtime, materialized views ensure stable query performance even as datasets grow.
Improved responsiveness for interactive analytics: Queries run on pre-aggregated data instead of raw tables, enabling faster filtering, slicing, and drill-down interactions.
Reduced database load: Precomputed results minimize repeated JOIN operations and aggregations, significantly lowering CPU, memory, and I/O usage on the source database.
Precomputed aggregations: Complex calculations such as totals, averages, and groupings are calculated in advance, eliminating on-the-fly aggregation overhead.
Reduced JOIN operations: Materialized views can store results of multi-table joins, preventing repeated execution of costly JOIN queries during dashboard interactions.
Faster dashboard filtering: Since the dataset is already optimized and smaller in size, filter operations execute more quickly, improving interactivity.

These advantages make materialized views a common best practice for performance-critical BI dashboards.

When to use materialized views in dashboards 

Materialized views work best when:

Dashboards rely on complex joins across large tables.
Metrics involve heavy aggregations (daily, weekly, monthly rollups).
The same KPIs are queried by many users.
Data updates on a scheduled cadence (hourly or daily).
Consistent performance is more important than real-time freshness.

For databases like MySQL, materialized views are often implemented using summary tables and scheduled refresh logic. Learn more in our article.

Next, let's look at a practical example of how materialized views can be applied in a real‑time marketing analytics dashboard.

Real-time example: Using a materialized view in a marketing analytics dashboard

The marketing analytics dashboard presents a summarized view of campaign performance using pre‑aggregated data. It includes KPI cards for key metrics such as total visitors, conversion rate, cost per click, average leads, and click‑through rate, along with channel‑wise comparisons, engagement insights, and a monthly summary table by channel and destination. Interactive filters for time periods and sales channels allow users to explore trends without reloading the dashboard.

Without optimization, each visual would require joining raw event data, session logs, attribution records, and revenue tables, making queries increasingly expensive as data volume grows.

To improve performance, teams commonly materialize daily or monthly metrics by channel and time period, along with precomputed engagement and conversion measures. By querying these summarized datasets instead of raw tables, Bold BI dashboards load faster and remain responsive, even with multiple visuals and filters.

To quantify the impact of materialized views, we tested a marketing analytics dashboard using a dataset containing 50 million records. The results below compare widget query execution times when using raw data versus materialized views. In most tested widgets, materialized views reduced query execution times from seconds to sub-second performance in most widgets, significantly improving dashboard responsiveness and user experience.

Materialized views and aggregated tables both reduce runtime work. The best choice depends on your database features, refresh needs, and how flexible the dashboard queries must be.

Widget Name	Design Mode using Raw Data	Design Mode using Materialized Views
Select Period (Combo box)	00:00:09.018	00:00:00.001
Select Channel (Combo box)	00:00:03.246	00:00:00.001
Revenue vs Expenses (KPI Card)	00:00:01.326	00:00:00.001
Conversion Rate (Number Card)	00:00:04.195	00:00:00.001
Cost Per Click (Number Card)	00:00:03.196	00:00:00.001
Total Visitors (Number Card)	00:00:01.212	00:00:00.001
Total Sessions (Number Card)	00:00:01.907	00:00:00.005
Average Leads (Number Card)	00:00:01.873	00:00:00.002
Average Click Through Rate (Number Card)	00:00:03.729	00:00:00.001
Avg. Revenue Per Visitor Channel-wise (Chart)	00:00:04.212	00:00:00.003
Average Session Duration (Heat Map)	00:00:05.144	00:00:00.001
Top 5 Channels Based on Engagement Rate (Proportion Chart)	00:00:30.908	00:00:00.001
Summary Details (Grid)	00:00:21.081	00:00:00.011

The results demonstrate how materialized views can dramatically reduce query execution times for dashboard widgets that rely on filters, KPIs, and aggregated metrics. As dataset sizes increase, these optimizations become even more important for maintaining a responsive analytics experience.

Syntax for creating a materialized view In Bold BI

Materialized views are created using the CREATE MATERIALIZED VIEW statement. They store the physical results of a query, typically aggregations or joins used for performance metrics, so dashboards can access data faster without recalculating complex queries each time.

Below are examples of how materialized views are created across commonly used databases.

Syntax for creating an indexed view in SQL Server

In SQL Server, SQL Server does not provide native materialized views. Similar functionality is achieved through indexed views. These views store precomputed metrics such as revenue and expenses, enabling faster dashboard queries when accessed by BI tools.

CREATE VIEW dbo.customer_order_totals
WITH SCHEMABINDING
AS
SELECT
  CustomerID,
  SUM(OrderAmount) AS total_order_amount,
  COUNT_BIG(*) AS order_count
FROM dbo.Orders
GROUP BY CustomerID;
GO

CREATE UNIQUE CLUSTERED INDEX IX_customer_order_totals
ON dbo.customer_order_totals (CustomerID);

Once a clustered index is created on this view, the data will be materialized and optimized for performance.

Syntax for creating a materialized view in PostgreSQL

In PostgreSQL, materialized views store precomputed query results that can be refreshed on demand or on a schedule.

This approach is ideal for dashboards that rely on aggregated metrics and require fast query execution.

-- Create a materialized view that joins employees and departments
CREATE MATERIALIZED VIEW employee_department_info AS
SELECT
  e.employee_id,
  e.first_name,
  e.last_name,
  d.department_name
FROM emp e
JOIN departments d
  ON e.department_id = d.department_id;

-- Refresh to populate/update the stored results
REFRESH MATERIALIZED VIEW employee_department_info;

Syntax for creating a materialized view in Oracle

In Oracle, a materialized view is created using the CREATE MATERIALIZED VIEW statement along with options that define how the data is built and refreshed.

The example below precomputes employee-related metrics such as salary, department, and job details, enabling faster retrieval for reporting and dashboard analysis without querying the base table repeatedly.

CREATE MATERIALIZED VIEW "MATERIALVIEWSAMPLE" AS SELECT "EMPLOYEES_V"."JOB" AS "JOB", "EMPLOYEES_V"."MANAGER_ID" AS "MANAGER_ID", "EMPLOYEES_V"."COMMISSION" AS "COMMISSION", "EMPLOYEES_V"."EMPLOYEE_NAME" AS "EMPLOYEE_NAME", CAST("EMPLOYEES_V"."HIREDATE" AS DATE) AS "HIREDATE", "EMPLOYEES_V"."SALARY" AS "SALARY", "EMPLOYEES_V"."DEPARTMENT_ID" AS "DEPARTMENT_ID", "EMPLOYEES_V"."EMPLOYEE_ID" AS "EMPLOYEE_ID" FROM "SYSTEM"."EMPLOYEES_V" "EMPLOYEES_V"; **-- Optional: Create a unique index on the materialized view to improve refresh performance** CREATE UNIQUE INDEX "MV_EMPLOYEES_ID" ON "MATERIALVIEWSAMPLE"("EMPLOYEE_ID"); **-- Optional: Create a materialized view log for fast refresh** CREATE MATERIALIZED VIEW LOG ON "EMPLOYEES_V" WITH ROWID, SEQUENCE ("EMPLOYEE_ID", "HIREDATE", "SALARY", "DEPARTMENT_ID") INCLUDING NEW VALUES; ** -- Optional: Schedule a refresh for the materialized view (you can adjust the refresh **options as needed) EXEC DBMS_MVIEW.REFRESH('MATERIALVIEWSAMPLE', 'C', '', TRUE, FALSE, 0); ** -- Optional: Grant necessary privileges to users or roles** GRANT SELECT ON "MATERIALVIEWSAMPLE" TO YOUR_USER_OR_ROLE;

Syntax for creating a materialized view in Google BigQuery

This example precomputes KPI-related metrics such as total salary expenditure by aggregating data from the employee_data table, enabling faster retrieval without recalculating the value each time.

CREATE MATERIALIZED VIEW `sample.mv_salary_by_department` AS
SELECT
  department_id,
  SUM(salary) AS total_salary
FROM `sample.employee_data`
GROUP BY department_id;

Refreshing a materialized view

Available refresh options vary by database platform. Refresh strategy is the key trade-off when using materialized views, balancing data freshness, performance, and resource utilization to suit different analytical needs. The different refresh options include:

Refresh Type	Description	Best For
Manual	Refresh is triggered explicitly when needed	Small datasets
Scheduled	Refresh runs automatically at defined intervals	BI dashboards
Incremental	Update only new or modified data instead of rebuilding everything	Near real-time analytics
Full Refresh	Recompute the entire dataset from scratch	Historical analytics

To learn how to explore and connect materialized views in Bold BI across MSSQL, PostgreSQL, and Oracle, refer to our KB article.

Performance comparison: Raw tables vs materialized views

By leveraging precomputed and summarized data, organizations can eliminate repetitive query processing and significantly boost overall dashboard performance compared to relying on raw datasets.

The table below highlights the comparative performance improvements achieved when using materialized views and aggregated tables instead of querying raw data directly.

Metric	Raw Tables	Materialized Views	Aggregated Tables
Execution Time	Slow (full scan + runtime compute)	Fast (precomputed query results)	Very fast (minimal dataset, pre-aggregated)
Dashboard Load	High latency	Faster loading	Fastest loading
CPU Usage	High (heavy processing required)	Moderate (reduced computation)	Low (minimal processing)
Data Volume Scanned	Very high	Medium	Very low
Concurrent Users	Limited (performance degrades)	Improved handling	Highly scalable
Network Latency	High (large data transfer)	Reduced	Minimal

To learn more about optimizing database performance using these techniques, refer to our detailed KB article.

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Improve Dashboard Performance with Materialized Views in Bold BI

Materialized views are a highly effective way to improve dashboard performance for large-scale BI workloads. By storing precomputed query results, they reduce query execution time, minimize database load, and help dashboards remain responsive even as data volumes and user concurrency increase. For organizations that rely on interactive analytics, materialized views provide a practical balance between performance, scalability, and data accessibility.

With Bold BI®, you can leverage materialized views, aggregated tables, and optimized data models to build high-performance dashboards that deliver fast, reliable insights across your organization. Whether you're analyzing marketing performance, operational metrics, financial KPIs, or customer data, Bold BI helps ensure users get the information they need without performance bottlenecks.

Ready to build faster, more responsive dashboards?
Start your free Bold BI trial today and discover how optimized data strategies, powerful visualizations, and 140+ data connectors can help you deliver analytics at scale. You can also schedule a personalized demo to see how Bold BI fits your BI and embedded analytics requirements.

Frequently asked questions

1.
What is a materialized view?
A materialized view is a database object that stores the result of a query as a physical table. Unlike standard views, it saves precomputed data for faster retrieval.
2.
How do materialized views improve dashboard performance in Bold BI?
They improve performance by allowing dashboards to use pre-aggregated data instead of running complex queries repeatedly, resulting in faster load times and reduced database load.
3.
When should I use materialized views?
They are best used when working with large datasets, complex queries, or frequently accessed dashboards where performance optimization is needed.
4.
How often should materialized views be refreshed?
Refresh frequency depends on your data needs, ranging from real-time or frequent refreshes for dynamic data to daily or periodic refreshes for reporting scenarios.
5.
Does Bold BI create materialized views automatically?
No. Materialized views must be created in the database, and Bold BI connects to them as an optimized data source.
6.
Can materialized views improve embedded analytics performance?
Yes. They store precomputed results, which helps speed up queries and improves dashboard load times.
7.
What databases support materialized views with Bold BI?
Bold BI works with databases such as PostgreSQL, Oracle, SQL Server (indexed views), Redshift, BigQuery, and Snowflake.
8.
Are materialized views suitable for real-time dashboards?
Generally not. Because they depend on refresh schedules, materialized views are typically better suited for near-real-time or historical analytics than fully real-time dashboards.

Interactive Dashboards: Features, Why It Matters & Use Cases

Bold BI by Syncfusion — Mon, 08 Jun 2026 07:31:37 +0000

TL;DR: Interactive dashboards help users move from static data views to faster, self-service analysis through filters and real-time data exploration. They make it easier to uncover trends, investigate performance changes, and act on insights across sales, marketing, support, and operational workflows.

Introduction

As organizations collect more data from CRM, marketing, support, and operational systems, teams often struggle to move beyond static reports and understand what is driving performance changes. Interactive dashboards address this challenge by allowing users to explore data, investigate trends, and answer follow-up questions without waiting for additional dashboards.

Interactive dashboards provide a dynamic layer on top of underlying datasets, enabling both technical and non-technical users to drill down, filter, sort, and explore data in real time. Instead of relying on static snapshots, stakeholders can adjust views on demand, zooming into specific regions, timeframes, or segments to understand exactly where and why changes occur. This shift empowers decision-makers to explore insights independently, making organizations more agile, responsive, and truly data-driven.

In this blog, we’ll explore what interactive dashboards are, how they differ from static dashboards, the features and business use cases that make them valuable, and how Bold BI® supports interactive analytics and dashboard experiences.

What is an interactive dashboard?

An interactive dashboard is a dynamic analytics interface that enables users to explore, analyze, and interact with data through visualizations, filters, and other interactive controls. Unlike static dashboards, interactive dashboards allow users to investigate trends, answer follow-up questions, and uncover deeper insights from a single view without requiring a new report for every question. By enabling self-service data exploration, they help business users access insights faster, improve visibility into performance, and make more informed decisions.

Understanding what an interactive dashboard is provides only part of the picture. To fully appreciate its value, it is important to see how it differs from traditional static dashboards and why many organizations are shifting toward more interactive analytics experiences.

Interactive dashboards vs. static dashboards

A static dashboard presents data in a fixed format with limited or no user interaction. You can view predefined metrics and visualizations, but to explore deeper insights, you often need to create additional dashboards. Let's see how static dashboards differ from interactive dashboards and why interactivity has become increasingly important for modern business analytics.

Feature	Static dashboards	Interactive dashboards
Data updates	Periodic or scheduled refresh	Real-time or near real-time updates
User interaction	Limited or none	High (filters, drill-down, cross-filtering)
Data exploration	Fixed view of data	Dynamic exploration of multiple data layers
Flexibility	Rigid and predefined	Customizable and user-driven
Decision-making speed	Slower (requires new dashboards)	Faster (instant insights)
User dependency	High reliance on analysts/IT	Self-service for business users
Insight depth	High-level summary only	From summary to granular analysis
Personalization	Same view for all users	Role-based, personalized views
Use case	Analysis and compliance	Analysis, monitoring, and decision-making

These differences show how interactive dashboards extend beyond fixed ones by helping you explore data, investigate trends, and answer follow-up questions more efficiently.

Now, let’s explore why interactive dashboards are important for modern businesses.

Why interactive dashboards matter

Interactive dashboards are important in the following ways:

Faster decision-making with real-time insights: Dashboards help teams monitor key metrics as they change, so they can respond quickly to revenue shifts, operational issues, campaign performance changes, or customer behavior trends.
Self-service analytics with reduced dependency on analysts: Business users can answer follow-up questions independently through filters, drill-downs, and interactive controls, reducing reporting backlogs and allowing analytics teams to focus on strategic work.
Improved operational visibility: Teams can track performance across departments, processes, and KPIs from a single view, making it easier to identify bottlenecks, delays, or underperforming areas before they escalate.
Enhanced collaboration across teams: Shared dashboards give sales, marketing, support, operations, and leadership teams a consistent view of performance, helping them align faster and make decisions using the same data.
More confident, data-backed decisions: Users can explore the data behind summary metrics, validate assumptions, and understand what is driving changes before acting.

Together, these benefits help organizations move beyond static reporting and interact with data in a more meaningful way. Next, let's explore the common features of an interactive dashboard.

6 features of an interactive dashboard

Interactive dashboards enable users to explore and analyze data dynamically through real-time interaction, making it easier to uncover insights and act faster.

Drill Down: Users can move from high-level summaries to detailed data with a single click, enabling them to uncover patterns, analyze hierarchies, and gain deeper insights without leaving the dashboard.
Filtering: Users can refine datasets in real time, focus on relevant segments, and explore multiple perspectives within the same dashboard for more targeted analysis.
Linking: Users can navigate between related dashboards while preserving context, allowing continuous exploration and a more comprehensive understanding of data relationships.
Show or Hide Values: Users can toggle specific data points on or off to simplify visuals, highlight key comparisons, and focus on the most important insights.
Sorting: Users can reorder data dynamically to identify trends, compare performance, and spot outliers more effectively in a structured format.
Drill up: Users can seamlessly navigate from detailed data back to higher-level summaries with a single click, allowing them to view aggregated insights, compare broader trends, and maintain context without leaving the dashboard.

These are just a few examples. To enhance your understanding of all features you can use in your dashboard, refer to our Bold BI blog on “5 Interactive Data Visualization Features to Know.”

Now that we’ve explored the key features, let’s see how interactive dashboards are applied across different business scenarios.

Use cases of interactive dashboards

Interactive dashboards adapt to different roles and industries by surfacing relevant insights at the points where decisions are made.

Sales performance analysis

Sales teams need visibility into how performance varies by region, product, or sales stage. The Sales analysis dashboard allows managers to filter sales data by product or territory and drill into trends, regional performance, customer channels, and company‑level sales.

Managers can identify declining regions, compare product performance, and respond to sales trends before they affect revenue targets.

Marketing campaign optimization

Marketing teams often need visibility into email campaign performance over a selected period. The ActiveCampaign Email Marketing Performance Dashboard provides insights based on a filtered date range, allowing focused analysis of recent activity. This allows teams to identify underperforming campaigns earlier, optimize marketing spend, and improve campaign ROI and identify high-performing campaigns faster.

Customer support and operations monitoring

Support teams must quickly identify why ticket volumes increase, which issues affect customer satisfaction, and where response delays occur. The Call Center Agent Performance Dashboard allows teams to drill down into agent ticket data and performance metrics by individual agents, including call volumes, handling time, adherence, efficiency, and quality scores.

By exploring support data interactively, teams can identify operational bottlenecks earlier, compare agent performance, and improve overall service responsiveness.

Fraud detection and risk analysis

Risk and operations teams often review claim activity to understand unusual patterns, settlement delays, or unexpected financial variations. The Policy Claims Analysis Dashboard enables analysts to examine average claim amounts over the past five months and drill into claims data across relevant dimensions. This interactive approach helps teams investigate anomalies, assess claim performance trends, and support more informed reviews of potential risks and operational inefficiencies.

These use cases show how interactive dashboards support faster analysis across different business functions. Next, let’s see how Bold BI helps teams build and scale these dashboard experiences.

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How Bold BI helps build interactive dashboards

Organizations often struggle to deliver interactive analytics because data is spread across multiple systems, dashboard development requires technical resources, and business users need self-service access without compromising security.
Bold BI® addresses these challenges by providing a unified platform for building, embedding, and scaling interactive dashboards across teams and applications.

Drag-and-drop dashboard designer: Business users can create dashboards quickly without coding, reducing dashboard development time and making analytics more accessible across teams.
AI-powered dashboard creation and insights: Users can ask questions, generate dashboards, summarize trends, and identify outliers faster, reducing the time spent manually exploring reports.
Data connectivity and preparation: Organizations can connect more than 140 data sources, unify scattered data, and prepare it visually, making it easier to create trusted dashboards from multiple business systems.
Dynamic filters and real-time data interaction: Users can personalize dashboard views, monitor live performance, and investigate changes in real-time rather than waiting for static reports.
Role-based security and personalization: Organizations can expand dashboard access securely by showing each user only the data relevant to their role, team, or customer account.
Custom branding and visual design: Businesses can deliver dashboards that match their brand, product experience, and user expectations, especially for embedded or customer-facing analytics.
Collaboration, sharing, and version control: Teams can securely share insights, work from the same trusted dashboard, and manage changes without losing earlier versions.

Ready to build interactive dashboards with your own data? Start your free trial and create your first dashboard in minutes, no credit card or complex setup required.

Frequently asked questions

1.
What is an interactive dashboard?
An interactive dashboard is a dynamic analytics interface that enables users to explore, analyze, and interact with data through visual elements such as charts, KPIs, tables, and interactive visualizations.
2.
How is an interactive dashboard different from a static dashboard?
Static dashboards show fixed data views, while interactive dashboards allow users to filter, drill down, and explore data independently.
3.
What are the key features of interactive dashboards?
Interactive dashboards combine drill-down analysis, cross-filtering, dynamic controls, and AI-powered insights to turn complex data into an intuitive, hands-on experience.
4.
Why are interactive dashboards important for businesses?
They enable faster decisions, reduce analyst dependency, and improve visibility across teams.
5.
Who uses interactive dashboards?
Executives, managers, analysts, operations teams, and product teams.
6.
How do interactive dashboards improve decision-making?
They enhance decision-making by delivering real-time insights, spotting trends early, and enabling interactive data exploration. This empowers users to quickly understand changes, validate assumptions, and act with confidence.
7.
How does Bold BI simplify dashboard creation for business users?
Bold BI makes dashboard creation effortless with its intuitive drag-and-drop interface and prebuilt visualizations, enabling users to derive insights without technical expertise. This simplicity empowers teams to quickly turn raw data into interactive, visually compelling dashboards.
8.
Can Bold BI integrate with multiple data sources?
Yes, Bold BI seamlessly connects to a wide range of data sources, from databases and cloud services to APIs, bringing all your data into a single unified view. This ensures users can analyze and act on complete, up-to-date information without switching between tools.

DEV Community: Bold BI by Syncfusion

AI-Powered Dashboards: Why AI-Powered Dashboards: Why Enterprises Need Them in 2026

Introduction

What are AI‑powered dashboards?

Why AI-powered dashboards matter in 2026

How different industries use AI-powered dashboards

1. Improve sales forecasting

2. Identify student performance risks earlier

3. Optimize inventory and merchandising

4. Improve healthcare operational visibility

How Bold BI delivers AI‑powered dashboards

1. AI-powered analytics and conversational insights

2. Embedded AI analytics for applications and SaaS products

3. Unified AI Agent: Analyze data through conversational interfaces

4. Built for SaaS and multi-tenant environments

5. Flexible deployment and enterprise readiness

Real-world use case of AI-powered dashboards

Ready to accelerate decision-making with AI-powered dashboards?

Frequently asked questions

What are AI-powered dashboards?

How do AI-powered dashboards differ from traditional dashboards?

Can AI-powered dashboards predict customer churn?

Are AI-powered dashboards suitable for small businesses?

What features should an AI-powered dashboard platform include?

Why are AI-powered dashboards becoming important in 2026?

Integrate MySQL with Elasticsearch using Bold Data Hub

Introduction

Benefits of integrating MySQL and Elasticsearch data for analytics

Challenges of integrating MySQL and Elasticsearch

How Bold Data Hub simplifies data integration

Integrate MySQL with Elasticsearch data using Bold Data Hub

Step 1: Prepare the data sources

Step 2: Create a data pipeline in Bold Data Hub

Step 3: Add Elasticsearch as a data source

Step 4: Save the datasets

Joining tables

Step 1: Open the data source editor

Step 2: Identify the common fields

Step 3: Define the join condition

Step 4: Preview the combined datasets

How to create a dashboard using MySQL and Elasticsearch data

Start building unified analytics today

Frequently asked questions

What data sources does Bold Data Hub support?

Do I need any technical expertise to use Bold Data Hub?

Does Bold Data Hub offer additional features?

Do I need Bold Data Hub to use Bold BI?

7 Best Looker Alternatives You Should Explore in 2026

Introduction

What to consider when choosing Looker alternatives

7 best Looker alternatives you should explore in 2026

1. Bold BI

2. Zoho Analytics

3. Sisense

4. Domo

5. Tableau

6. Qlik Sense

7. ThoughtSpot

Why Bold BI is the right choice for you

Faster integrations without rebuilding workflows

Frequently asked questions

What makes Bold BI a leading Looker alternative?

What can you build with Bold BI?

Do I need technical skills to use Bold BI?

Can I share dashboards with my team?

Are Looker alternatives good for startups?

Live Dashboards: Why Enterprises Are Outgrowing Spreadsheets

Introduction

Why Growing Businesses Move Beyond Spreadsheet Reporting

The Hidden Cost of Spreadsheet-Based Reporting

Why Organizations Are Moving to Live Data Dashboards

Spreadsheet Reporting vs Live Dashboards: What’s the Difference

So, Should Live Dashboards Replace Spreadsheets?

What Should You Look for When Evaluating a Dashboard Software Platform?

Where Bold BI Fits as a Dashboard and Analytics Platform

From Spreadsheet Challenges to Business Outcomes with Bold BI

How Organizations Use Bold BI in Real World

Persistent systems improved enterprise analytics delivery

Traffic data exchange enhanced real-time visibility

Community License users accelerated analytics adoption