DEV Community: Bold BI by Syncfusion

How to Connect Snowflake Data to BI Dashboard

Bold BI by Syncfusion — Mon, 13 Apr 2026 15:54:10 +0000

TL;DR: Building a dashboard with Snowflake data in Bold BI® makes it easier to turn cloud data into real-time, interactive insights without unnecessary complexity. With a direct connection to Snowflake, Bold BI helps teams visualize key metrics, monitor performance, and make faster data-driven decisions through secure and scalable dashboards.

Introduction

As more organizations adopt cloud data platforms such as Snowflake, many teams look for efficient ways to access and visualize their data. Snowflake offers scalable storage and processing, but turning raw data into shareable dashboards may still require the right analytics tool.

Dashboarding tools like Bold BI can help by providing a web-based environment to connect to Snowflake, visualize data, and explore metrics through interactive dashboards. By integrating snowflake with Bold BI, teams can monitor key metrics, analyze trends, and share insights with stakeholders. This guide walks through connecting to Snowflake, preparing data, and building a dashboard in Bold BI to support more consistent, accessible reporting.

What is Snowflake?

Snowflake is a cloud-based data platform designed to store, process, and analyze large volumes of structured and semi-structured data. Built for scalability and performance, it enables organizations to manage data workloads efficiently without the limitations of traditional data warehouses.

Benefits of integrating Snowflake data into a BI platform

Integrating Snowflake with a BI platform can help organizations make better use of their cloud data by improving accessibility and visualization:

Improved data accessibility: Connecting Snowflake to a BI platform allows users to access and explore data through visual interfaces instead of relying only on complex queries.
Centralized data visualization: Integrating with Snowflake helps consolidate data into a single reporting layer, which can improve consistency and reduce fragmentation.
Scalable analytics: Since Snowflake is designed for scalable data storage and processing, BI platforms can leverage this to handle growing data volumes and user demands.
Real-time analytics: Many BI platforms support direct connections to Snowflake, which can help provide more up-to-date insights compared to manual reporting methods.
Enhanced collaboration and sharing: BI tools make it easier to share dashboards and reports across teams, helping stakeholders access the same information in a structured format.
Data-driven decision-making: By combining Snowflake’s data capabilities with BI visualizations, organizations can improve visibility into key metrics and support more informed decisions.

How to Connect Snowflake Data to Bold BI Dashboard

Connecting Snowflake to a BI dashboard can be tricky, but Bold BI simplifies it with an intuitive setup flow. Follow the steps below or refer to the Bold BI help documentation to configure your Snowflake data correctly.

Before you start:

Make sure you have the following details from your Snowflake account:

Account URL
Warehouse name
Database and schema
Username and password (or key‑based credentials)

You’ll also need access to Bold BI (cloud or embedded versions).

Step 1: Open the Bold BI dashboard Designer

Log in to the Bold BI dashboard page. From the left-hand navigation panel, click Data Sources to open the data source management page.

Step 2: Create a new data source

In the Data Sources page, click New Data Source in the top-right corner. From the dropdown menu, select Create Data Source. This opens the data source selection panel listing all supported connectors.

Step 3: Select Snowflake connector

On the Connection panel, select Snowflake connector option.

Step 4: Enter Snowflake connection details

The Snowflake data connection window will open in the data source configuration page as shown. Here, enter a Name for the data source and, optionally, a description. Specify the Server Name (Snowflake account URL), choose the Authentication Mechanism, and enter the UserName and Password. Next, select the required Mode (Live or Extract) and provide the Database name.
After entering all the details, click Connect to establish the connection.

Step 5: Load Snowflake Schemas in the Data Source Designer

After a successful connection, the Data Source Designer opens automatically. All available Snowflake schemas, such as INFORMATION_SCHEMA and PUBLIC, are loaded and displayed in the left panel as shown.

Step 6: Select tables from Snowflake schema

Here, expand INFORMATION_SCHEMA from the left panel. You will see a list of options, such as Tables. Expand Tables, then expand Views that appears inside it.

Step 7: Select a view, drag and drop, and preview data

Select a view from the list shown in the left panel. Drag the selected view into the design area, where the column details are displayed. Click Data Preview, as shown in the image, and verify that the records are loaded correctly.

Step 8: Save the data source

After verifying the data preview, click the more options (☰) icon in the top‑right corner of the data source designer. From the menu, you can choose Code to switch to Code mode and view or edit the generated query. To complete the process, click Save and Exit to save the Snowflake data source and close the designer.

Alternatively, select Continue to Dashboard to proceed directly to dashboard creation.

With your data source configured, the next step is turning Snowflake data into meaningful dashboards. Let's explore how you can achieve this with Bold BI

Creating a dashboard using Snowflake data in Bold BI

Using the data loaded from Snowflake, you can define metrics and KPIs to create a Snowflake usage analytics dashboard that provides clear visibility into warehouse activity, query behavior, and credit consumption. The following metrics can be used to build an interactive dashboard:

Total users: showing the number of active Snowflake users.
Total databases: indicating database availability and usage scope.
Query success rate: measuring successful query execution percentage.
Average elapsed query time: reflecting overall query latency.
Average execution time: highlighting query performance efficiency.
Total credits used: tracking overall compute consumption.

To analyze usage patterns and cost distribution in greater detail, the dashboard can also include:

Query amount by type: such as SELECT, CREATE, ALTER, and SHOW queries.
User numbers by database: showing how users are distributed across databases.
Top warehouses by credit usage: identifying high‑consumption warehouses.
Top warehouses by compute cost: supporting cost monitoring and optimization.
Total users and query volume by warehouse: comparing workload distribution across warehouses

A Snowflake usage analytics dashboard enables data platform and operations teams to monitor performance trends, detect unusual usage spikes, optimize warehouse utilization, and control Snowflake costs, all through a single, interactive dashboard created in Bold BI.

Real-world use case of visualizing snowflake data

The following are some use cases of a Snowflake dashboard.

Warehouse cost optimization

A data engineering manager notices a sudden spike in Snowflake costs but struggles to pinpoint which warehouse is driving the increase. Analyzing multiple reports manually takes time and delays cost-control decisions.

Using the dashboard, the manager can quickly view top warehouses by compute cost and credit usage. In the example widget, production shows the highest cost ($8.7K) and credit usage (1.12K), making it easy to identify optimization opportunities, such as resizing or auto-suspending warehouses.

Workload analysis by query type

A data platform owner wants to understand whether Snowflake is being used more for analytics or schema operations but has no clear breakdown of query types.

With the query count by type widget, the dashboard shows a dominance of SELECT queries (38), indicating heavy analytical usage. This helps in deciding workload isolation strategies, such as separating ETL and reporting workloads into different warehouses.

User activity and data adoption tracking

A data leader wants to know which datasets are actively used and which are underutilized to guide data investment decisions.

The user count by database visual reveals which databases are most accessed. This enables teams to prioritize high-value datasets and deprecate unused ones.

Scheduling Snowflake data refresh in Bold BI

Bold BI lets you schedule refresh for datasets and dashboards that use Snowflake data so your analytics stay up to date automatically at the time you choose.

You can automate Snowflake data refresh on a daily, weekly, or monthly basis (or set custom intervals), ensuring snowflake dashboards and reports consistently display the latest data without manual refresh. Bold BI also supports failure notifications, so you can be alerted if a scheduled refresh doesn’t complete successfully. To configure scheduling, follow the step-by-step guide in our documentation or the steps shown in the following GIF.

Collaborating and sharing Snowflake insights in Bold BI

To collaborate and share insights from your Snowflake dashboards in Bold BI, follow these steps or refer to the official documentation.

Step 1: Open the dashboards listing page

Log in to Bold BI Dashboards page, using your preferred login method and then click a Share option in the dashboard listing page.

[caption id="" align="alignnone" width="1484"] Open the dashboards listing page and click the share icon[/caption]

Step 2: Review the dashboard options

When the Share with others dialog opens, review the dashboard’s sharing details:

The current visibility is shown as private, meaning only users with explicit permissions can access the dashboard.
Click Change to modify the visibility if needed.
Click Copy link to copy the dashboard URL for sharing.
View the list of users or groups with whom the dashboard is already shared.
Click Manage Access to review or update existing permissions.

[caption id="" align="alignnone" width="1484"] Review the dashboard options[/caption]

Step 3: Add users or groups

In the Share with others dialog, use the search field to find and select the required user or group. Open the permission dropdown and assign the appropriate access level: Read; Read and Write; Read, Write, and Delete; or Download. In my case, I select Read access.

[caption id="" align="alignnone" width="1484"] Add users and select access option[/caption]

Step 4: Apply sharing permissions

After adding the required users or groups and assigning the appropriate permission level, click Share to apply the sharing settings. Once the access has been successfully applied, click Done to close the dialog. The selected users or groups will now have access to the dashboard according to the permissions granted.

[caption id="" align="alignnone" width="1484"] Click Share[/caption]

[caption id="" align="alignnone" width="1484"] Click Done[/caption]

Users with access to the dashboard can collaborate by:

Adding comments at the dashboard or widget level.
Replying to existing comments.
Mentioning others using @username.

Comments help teams discuss insights directly within Bold BI without using external communication tools. For more details about collaboration in Bold BI, refer to our blog on What Is Collaborative BI and How Does It Work? or collaborative analytics page.

Transform Snowflake data into business insights

Snowflake gives you a powerful, scalable foundation for analytics, but real value comes when stakeholders can see, explore, and act on that data without friction. By connecting Snowflake to Bold BI®, teams can convert warehouse data into interactive dashboards that update automatically, follow governance rules, and scale with business needs.

Whether you’re monitoring usage trends, analyzing operational performance, or delivering metrics to internal teams or customers, Bold BI helps you move from raw Snowflake tables to decision‑ready analytics, faster and with less effort.

Ready to turn your Snowflake data into dashboards your teams actually use? Start your free trial today or explore our live demo to experience how Snowflake data can be visualized and analyzed in Bold BI. Additionally, if you’re evaluating options, don’t miss our guide on the Top 5 Dashboarding Tools to Explore for Snowflake in 2025 to discover the best fit for your needs.

Frequently asked questions

1.
Does Bold BI support real-time Snowflake data?
Bold BI supports updates from Snowflake through scheduled refresh. For the most up-to-date results, use live mode (querying Snowflake at runtime) where appropriate.
2.
Can I use Snowflake tables and custom SQL?
Yes. You can connect using Snowflake tables, views, or custom SQL queries when creating the data source.
3.
How is performance handled for large Snowflake datasets?
All features work seamlessly in embedded environments.
4.
Does chart switching work in embedded mode?
For large datasets, use extract mode to cache data for faster dashboard load times and to reduce repeated queries to Snowflake.
5.
Is Snowflake data secure in Bold BI?
Yes. Bold BI respects Snowflake authentication and roles while applying its own access controls for dashboards and users.

Top 5 Use Cases for Embedded Analytics for SaaS in 2026

Bold BI by Syncfusion — Mon, 13 Apr 2026 15:45:49 +0000

TL;DR: Embedded analytics transforms SaaS products into insight-driven platforms. By delivering real-time dashboards, contextual insights, and self-service capabilities directly within applications by using platforms like Bold BI®, SaaS companies can improve user experience, increase retention, and unlock new revenue opportunities.

Introduction

In 2026, SaaS users expect more than flashy features. They expect answers. But in many applications, analytics is disconnected from the core experience. Data lives in separate tools, dashboards take time to generate, and users depend on analysts to answer even simple questions. This slows down decision-making and reduces the overall value of the product. Embedded analytics changes this by bringing insights directly into the application. Instead of switching tools or waiting for dashboards, users can access real-time data exactly where they work and take action immediately. For SaaS product teams, this is a shift from treating analytics as an add-on feature to treating it as a core product capability.

In this blog, we’ll explore the top five embedded analytics use cases for SaaS platforms in 2026 and how embedded analytics transforms applications into insight-driven platforms.

What is embedded analytics for SaaS?

Embedded analytics for SaaS is the integration of dashboards, and interactive data exploration directly inside a SaaS application so users can get answers without disrupting their workflow.

It typically includes:

In-app dashboards.
Contextual metrics inside workflows.
Drilldowns, filters, and other interactive features.
Role-based and tenant-aware access.
Exports, alerts, and scheduling.

The goal is simple: turn your SaaS product into a place where decisions happen, not just where data lives.

Why does embedded analytics for SaaS matter?

Embedded analytics for SaaS offers a variety of benefits to businesses, including:

Faster decision‑making: Users get real‑time insights directly inside the application, without switching tools.
Trusted and consistent data: Centralized KPIs and governed metrics eliminate conflicting numbers across spreadsheets and dashboards.
Higher customer adoption and retention: When users clearly see value and outcomes, they stay engaged and are more likely to renew.
Competitive differentiation and monetization: Embedded analytics help SaaS products stand out by providing premium features, unlocking data‑driven revenue streams.

Top 5 use cases for embedded analytics for SaaS to know in 2026

In 2026, successful SaaS companies are moving beyond basic dashboards to deliver more advanced and innovative BI experiences.

1. Real-time executive dashboards

Executives need fast, trustworthy insight, and real-time dashboards deliver a single view of performance without extra BI tools. Within your SaaS application, executives can:

View high-level KPIs aligned to business goals.
Compare performance across teams, regions, or periods.
Detect strategic risks and growth signals early.
Validate decisions with live data instead of assumptions.

By delivering executive-level insights to the top of the organization, your product becomes a strategic command layer. This helps managers make faster, more confident decisions while keeping teams aligned on the same real-time KPIs.

2. Embedded analytics inside workflows

In organizations, decisions happen inside day‑to‑day workflows. But when insights live elsewhere, users lose context, slowing execution. Embedded analytics in SaaS platforms solves this by delivering insights at the exact moment and place where action happens.

Embedded within workflows, analytics can surface:

Customer health metrics inside account views.
SLA trends within support tickets.
Performance indicators within campaigns or projects.
Operational signals tied to specific records.

Analytics doesn’t just summarize outcomes. It guides the next step. Users don’t ask “What happened?” after the fact. They see it in real time and act without changing their workflow.

3. Customer-facing analytics

Customer-facing analytics make data shareable and trustworthy by letting SaaS teams present product insights directly to clients, leaders, and stakeholders. This eliminates the friction, inconsistencies, and trust gaps caused by exporting data to third-party tools.

Your application can enable users to:

Create presentation-ready dashboards from live data.
Automate report delivery on a schedule.
Share role-specific views with controlled access.
Export trusted outputs for formal analytics.

Such analytics significantly streamlines insight delivery at all levels of the business by eliminating context switching to external BI tools or report designers. It also supports this securely, helping SaaS teams adhere to data governance and regulatory requirements.

4. Real-time alerts and exception monitoring

SaaS operations teams don’t need more dashboards; they need real-time alerts when something drifts. solves this by turning continuous data streams into actionable signals that demand attention.

Embedded monitoring capabilities allow users to:

Track operational metrics continuously.
Define thresholds for acceptable performance.
Receive alerts when anomalies occur.
Prioritize exceptions that require immediate action.

5. Self-service exploration for power users

Advanced users need to go beyond predefined dashboards, but relying on internal teams slows down insight discovery. Self-service analytics solves this by scaling access while balancing power-user flexibility with organizational control.

Within governed limits, users can:

Drill into metrics and investigate root causes.
Apply filters and segment data dynamically.
Create and save personalized analytical views.
Answer ad hoc questions independently.

This reduces analytics bottlenecks, increases advanced-user satisfaction, and creates a natural path for premium analytics adoption, all without compromising data integrity.

What to look for in an embedded analytics platform for SaaS

Whether you choose to build or buy, embedded analytics only delivers value when it feels native to your product and scales readily as your business grows. Key capabilities to prioritize include:

Seamless embedding: Delivers a fully white‑labeled, product‑native analytics experience so users stay engaged in your application without context switching or feeling like they’ve left your product.
Security at scale: Ensures each user only sees what they’re authorized to see through row‑level security and role‑based access, protecting sensitive data as usage grows.
Multitenant readiness: Safely isolates customer data within a shared architecture, enabling you to scale across many customers without compromising performance or trust.
Interactivity: Empowers users to explore insights on their own with drill-down and drill through capabilities, reducing reliance on analyst support.
Flexible integration: Fits into your existing ecosystem with APIs, connectors, and sharing options that adapt to different workflows and customer needs.
High performance: Maintains fast, reliable dashboard responsiveness even under heavy concurrency, ensuring analytics never becomes a bottleneck.
Strong governance: Preserves trust in data through audit logs, controlled datasets, and consistent metrics, giving teams confidence in the numbers they use to make decisions.

If you’re evaluating platforms, Bold BI is offering interactive dashboards, secure multitenant embedding, and AI‑assisted insights so you can deliver customer‑facing analytics without building a BI stack from scratch.

Ready to embed analytics in your SaaS Product?

In 2026, SaaS teams must thoughtfully choose between building and buying embedded analytics while balancing speed, scalability, and long-term product focus. Choosing a proven platform like Bold BI® allows teams to embed analytics faster, reduce manual effort, and stay focused on delivering value through their core SaaS product.
To help you make an informed decision, on whether to build or buy embedded analytics for your SaaS product, you can explore these resources to learn the pros and cons in each scenario:

Ready to see how Bold BI can elevate your product with embedded analytics? Sign up for a free trial or request a personalized demo now; to experience how embedded and self‑service analytics can help your organization grow revenue and cut costs.

Frequently asked questions

1.
What are embedded analytics tools?
Embedded analytics tools are platforms designed to integrate analytics capabilities directly into applications, rather than operating as standalone BI or analytics products.
2.
Are embedded analytics tools only relevant for SaaS?
No. Embedded analytics tools are also widely used in internal systems, enterprise portals, and commercial software products, not just SaaS applications.
3.
How do embedded analytics tools differ from traditional BI platforms?
Embedded analytics tools are typically evaluated as application infrastructure. They prioritize integration, scalability, controlled access, and extensibility, rather than standalone analytics exploration.
4.
What should teams prioritize when choosing an embedded analytics tool?
Key considerations include the embedding approach, security and access control, customization requirements, scalability, deployment flexibility, and licensing models.
5.
What are embedded analytics tools used for?
They are commonly used to deliver operational dashboards, customer-facing analytics, internal dashboards, and compliance reports as part of a software application.

12 Key Twilio Dashboard Metrics and KPIs You Should Track

Bold BI by Syncfusion — Fri, 27 Mar 2026 15:05:45 +0000

TL;DR: A Twilio dashboard unifies voice and SMS data so you can monitor delivery rates, call completion, costs, and daily trends in one place. With Bold BI®, teams spot failures faster, manage spending, and make confident, data‑driven decisions.

Introduction

Every call answered and every message delivered shapes the customer experience. From OTPs and alerts to support calls and follow-ups, businesses depend on Twilio to keep communication fast, reliable, and consistent.

But when voice and SMS data are spread across multiple logs and APIs, it becomes harder to spot failures, control costs, and understand performance. That is why tracking the right Twilio metrics and KPIs matters. In this blog, we’ll explore the 12 key Twilio dashboard metrics and KPIs that help teams monitor communication performance, reduce issues, and make better decisions.

Who this guide is for

This guide is for:

Customer support managers who monitor call volumes, call durations, missed calls, and agent performance to improve customer service operations.
Marketing teams tracking SMS campaign performance, delivery rates, engagement, and conversions generated through voice calls or text messages.
Sales teams analyzing call interactions and SMS responses from leads to improve follow-ups and increase conversion rates.
Operations or business intelligence teams overseeing overall communication activity, costs, and system performance across voice and messaging channels.
Product and engineering teams monitoring delivery errors, call quality, and API performance to ensure reliable communication services.
Executives or business leaders who review high-level communication metrics to evaluate customer engagement and operational efficiency.

You’ll learn the key Twilio KPIs and metrics, how Bold BI brings them into a single, interactive dashboard, and how different teams use these insights to improve communication reliability at scale.

What are Twilio dashboard metrics and KPIs?

Twilio dashboard metrics and KPIs are measurable indicators used to evaluate the performance of voice calls and SMS messaging. These metrics help organizations understand communication reliability, message delivery success, call outcomes, operational costs, and usage trends.

A Twilio voice calls and SMS tracking dashboard presents these metrics in a centralized view using KPI cards, charts, and trend lines. This makes it easier for both technical and business teams to monitor communication performance and identify issues quickly.

To learn more, explore our Twilio voice calls and SMS tracking dashboard to see how communication analytics can improve operational visibility.

Next, let’s explore why tracking Twilio communication metrics and KPIs is important.

Why track Twilio metrics and KPIs?

Tracking Twilio communication metrics and KPIs gives organizations better visibility into communication systems and helps them maintain reliable voice and messaging services. The benefits include:

Monitoring communication reliability: Metrics like message delivery status and call completion show whether customers are receiving messages and calls.
Managing communication costs: Tracking call and message costs helps control spending and detect inefficient routes.
Improving operational visibility: Metrics such as total calls, total messages, and duration highlight workload and activity levels.
Faster troubleshooting: Teams can isolate undelivered messages, failed calls, or unusual patterns immediately.
Supporting data-driven decisions: Consistent KPI monitoring improves routing efficiency and customer engagement.

To effectively monitor Twilio communication performance, organizations should focus on the most impactful metrics and KPIs. The following are some of the Twilio metrics and KPIs you can consider.

12 Twilio dashboard metrics and KPIs to track

Tracking the right Twilio metrics and KPIs helps organizations measure messaging reliability, communication volume, and operational costs. Below are twelve important metrics to track.

Average call duration

Average call duration measures the typical length of completed calls. This metric helps teams understand interaction patterns and identify unusual increases or declines in call length that may signal service issues or behavior changes.

Total call duration

Total call duration represents the total time spent across all calls during a selected period. It helps organizations estimate call handling workload and assess resource usage.

Total calls

Total calls represent the number of voice calls processed during the reporting period. Monitoring this metric helps teams understand call volume trends and changes in communication demand.

Cost per call

Cost per call calculates the average cost associated with each voice call. If the cost rises unexpectedly, it may indicate inefficient routing, carrier issues, or unnecessary retries.

Cost per message

Cost per message measures the average cost of sending an SMS. Monitoring this metric helps organizations control messaging expenses and optimize message routing.

Total cost of messages

Total cost of messages represents the overall spending on SMS messages during the reporting period.

Total messages

Total messages represents the total number of SMS messages sent (and/or received, depending on your dataset) during the selected period.

Total cost of calls

This metric shows the aggregated spending on voice calls during the selected period. It helps you restructure call flows or switch to cost‑effective routing based on trends.

Messages by delivery status

Messages by delivery status categorizes SMS messages into delivered, received, and undelivered groups. This KPI helps teams quickly evaluate messaging reliability and identify delivery failures.

Calls by completion status

Calls by completion status categorizes calls into completed, failed, or no-answer outcomes. Monitoring this KPI helps teams assess call reliability and identify factors affecting successful communication.

Messages by date

Messages by date displays daily messaging activity over a selected time period, such as the last 15 days. This trend helps organizations spot fluctuations in volume or delivery performance.

Calls by date

Calls by date visualizes daily counts of completed, failed, and unanswered calls. This metric helps teams identify patterns in call outcomes and investigate recurring communication issues.

Tracking these KPIs consistently helps teams identify communication issues early and improve operational monitoring.

Tips for tracking Twilio KPIs effectively

Effective tracking of Twilio metrics and KPIs is essential for improving communication reliability, controlling costs, and maintaining consistent customer experiences. Here are some tips for effective KPI tracking:

Align KPIs with communication goals: Choose metrics that reflect your business goals, such as improving message delivery, increasing call completion, or reducing communication costs.
Prioritize critical KPIs: Focus on the most important metrics, such as messages by delivery status, calls by completion status, cost per call, and cost per message.
Monitor KPIs regularly: Review Twilio KPIs frequently to identify delivery failures, call issues, or unusual cost increases before they affect customers.
Analyze status-based metrics: Track delivered, undelivered, completed, failed, and no-answer outcomes to quickly identify communication problems.
Use trend analysis: Monitor messages by date and calls by date to detect recurring activity patterns, unusual spikes, or drops in performance.
Track communication costs closely: Review total cost of calls, total cost of messages, cost per call, and cost per message to manage budgets and optimize communication spending.
Review and adjust KPIs regularly: As communication needs change, revisit your Twilio metrics and KPIs to ensure they still align with business priorities and operational goals.

When applied effectively, these metrics provide valuable insights across multiple operational and business scenarios.

Build an interactive Twilio dashboard with Bold BI

With Bold BI®, you can build an interactive dashboard using Twilio data, allowing you to get a more informed view of your metrics. Bold BI easily connects with Twilio data source through secure APIs and flexible data connectors. The Bold BI dashboard designer supports connecting to Twilio web services through the REST API. Therefore, you can fetch real-time data, transform it as needed, and visualize it using a wide range of interactive widgets such as charts, KPIs, and tables.

You can also create a full Twilio dashboard using Bold BI’s Prompt to Dashboard AI analytics feature just by typing a simple request in natural language. It interprets the prompt, connects to Twilio data, and automatically builds a dashboard with relevant widgets and value labels enabled, saving time while reducing reliance on developers or analysts.

To learn how to connect your Twilio data to Bold BI, follow the step-by-step guide provided in our Twilio Dashboard Example with Bold BI blog. Additionally, you can explore our 10 Best Practices for Designing Powerful Dashboards or watch our YouTube video if you are a beginner to start creating your first dashboard with Bold BI easily. Ready to start monitoring Twilio metrics more effectively? Sign up for a free trial of Bold BI and explore how communication analytics can improve your operations.

Frequently asked questions

Can Bold BI display real-time Twilio data?

Yes. Bold BI can refresh Twilio data frequently for near real-time monitoring.
Can we segment data by carrier, region, or channel?

Yes. Bold BI supports filtering and segmentation for deeper analysis.
How does this dashboard help reduce failures?

By surfacing delivery and call issues quickly, teams can identify root causes and resolve problems faster.
Can we analyze long-term performance trends?

Yes. Bold BI supports weekly, monthly, and multi-year trend analysis.
Is Twilio data secure in Bold BI?

Yes. Bold BI uses enterprise-grade security features including role-based permissions and authentication options.

Bold BI Support and Service Level Agreement in 2026

Bold BI by Syncfusion — Fri, 27 Mar 2026 08:32:29 +0000

We have updated the Bold BI® service level agreement (SLA), effective February 2026, designed to better support how modern organizations deploy, manage, and scale their analytics ecosystems.

The updated SLA applies to both deployment models- Bold BI Dedicated Cloud Edition and Bold BI On-Premises Edition.

This update provides greater clarity, stronger operational reliability, and more flexible support options for organizations running analytics at scale.

Below is an overview of what’s new.

A more structured support model

One of the most notable improvements is the introduction of a three-tier support structure:

Standard support
Business support
Enterprise support

Previously, Bold BI offered two support tiers. The new model introduces a clearly defined middle tier, giving organizations more flexibility to align support with their operational needs.

Organizations now have more flexibility to choose a support level that matches their operational requirements. Whether you’re building dashboards for internal reporting or running analytics for business-critical applications, the updated model makes it easier to select the right level of assistance.

Clearer and more structured onboarding

Successful analytics deployment begins with a smooth onboarding experience. The updated SLA introduces a more structured onboarding framework across all support tiers. Key onboarding improvements include:

Clearly defined onboarding timelines.
Defined limits for supported data connectors.
Tier-based deployment guidance hours.
Faster onboarding response commitments.
Expanded training hours depending on the support level.

These changes make the onboarding process more predictable and transparent. Teams adopting Bold BI can better plan their implementation while receiving the right level of technical assistance from the start.

Improved hosting availability for dedicated cloud deployments

For organizations running Bold BI Dedicated Cloud Edition, the updated SLA now defines clear availability targets based on support tier:

99.5% monthly uptime for Standard support.
99.9% monthly uptime for Business support.
99.95% monthly uptime for Enterprise support.

Enterprise deployments also include additional operational capabilities such as:

24/7 coverage for critical incidents.
Defined recovery point objective (RPO) and recovery time objective (RTO) targets.
Multiregional deployment options.
Extended backup retention policies.
Enhanced audit logging and security information and event management export options

These capabilities provide stronger operational reliability and governance for organizations running Bold BI in production environments or highly regulated industries.

Faster incident response and resolution targets

The updated SLA introduces clearer definitions for how support incidents are handled, including:

Initial response timelines.
Update frequency commitments.
Target resolution timeframes.

For Enterprise support, critical priority 1 (P1) incidents receive:

An initial response within 2 hours (24/7 coverage).
Regular updates during issue resolution.
Defined resolution targets depending on the issue type.

These improvements ensure that teams know exactly what to expect when issues arise, helping maintain continuity for business-critical analytics systems.

Structured escalation and faster hotfix delivery

Enterprise support also introduces more structured escalation procedures. These include:

Clearly defined escalation paths.
Dedicated escalation contacts.
The ability to deliver hotfixes outside the standard release cycle when necessary.

For production environments where downtime can affect business operations, faster escalation and remediation pathways can significantly reduce risk.

Expanded product lifecycle and extended support

The updated SLA also provides clearer guidance around product lifecycle coverage and management. Enhancements include:

Defined support windows for product versions.
Extended support coverage for Business and Enterprise tiers.
Formalized extended support options to help organizations during upgrade planning and transitions.

These improvements help organizations with strict change-management policies or regulated environments plan upgrades with greater predictability and confidence.

Enhanced infrastructure and security maintenance

For Dedicated Cloud Edition deployments, the SLA now provides more clarity around infrastructure maintenance and security responsibilities. Coverage includes:

Security patching for operating systems, runtimes, databases, and Kubernetes.
Accelerated patching for critical vulnerabilities under Enterprise support.
Patch coordination across production, user acceptance testing, and disaster recovery environments.
Advanced configuration and performance tuning assistance.

These updates help ensure that Bold BI environments remain secure, stable, and optimized for performance.

What this means for legacy customers

Existing customers’ current analytics environment will continue to be supported without disruption. The support experience will align with one of the updated tiers. They may benefit from improvements in areas such as response times, onboarding guidance, and operational support.

If you are unsure which support tier applies to your account, you can check the support portal or contact our team for assistance.

A more transparent and predictable support experience

Overall, the updated Bold BI SLA introduces a more structured and transparent framework for support and service delivery. With these updates, organizations can benefit from:

Greater flexibility in selecting support tiers.
Clearer onboarding timelines and guidance.
Improved availability commitments for Dedicated Cloud Edition deployments.
Defined incident response expectations.
Stronger lifecycle and upgrade planning support.
Enhanced infrastructure and security maintenance coverage.

Whether you are deploying Bold BI for internal reporting or powering large-scale analytics across your organization, these updates help ensure a reliable and well-supported analytics environment.

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For full details, please refer to the updated Bold BI® SLA documents linked above.

If you have questions about choosing the right support tier for your needs, our team will be happy to assist.

Frequently asked questions

1.
What are the support tiers available in the updated Bold BI SLA?
The updated Bold BI SLA introduces three support tiers:
- Standard support
- Business support
- Enterprise support
Each tier provides different levels of onboarding assistance, response times, and operational capabilities so organizations can select the level of support that best fits their deployment needs.
2.
What availability commitments are provided for Bold BI Dedicated Cloud Edition?
For Bold BI Dedicated Cloud Edition, the SLA defines tier-based monthly uptime targets:
- 99.5% availability for Standard support
- 99.9% availability for Business support
- 99.95% availability for Enterprise support
Enterprise deployments also include additional operational capabilities such as 24/7 coverage for critical incidents, defined RPO and RTO targets, and enhanced monitoring and logging options.
3.
How does the updated SLA improve support and issue resolution?
The updated SLA introduces clearer commitments for incident management, including:
- Defined initial response times for support requests.
- Regular status updates during issue resolution.
- Target resolution timelines based on issue priority.
For example, Enterprise support provides a 2-hour initial response for priority 1 incidents with 24/7 coverage, helping ensure faster engagement during critical situations.

How to Switch Chart Types Dynamically in Embedded Dashboards

Bold BI by Syncfusion — Fri, 27 Mar 2026 08:29:20 +0000

TL;DR: Users don’t always interact with data the same way. Some prefer trends, others comparisons, and some want proportions. Instead of cluttering dashboards with multiple charts, you can enable dynamic chart switching in Bold BI® using parameters, inter-widget linking, and visibility rules, allowing users to toggle chart types instantly within a single dashboard.

Introduction

Data visualization requirements vary from one user to another, especially in embedded analytics. While one user focuses on trends over time, another may want category comparisons, and a third may prefer understanding proportions at a glance. Bold BI offers a simple way to solve this by enabling users to switch chart types in embedded dashboards. With a combination of dashboard parameters, inter-widget linking, and conditional visibility, you can choose the visualization that works best for you, without duplicating widgets or building multiple dashboards.

In this blog, you’ll learn how to implement dynamic chart type switching in embedded dashboards using Bold BI, so users can instantly switch between visualizations without duplicating data or redesigning dashboards.

What is dynamic chart switching?

Dynamic chart switching is a dashboard technique that lets users toggle between different chart types (bar, column, pie, or line,) using the same dataset, without duplicating dashboards or rebuilding the layout.

In Bold BI, you can implement it by combining:

Dashboard parameter: Create a list parameter containing chart-type values like bar, column, pie, or doughnut.
Combo box (master widget): Bind the combo box to that parameter so the user can select the chart type.
Inter-widget linking + tab widget: Link the combo box to a tab widget that contains one chart per tab (all using the same data). When the selection changes, Bold BI switches to the matching tab (and chart) instantly.

Why dynamic chart switching matters in embedded dashboards

A single chart type rarely tells the whole story. Depending on the task, different visuals highlight different insights. Dynamic chart switching gives users the ability to change visualizations instantly and interactively. This gives you:

Multiple perspectives from the same data: Switching chart types lets users instantly view the same data as trends, comparisons, or proportions without leaving the dashboard.
Faster insight discovery: Users can change visualizations on the spot with no exports or extra navigation, making data analysis quicker and more seamless.
A personalized dashboard experience: Chart switching allows each user to choose the visualization style that suits their needs, creating a more intuitive and customized experience.
Reduced layout clutter: Instead of using multiple widgets for the same metric, dynamic switching keeps the dashboard clean by updating one visual area based on the user’s selection.

Try dynamic chart switching in your dashboard

Want to see how this works in a real scenario? Open your Bold BI dashboard and experiment with switching between chart types using parameters and tab widgets. It’s one of the simplest ways to make your dashboards more interactive without adding extra complexity.

Now, let’s walk through how to switch chart types in Bold BI embedded dashboards.

How to switch chart types in Bold BI embedded dashboards

Bold BI enables dynamic chart switching using a combination of parameters, tab widgets, and inter-widget linking. To configure dynamic chart switching in Bold BI embedded dashboards, follow these steps.

Step 1: Create a dashboard parameter with chart names

Log into the Bold BI dashboard designer home page and select the dashboard where you want to enable chart type switching.
Select Edit to open the dashboard in the designer page.
Select Data Sources then Edit in the top right corner of the designer page.
In the pop-up window, select the Relevant Dashboard Parameter from the menu.
In the Dashboard Parameter dialog, one default parameter will already be added. Under Mode, select List.
Next, add dashboard parameter values and then adjust the name of the dashboard parameter. Note that for this example, we’ll be using bar, column, pie, and doughnut values.

Step 2: Configure the dashboard with widgets

To configure the dashboard:

Drag and drop the combined widget onto the design canvas and add the configuration to the combined widget. Under Assign data, add the combo box widget and bind the dashboard parameter field to the Value section.
Next, add a Tab widget to the canvas and create one tab per chart type you want.
In each tab, add a chart widget configured with the same dataset. Then, set the chart type for that tab, whether bar, column, pie, doughnut, or otherwise. [caption id="" align="alignnone" width="1484"]
Disable the options for Hide Tab Header and Show Shadow in the tab widget. Likewise, disable the show shadow option for all chart widgets.

Step 3: Enabling inter-widget linking in the master widget

Select the option labeled inter-widget linking in the combo box widget.

Go to the properties panel for the combo box widget, check the box under inter-widget linking, and choose the tab widget from the list of items.

Step 4: Preview and publish

Once done, you can publish or preview the dashboard. The dashboard grants users the flexibility to dynamically switch the chart type by selecting a value from the combo box widget.

Best practices for designing interactive dashboards

To get the most out of dynamic chart switching:

Limit options to three to five chart types.
Keep the selector clearly visible.
Use consistent color schemes across charts.
Optimize queries to avoid performance issues.
Test visibility rules thoroughly.

For more guidance on building intuitive, high‑quality dashboards, refer to our blog on the 10 best practices for designing powerful dashboards.

Real-world use cases of switching chart types

Switching chart types dynamically in Bold BI embedded dashboards lets users instantly shift between composition views to answer different real-world questions in the same dashboard without rebuilding visuals.

Sales performance dashboard

A sales manager can use the sales analysis dashboard to track revenue, top products, customer segments, and month-to-month sales. By switching chart types, they can view monthly trends, which helps them quickly spot seasonal peaks, understand category performance, and make faster inventory decisions.

Patient health monitoring dashboard

In healthcare, a care coordinator can use a patient health monitoring dashboard to track key vitals like blood pressure, heart rate, and oxygen saturation for each patient. With chart switching, they can move from a line chart showing sleep trends over 15 days to a bar chart comparing steps walked with target steps. This helps them quickly spot changes in patient activity and overall health without switching dashboards.

School performance dashboard

A school administrator can use a school performance dashboard to monitor student scores, pass rates, and upcoming activities. With chart switching, they can change from a bar chart showing subject pass rates to a line chart that visualizes score trends, helping them quickly spot performance gaps.

Financial management dashboard

A finance manager can use a finance management dashboard to review revenue, working capital, and payment cycles. Switching chart types lets them view inventory trends as a line chart, compare AR and AP turnover with a bar chart, or see payable distribution in a pie chart for faster financial insights.

Build smarter, interactive dashboards today

Dynamic chart switching in dashboards is one of the most effective ways to make embedded dashboards more interactive and user-friendly. By combining dashboard parameters, inter-widget linking, and visibility rules, Bold BI® allows you to deliver personalized, flexible, and efficient embedded analytics experiences.

Ready to get started? Log in to Bold BI and try switching charts in your dashboard designer today. For more guidance, visit our documentation or request a personalized demo to make the most of this feature.

Frequently asked questions

1.
What is dynamic chart switching?

Dynamic chart switching allows users to change visualization types, bar, line, pie charts, and more, within the same dashboard without reloading or duplicating widgets.
2.
Can I add custom chart types?
All built-in chart widgets are supported. Fully custom visuals require custom embedding.'
3.
Can I customize the look of the master widget?
All features work seamlessly in embedded environments.
4.
Does chart switching work in embedded mode?
All dashboard runtime features behave the same in embedded dashboards.
5.
Is there a limit to how many chart types I can offer?
You can add many, but three to five chart options generally provide the best experience.

Integrate MySQL with Elasticsearch using Bold Data Hub

Bold BI by Syncfusion — Tue, 24 Mar 2026 07:51:10 +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.

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.
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]
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.

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.

Step 4: Save the datasets

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

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.

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.

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.

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.

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.

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.

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.

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 options like MySQL and Elasticsearch.

*2. Do I need any technical expertise to use Bold Data Hub? *
No advanced expertise is required. A basic understanding of the connectors you plan to use is enough.

*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 each project. 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 data sources. Bold Data Hub is optional and is mainly helpful when you need to prepare, clean, or combine data—like blending MySQL and Elasticsearch data before building dashboards.

Secure Embedded Dashboards with Role-Based Access Control

Bold BI by Syncfusion — Wed, 18 Mar 2026 08:42:23 +0000

TL;DR: Role-based access control (RBAC) is the foundation of secure dashboard access in Bold BI®. It determines who can view, edit, or manage dashboards and related resources such as categories, data sources, and schedules. By assigning permissions through groups, roles, and users, role‑based access keeps access predictable, reduces exposure risk, and scales cleanly for embedded analytics.

Introduction

Organizations increasingly rely on dashboards to monitor operations, track performance, and support faster decision-making. As dashboards become widely used across departments, applications, and customer portals, controlling who can access what information becomes a critical security requirement.

In many organizations, the same dashboard may be accessed by executives, analysts, developers, partners, or even external customers. Each group requires a different level of access. Without proper access controls, sensitive metrics or operational data can easily become visible to unintended users.

This is where role‑based access control (RBAC) becomes essential. Role‑based access control addresses this by assigning permissions through groups which act as roles instead of assigning permissions directly to individual users. In Bold BI, role‑based access control helps administrators manage access efficiently through users, groups, and admin roles, ensuring dashboards and related resources are available only to authorized users.

In this blog, we explore how role‑based access control works in Bold BI, why it matters for secure analytics, and how to configure it effectively. Before diving into the technical configuration, let’s look at who will benefit most from this guide.

What is role‑based access control?

Role‑based access control is a security model where permissions are assigned to groups which act as roles instead of individual users. This makes access management simpler and more consistent. Instead of configuring permissions for every user separately, administrators organize access through two components:

Users
Groups

When a user is added to a group or admin role, they automatically inherit all the permissions assigned to that role.

Now that we’ve covered the basics, let’s examine why role‑based access control is so important in embedded analytics environments.

Why role‑based access control matters for securing embedded dashboards

In analytics environments, role‑based access control helps organizations:

Prevent unauthorized access: Role‑based access ensures dashboards are visible only to users who are allowed to access them, reducing the risk of accidental data exposure.
Simplify permissions management: Permissions can be assigned to roles or groups instead of configuring each user individually.
Support scalable analytics environments: As organizations grow, role‑based access allows administrators to manage hundreds or thousands of users without increasing operational overhead.
Maintain consistent security policies: Role-based permissions ensure access rules remain consistent across dashboards and analytics resources.

Before configuring permissions, make sure that users and groups are already created in your Bold BI environment.

With that foundation in place, let’s walk through how role‑based access works with users and groups.

How to provide role‑based access control for users in Bold BI

Permissions let administrators assign access through groups which act as roles instead of assigning permissions directly to individual users who need unique or temporary permissions.

To configure user-level permissions:

Log in to the dashboard page using your preferred login method and select Users, which is available in the left‑side navigation panel.
In the pop-up window, select the user you want to configure to open their User Details
Select Manage Permissions to open the permissions panel.
Select the permission level you want to grant, based on the actions you want the user to be able to perform. In this case, I have selected the Read, Write, and Delete options under Access Mode. Next, select the User (System Administrator) in the Actions
A pop-up window will open as shown below. Select Add Permission.
Enable all available entities in the Entity Table and choose the Access Mode of your choice. Here, I’ve selected the Read, Write, Delete Select Add to grant permission.

7. A confirmation message will appear indicating that the permission has been added successfully.

To dive deeper into setting up role‑based access control for your users, explore our documentation.

How to implement role‑based access control for groups in Bold BI

Group-based permissions are the recommended way to manage role‑based access because permissions assigned to a group automatically apply to all users within that group. To implement group permissions:

Go to Users > Groups in the left-side navigation panel of the Bold BI dashboard page.
Select New Group, located at the top‑right corner. From the dropdown menu that appears, select Create Group to begin setting up a new group.

In the Add Group dialog, enter a name for the group and provide an optional description. Once the details are filled in, select Add Group to create the new group.

A confirmation message will appear at the top-right of the Groups page indicating that the group has been successfully created.

Next, open the newly created group and open the Group Details Here, select the Manage Permissions button from the Manage drop-down menu to open the permissions panel for the group.

Next, select Add Permission > Resource Permission to open a group dialog for assigning permissions as shown.

In the Entity section, choose the entities you want to configure. Select an Access Mode of your choice. When finished, select Add to apply the permission. /li>

A notification will be sent to indicate that the permission was added successfully.

To learn more about how to implement role‑based access for groups in Bold BI, explore our documentation.

Now that you’ve seen how role‑based access works at both user and group levels, let’s look at key practices that help you maintain a secure and scalable setup.

Role‑based access best practices in Bold BI

To maintain a secure and scalable deployment in Bold BI:

Prefer group-based permissions over individual permissions.
Use role‑based access primarily for dashboard access control, not data filtering.
Follow the principle of least privilege, granting only the required permissions.
Use stable identifiers such as TenantID or RegionID for consistent access management.
Regularly review group memberships and permissions.
Enable audit logs to track permission changes.
For embedded deployments, generate embed tokens securely on the server side.

By clearly separating access control from data filtering mechanisms such as row-level security, organizations can securely scale analytics environments without duplicating dashboards.

To understand how role‑based access delivers value in real environments, here are some examples of how different industries use it to secure sensitive analytics.

Use cases of role‑based access

Role-based access control provides significant benefits across industries.

1. Healthcare

Challenge

Healthcare teams work with highly sensitive patient information, and exposing clinical dashboards to unauthorized staff creates privacy, compliance, and data‑security risks.

How role‑based access helps
Role‑based access restricts patient‑related dashboards to authorized clinical roles only, ensuring that only approved medical staff can view sensitive insights and helping maintain strict healthcare privacy compliance.

2. Retail

Challenge

Retail organizations operate across multiple stores and regions. Giving all teams unrestricted dashboard access leads to cross‑region visibility issues and inconsistent operational insights.

How role‑based access helps
Role‑based access assigns dashboard access based on region or store role, ensuring headquarters, regional managers, and store managers see only the dashboards relevant to their responsibilities.

3. Finance

Challenge

Financial institutions must tightly control access to dashboards containing confidential financial data, and unrestricted visibility can cause compliance violations and data‑security risks.

How role‑based access helps
Role‑based access limits access to sensitive financial dashboards by user role, such as advisors, analysts, or executives, ensuring each user sees only the insights appropriate for their responsibilities.

Conclusion

Role-based access control plays a critical role in securing analytics environments. By assigning permissions through users and groups, organizations can efficiently manage dashboard access while reducing security risks.

Bold BI provides flexible role‑based access capabilities that allow administrators to control access to dashboards, data sources, categories, and other resources. This approach simplifies permissions management, reduces dashboard duplication, and enables organizations to securely scale analytics across internal teams and embedded applications.

To explore these capabilities further, review the Bold BI® documentation on permissions, or start a free trial or request a personalized demo to experience secure and scalable dashboard access in action.

Frequently asked questions

Does Bold BI support roles directly?

Bold BI uses groups to represent roles. Users inherit permissions from the groups they belong to.
How can I prevent users from modifying dashboard permissions?
Use group-based role‑based access and ensure users are assigned only the required permissions based on the principle of least privilege.
Can role‑based access reduce dashboard duplication?
Yes. Role‑based access allows a single dashboard to serve multiple audiences without creating duplicate dashboards for each team.
Can role‑based access be used without individual user accounts?
Yes. Anonymous access or identity provider integrations can apply group permissions automatically.

How to Use Widget-Level Filters in Dashboards Effectively

Bold BI by Syncfusion — Thu, 12 Mar 2026 08:56:57 +0000

TL;DR: Widget-level filtering lets you control which widgets respond to filters and interactions in a dashboard. Instead of applying a filter to the entire dashboard, you can apply it only to selected visuals. In Bold BI®, this can be achieved through widget-level data filters, dedicated filter widgets, and cross-data-source filtering, allowing you to create dashboards where KPIs remain stable while exploratory visuals respond to user interactions.

Introduction

Interactive dashboards help users explore data from different perspectives. However, not every visualization should respond to every filter or interaction. For example, when a dashboard contains executive KPIs, summary indicators, and detailed charts, applying a global filter can unintentionally change metrics that should remain constant. This can lead to confusion, incorrect interpretation, or the need to maintain multiple dashboard versions for different users.

Widget-level filters in dashboards solve this challenge. They allow dashboard designers to apply filters only to selected widgets, ensuring that some visuals remain fixed while others respond to interactions. In this blog, you'll learn how widget-level filtering works and how to implement it effectively in Bold BI dashboards.

Understanding widget-level filtering

Widget-level filtering is a dashboard design technique where filters are applied only to specific widgets instead of the entire dashboard. This means your KPI tiles and summary widgets remain stable, while charts and tables meant for exploration respond to user‑driven filters. This approach allows dashboards to provide both stable metrics and interactive exploration.

Why widget-level filtering matters in dashboards

Dashboards usually serve several purposes at once, for example executive summaries, operational monitoring, and data exploration. A single global filter affects all visuals, which can cause issues such as:

Executive KPIs change when a user interacts with a visual.
Users struggle to compare different segments because everything responds at once.
Difficulty personalizing dashboards without creating duplicates.

Widget-level filters in dashboards prevent these problems by letting you choose which widgets respond to which filters. This improves clarity, flexibility, and usability. Next, we’ll explore the types of widget-level filters in Bold BI.

Types of widget-level filters in Bold BI

Bold BI provides three methods for applying widget-level filters. These methods define how widgets respond to filters and interactions.

1. Widget-level data filtering

Widget-level data filtering applies filter conditions directly inside a widget. These filters are defined in the widget’s Assign Data panel and affect only that widget. These filters can be applied to measure columns, dimension columns, and date columns. Below is a clear breakdown of each filter type and how it works.

Measure filters: Measure filters let you control data based on numeric values. They are configured in the measure filter dialog. They can be used to show products with sales greater than a specific value, for example.

Dimension filters: Dimension filters are used for categorical fields such as product category, customer segment, or region. They can be used to show sales for selected regions.

Date filters: Date filters are used for fixed date ranges. They can be used to show orders between two selected dates.

Relative date filters: Relative date filters are used for dynamic time periods such as previous week, previous month, or previous year. The filter updates automatically as time progresses. You can also pin the filter to a specific anchor date so results are always relative to that date.

In the Sales Performance Dashboard, selecting previous month ensures the widget always displays data for the previous full month, without manually updating the filter. This approach is useful for dashboards that require automatic time-based updates.

2. Dedicated filter widgets

A dedicated filter widget allows a filter control to affect only selected widgets. This uses the master and listener configuration. The master widget is the filter control that drives the interaction, and it can be enabled to act as a master property for each widget. Listener widgets are the visuals that respond to the filter, and other widgets remain unchanged.

In the School Performance Dashboard, a grade dropdown filter may control charts showing subject performance and student distribution while overall enrollment KPIs remain unchanged. This makes dashboards easier to use for different roles while preserving key summary metrics.

3. Cross data source filtering

Cross data source filtering in Bold BI allows widgets that rely on different data sources to work together during user interactions. When you link equivalent fields between a master widget and its listener widgets, any selection made in one visual can automatically refine the data shown in the others, and this works without combining datasets or performing joins in the database.

In the Real Estate Management Dashboard, you might have a combo box showing month (year) from one data source and a chart showing sales vs. target from another data source. By mapping a common field such as sales date, selecting a month in the combo box can filter the sales chart.

Now that you have seen the types of widget-level filters in Bold BI, let’s explore how you can apply them in dashboards.

How to apply widget-level filters in Bold BI dashboards

Follow these steps to set up a widget-level filter in Bold BI:

Open a Bold BI dashboard and select the widget to which you want to apply the filter. Here, I’ve selected the bar chart Top 5 Products by Sales.
Click the Properties icon for the widget, then click Assign Data to open the data panel.
In the data panel, drag the field you want to filter by into the Filters. Here, I have used the Country field.
Click the Settings icon next to the added filter field, and then select Edit to open the filter dialog.
Set the filter conditions then select Apply. Here, I have filtered by the country Argentina. The widget will display the filter condition already applied.
Next, click on the Publish option to save and apply the changes to your dashboard.

After publishing, switch back to view mode or reopen the dashboard. The widget will now display data based on the applied filter condition, as shown below. Here, you can verify that the filter is applied only to the selected widget while the rest of the widgets remain unchanged.

If you want to personalize other widgets, repeat the same steps for each widget and publish the dashboard again. To learn more about widget filtering, refer to our documentation.

Now that you’ve seen how to configure a widget filter, here are practical scenarios where it prevents dashboard sprawl while keeping KPIs consistent.

Use case of widget-level filters in dashboards

Here are some real-world use cases where you can see widget-level filters in action as they keep KPIs stable while allowing role-specific exploration.

Sales: Sales analysis dashboard

In the sales analysis dashboard, a chart may display sales for all companies. Applying a Top N manual filter to the company field allows the chart to focus on the highest-performing accounts, making the visualization clearer and more actionable.

Using manual filters in a sales analysis dashboard

Education: School performance dashboard

In the school performance dashboard, a year filter may initially display all enrollment years. Using a relative date filter helps automatically focus the dashboard on the current academic year, improving the relevance of the data shown.

Using the relative date filter in a school performance dashboard

These use cases highlight how widget‑level filters improve personalization, clarity, and usability. The key takeaways below summarize why widget‑level filtering is such a valuable part of effective dashboard design.

Key takeaways

Widget-level filtering is an interaction control feature. It lets you decide which visuals respond to filters and which remain fixed. It is not a security feature. Combine manual widget filters, dedicated filter widgets (master → listener), and cross data source filters to deliver precise and predictable interactions that fit executive reviews and embedded experiences.

Ready to reduce dashboard duplicates and keep KPIs stable? Apply widget-level filters in Bold BI® to control exactly which visuals users can filter. For detailed insights:

Explore Bold BI documentation to configure advanced filtering options
Try Bold BI and build your first personalized dashboard
Schedule a demo to see role-aware dashboards in action
Explore the following related resources

Empower every user with the right data, without sacrificing clarity, control, or consistency.

Frequently asked questions

Does widget-level filtering handle security?

No. Security belongs to user filters and row level security. Widget-level filtering only controls the interaction scope.
Can I limit a filter widget to certain visuals?

Yes. Configure the filter widget as a master widget and assign listener widgets that should respond.
Can I pre-filter a single widget?

Yes. Apply a manual filter directly inside the widget.
Can one widget filter another widget?

Yes. Configure cross data source filtering between widgets to enable interaction-based filtering.

Bold BI Earns Multiple Badges in G2’s Winter 2026 Reports

Bold BI by Syncfusion — Tue, 10 Mar 2026 15:30:27 +0000

TL;DR: Bold BI earned 17 badges in the G2 Winter 2026 Reports for its excellence across business intelligence, embedded analytics, regional performance, and high‑performer categories. These customer‑driven recognitions highlight Bold BI’s commitment to delivering powerful analytics, intuitive dashboards, and scalable embedded BI solutions for organizations of all sizes.

We are excited to share that the G2 Winter 2026 Reports are here, bringing a new season of accolades for Bold BI. These recognitions highlight our continued commitment to delivering intuitive, customer‑focused analytics that make insight discovery faster for teams of all sizes.

In this post, we’ll walk you through the badges we earned, what they mean, and how they highlight Bold BI as the leader of modern business intelligence and embedded analytics.

Why G2 badges matter

G2 s a widely used software marketplace where quarterly reports are driven by verified user reviews. This means every Bold BI badge reflects real customer satisfaction around performance, usability, deployment flexibility, and analytics clarity.

Earning 17 badges in a single quarter is a meaningful validation of Bold BI’s ongoing investment in AI-powered insight discovery, embedded BI innovation, and user-first product design.

G2 Winter 2026 reports featuring Bold BI

Bold BI continues to shine in G2’s Winter 2026 Reports, earning 17 badges across major analytics categories. These recognitions reflect real customer experiences, measurable value, and our focus on scalable analytics.

So, let’s take a closer look at the Winter 2026 G2 badges Bold BI received.

Enterprise excellence G2 badges: Easiest experiences and highest user adoption

Bold BI earned the following G2 recognitions in the Analytics Platforms – Enterprise report:

Easiest To Do Business With
Highest User Adoption

Why they matter: Enterprises need analytics solutions that are easy to adopt, maintain, and scale. These badges reflect high user satisfaction with Bold BI across the adoption, deployment, and usage phases.

Leadership in BI and embedded BI: Setting the analytics standard

Bold BI continues to lead the way in business intelligence and embedded business intelligence, delivering intuitive dashboards, flexible embedding, and enterprise‑ready analytics. We received Leader badges in the following reports:

Embedded BI and Business Intelligence – Small Business
Business Intelligence – Mid‑Market
Business Intelligence and Embedded BI

Why these matter: Being named a Leader across market-size categories proves Bold BI consistently delivers usability, innovation, and value, no matter how organizations use analytics.

Regional strength G2 badges: A global footprint of high performance

Bold BI earned High Performer badges in the following regional reports:

Embedded BI – Asia
Embedded BI – Asia Pacific
Embedded BI – EMEA
Embedded BI – India
Embedded BI – Americas

Why these matter: These recognitions reflect Bold BI’s effectiveness in diverse markets, each with unique analytics needs.

High performer across business segments

Bold BI also earned High Performer distinctions in the following reports:

Analytics Platforms – Enterprise
Analytics Platforms – Global
Embedded BI – Americas Region, Small Business
Analytics Platforms – Small Business

Why they matter: These badges show how Bold BI adapts to organizations at various stages of analytics maturity. From small teams building their first dashboards to enterprises deploying embedded BI across departments, Bold BI is a reliable solution.

You can check out G2 Winter 2026 Reports to learn more about these 17 badges.

What makes Bold BI stand out

Since its launch, we’ve been hard at work enhancing the Bold BI analytics experience. Here are some of the latest innovations:

AI-powered analytics: Experience faster insight discovery with natural language queries, automated dashboard summaries, and secure BYOK integration for OpenAI and Azure, making advanced analytics accessible without complexity.
Anonymous user access: Publish dashboards externally with secure, login-free access while maintaining full audit tracking for visibility and compliance, ideal for customer-facing analytics and stakeholder reporting.
Cloud storage support: Seamlessly connect to Amazon S3, Oracle Cloud, and other storage platforms to support scalable, cloud-native deployments and centralized data management.
Dashboard design upgrades: Build visually engaging, structured dashboards using fixed layouts, modern visual components, and fully customizable widgets for richer storytelling and a better user experience.
Flexible deployment: Deploy in the cloud or on-premises based on your infrastructure needs.
Excellent customer support: Receive fast, expert help with responsive support and authoritative guidance from day one.
Over 140 data connectors: Instantly connect to a wide range of data sources.
Intuitive interface: Build and customize dashboards with a clean, user-friendly interface that accelerates insight delivery.

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Start your analytics journey

Bold BI’s recognition in the G2 Winter 2026 Reports, with 17 badges spanning the business intelligence, embedded BI, and analytics platforms categories, is evidence of our commitment to delivering intuitive, scalable, and secure analytics solutions. These honors reflect the trust our customers place in us.

Whether you’re leading a small business seeking ease-of-use or part of an enterprise needing robust performance, Bold BI^® empowers you to turn data into decisions with confidence. Experience Bold BI with a free trial, no credit card required. Create interactive dashboards, embed analytics into your apps, and see why we’re a G2 High Performer.

CFO Dashboard Examples: Improve ROI & Reduce Financial Risk

Bold BI by Syncfusion — Mon, 09 Mar 2026 06:55:12 +0000

TL;DR: CFO dashboards give finance leaders a real-time view of revenue, expenses, cash flow, and financial risk. By consolidating financial data into one place, they help organizations improve ROI, control costs, and make faster financial decisions.

Introduction

Chief Financial Officers are responsible for guiding organizations toward profitable and financially stable growth. Many finance teams still rely on spreadsheets and manually compiled reports to track revenue, expenses, and cash flow. This creates delays in month-end reporting, increases the risk of calculation errors, and makes it difficult to identify financial issues early. When financial insights arrive late, identifying risks, tracking performance, and making timely decisions becomes difficult.

To solve this problem, CFO dashboards bring essential financial metrics, such as revenue, costs, cash flow, and budget performance, into a single, organized view. This reduces manual reporting and gives finance leaders quick insight into performance, ROI, and potential risks, enabling faster and more informed decision‑making.

Here’s what a CFO dashboard includes, the KPIs that matter most, and real-world examples you can model.

What is a CFO dashboard?

A CFO dashboard is a financial dashboard designed to give finance leaders a consolidated view of an organization's financial health. It displays key performance indicators, such as revenue, profitability, expenses, cash flow, and financial risk metrics, in a single visual interface.

Why CFOs need financial dashboards

CFO dashboards strengthen financial performance by consolidating revenue, cost, cash, and risk signals into a single view. They help organizations improve ROI and maintain financial stability in the following ways:

Enable faster financial decisions through instant visibility into key metrics like revenue growth, expense trends, profit margins, and cash‑flow movements.
Improve ROI visibility by clearly showing which projects, products, or departments generate the highest financial returns and which ones underperform.
Help detect financial risks early by revealing unusual variances, rising costs, declining margins, or signs of cash‑flow pressure before they escalate.
Strengthen cash‑flow management with real‑time tracking of inflow, outflow, liquidity levels, and working‑capital cycles to support smoother financial operations.
Increase financial transparency by giving executives and finance teams a unified view of financial health, helping everyone align decisions with overall financial goals.

In summary, CFO dashboards improve ROI and reduce financial risk through clear, timely, and actionable financial insights. Now, let’s explore the metrics and KPIs every CFO dashboard should include.

Which financial KPIs should CFOs track?

To effectively support a financial strategy, CFO dashboards should focus on metrics that provide both performance insights and risk visibility.

Revenue growth: Revenue growth shows how an organization’s income changes over time. Monitoring this trend helps finance leaders see whether the business is growing or contracting.
Profit margin: Profit margin measures how efficiently revenue is converted into profit. It helps identify whether costs are increasing faster than revenue.
Cash-flow: Cash-flow visibility is essential for maintaining liquidity and meeting financial obligations. A dashboard should track operating cash flow, cash inflow and outflow, and key liquidity trends.
Budget vs. actual performance: Comparing planned budgets with actual spending helps CFOs identify overspending and maintain financial discipline. This metric supports better cost management and more accurate financial forecasting.
Operating expenses: Operating expenses provide insight into how resources are being used across the organization. Monitoring expense trends helps finance teams identify opportunities for cost optimization.
Return on investment (ROI): ROI helps CFOs evaluate whether initiatives and investments are delivering measurable financial value. Tracking ROI provides clarity on payback period and long‑term financial impact.
Accounts receivable (AR) aging/DSO: AR aging and Days Sales Outstanding (DSO) highlight how quickly a business collects cash from customers. This KPI helps CFOs reduce cash-flow risk and spot collection issues early.
Working capital metrics: Working capital indicators, such as the current ratio, quick ratio, and cash conversion cycle, show the organization’s short-term financial health. These KPIs help CFOs manage liquidity, reduce financing pressure, and avoid operational disruptions.
Forecast accuracy: Forecast accuracy compares forecasted numbers to actual results over time. Tracking it helps CFOs improve planning reliability, reduce unexpected variances, and make better investment and hiring decisions.
Debt-to-equity ratio: The debt-to-equity ratio measures financial leverage by comparing total company debt to shareholder equity. Monitoring this ratio helps CFOs assess financial risk, evaluate borrowing capacity, and ensure the organization maintains a sustainable capital structure.

Now that we’ve covered the essential KPIs, let’s explore real‑world CFO dashboard examples that bring these metrics to life.

CFO dashboard examples used by finance teams

In most organizations, CFO dashboards consist of multiple financial dashboards that together provide a complete view of financial performance. The following examples illustrate dashboards commonly used by finance teams.

Profit and loss dashboard

This dashboard focuses on profitability and revenue performance.

Typical insights include:

Gross profit
Net profit
Profit margins
Revenue trends over time
Expense distribution

Finance leaders use this view to answer questions such as:

Is the company maintaining healthy profit margins?
Are expenses increasing faster than revenue?
Which regions or segments generate the most revenue?

Understanding profitability trends helps leadership evaluate financial performance and adjust strategy when necessary.

Budget vs. actual dashboard

Budget monitoring is one of the most common uses for financial dashboards. The following dashboard compares planned budgets with actual financial results.

Key indicators include:

Income vs. budget
Expense vs. budget
Category-level variance
Monthly financial trends

By identifying deviations early, finance teams can respond quickly and maintain better cost control.

Financial management dashboard

Finance leaders often require a broader view of financial performance across multiple indicators. A financial management dashboard typically includes:

Revenue performance
Accounts receivable
Accounts payable
Profit margins
Financial ratios such as equity ratio or debt-to-equity

This type of dashboard provides a high-level overview of the organization’s financial condition.

Balance sheet dashboard

Balance sheet dashboards focus on financial stability and long-term financial health. Common metrics include:

Current ratio
Quick ratio
Working capital
Debt ratios
Cash balances

These indicators help finance leaders assess liquidity and understand whether the organization maintains a healthy financial structure.

Cash flow dashboard

Cash-flow management is critical for financial stability. A cash-flow dashboard provides insights such as:

Cash inflow and outflow
Accounts receivable aging
Payable obligations
Burn rate
Projected cash balance

Monitoring these indicators helps finance teams prevent liquidity issues and maintain operational stability.

The above dashboards are built in Bold BI® by Syncfusion®. Bold BI helps finance teams consolidate financial data from multiple systems, such as ERP platforms, accounting software, and operational databases. Instead of exporting spreadsheets and manually combining reports, CFOs can view financial performance in real time through centralized dashboards.

How Bold BI helps build a powerful CFO dashboard

Modern analytics platforms like Bold BI simplify creating a CFO dashboard by connecting to multiple financial data sources and transforming them into interactive visualizations.

With Bold BI, organizations can:

Connect to databases, ERP systems, and cloud applications to unify financial data from multiple sources.
Build real-time financial dashboards to monitor key metrics such as revenue, expenses, cash flow, and ROI.
Schedule dashboard updates and automated reports to ensure stakeholders always have access to the latest financial insights.
Collaborate on dashboards and reports so finance teams and leadership can analyze performance together.
Share insights across teams with secure access controls, ensuring the right people see the right data.
Embed dashboards and analytics into internal applications or portals for seamless access to financial insights within existing workflows.
Ask questions in natural language and receive instant visual and textual insights using its AI features. This enables users to explore financial data quickly without writing complex queries.
White-label dashboards by customizing branding elements such as logos, themes, and UI styles, so analytics look and feel like a native part of your product or organization.
Configure data refresh schedules (and refresh on demand) so dashboards always reflect the most current financial data.
Enable drill-down analysis to move from high-level KPIs into underlying details.

By leveraging powerful data visualization and analytics capabilities, Bold BI helps finance teams gain deeper insights into financial performance, identify risks earlier, and make more confident, data-driven decisions.

Explore our ‘’10 Best Practices for Designing Powerful Dashboards” and “How to Create a Dashboard in 5 Minutes” blogs to learn how to build a complete CFO dashboard with Bold BI.

Key takeaways

Financial decisions rely on accurate, timely data. CFO dashboards bring together revenue, expenses, budgets, and cash-flow insights in one place, helping finance leaders detect risks earlier and improve financial performance. With tools like Bold BI®, organizations can move beyond manual reporting and gain real-time financial visibility that supports faster, more confident decisions.

To deepen your understanding of financial dashboards and the key metrics that CFOs monitor, explore our financial dashboard pages for additional examples and insights. Start a free trial to build your own CFO dashboard using your financial data and turn insights into strategic decisions.

Frequently asked questions

What is a CFO dashboard?

A CFO dashboard is a financial analytics dashboard that consolidates key metrics such as revenue, profitability, cash flow, and financial risk indicators into a single visual interface.
How do CFO dashboards improve financial decision-making?

They provide real-time insights into financial performance, helping finance leaders detect trends, identify risks, and make faster strategic decisions.
Why are dashboards better than spreadsheets for finance reporting?

Dashboards automatically update data, reduce manual errors, and present financial information visually, making trends easier to interpret.

Choosing the Right BI Deployment Model: Cloud vs On-Premises

Bold BI by Syncfusion — Thu, 26 Feb 2026 14:21:04 +0000

TL;DR: Struggling to choose between cloud and on-premises deployments? Learn when to choose cloud deployment or on-premises deployment in this blog to align your BI strategy with your organization’s infrastructure, security, compliance, and scalability needs.

Introduction

In today’s data-driven world, teams evaluating BI platforms often struggle to decide where to deploy their solution, balancing cost, scalability, speed, security, compliance, and infrastructure control while planning for long-term growth. Bold BI® addresses this by offering both cloud and on-premises (self-hosted) deployments, giving organizations flexibility to align with security policies, compliance requirements, and IT capabilities. Whether you want the agility of cloud infrastructure or the control of running BI on your own server, Bold BI supports both models.

In this blog, we’ll compare cloud and on-premises deployment, outline key advantages and limitations, and help you choose the best fit for your organization’s technical and business needs.

Who this guide is for

This guide is for technical teams selecting, deploying, and managing BI platforms, especially when security, compliance, and scalability matter. It is particularly helpful for:

IT managers responsible for governance and availability.

DevOps engineers managing deployment and scaling.

BI and data architects designing analytics environments.

With that context, the next step is to define deployment model and explain why it affects security, compliance, cost, and operational effort.

Understanding Bold BI deployment models

Bold BI allows you to deploy your analytics platform in the cloud for faster rollout and easier scaling, or on-premises for maximum control over data, security, and infrastructure, depending on your organization’s requirements. Let’s explore each deployment model in detail to better understand the benefits, limitations, and ideal use cases.

Cloud deployment

Cloud deployment is a hosted option where the BI environment runs in public cloud infrastructure. Bold BI manages the hosted environment, while your team configures data connections, access controls, and governance based on your requirements.

Key advantages

Quick setup with reduced maintenance: Get started faster and provide access to dashboards for authorized users, subject to your network and security configurations.

Faster dashboard creation and timely insights: Build dashboards efficiently, connect to many data sources, and support near‑real‑time analytics depending on your data pipeline and refresh configuration.

Enterprise-oriented capabilities: Options may include multitenancy, branding, embedding, and controls that can help support common compliance needs.

Recommended if

You need a fast setup with minimal infrastructure lead time.

You want a solution that grows with your business, especially if workloads are unpredictable or expected to change frequently.

You prefer less IT work instead of managing servers, updates, and maintenance.

Your team requires access from anywhere to support remote or distributed users.

Next, let’s explore on-premises deployment.

On-premises (self-hosted) deployment

On-premises deployment allows you to run Bold BI within the infrastructure you manage, providing greater control over data location, security configuration, and operational policies. It can be deployed on your Windows or Linux environments, you can use Docker or Kubernetes, or even host it on cloud virtual machines such as AWS, Azure, or GCP.

Key advantages

Internal deployment without vendor-hosted infrastructure: Run the platform inside your network boundary, with optional integrations based on your architecture.

Full data ownership and flexibility: Deploy on your own servers for complete control, customization, and deployment flexibility.

Security and access control: Options may include role-based access control, SSO integration, and encryption features to help meet your security policies.

End-to-end analytics capabilities: Connect data, prepare it with built-in ETL, explore insights with AI, and easily export or embed dashboards.

Recommended if

You require full control over deployment and hosting.

You must align with internal governance or regulatory standards.

Your organization has dedicated IT and DevOps capacity.

You prefer managing update cycles internally.

To learn more about these Bold BI deployment options that give you flexibility to meet security, compliance, and infrastructure needs, refer to our documentation.

To make the trade-offs easier to evaluate quickly, here’s a side-by-side comparison of cloud and on-premises deployment.

Cloud BI vs On‑premises BI: Side-by-side comparison

The following comparison table highlights the key differences between cloud and on-premises deployments to help you choose the right model for your organization’s needs.

Criteria	Cloud deployment	On‑premises deployment
Where it runs	Hosted in public cloud infrastructure	Internal servers or organization‑owned data centers
Control level	Low to moderate	Highest
Security and compliance	Strong cloud security but dependent on provider controls	Greater control over data residency and security configuration
Scalability	Very high elasticity and auto scaling	Based on available infrastructure capacity
Infrastructure management	Managed by the hosting environment	Managed by your internal team
Upgrade control	Typically provider‑managed	You control patching and version upgrades

If you are still deciding, the checklist below walks through the key questions in the same order that most IT and DevOps teams evaluate them.

How to choose the right deployment model

Use the criteria below to narrow down the best deployment model based on your environment and constraints.

Data governance requirements: Choose the model that gives you the level of control you need over data, security policies, and compliance obligations.

Compare upfront investment vs ongoing spend: Decide whether a lower initial cost or investing in your own infrastructure better fits your budget strategy.

Infrastructure management: Select an approach based on whether you want faster rollout with less infrastructure management or full internal configuration and oversight.

Scalability expectations: Pick the option that aligns with how you expect to scale, either flexing quickly with demand or expanding based on available capacity.

Decide how updates should happen: Choose between an approach with provider-managed updates or one where you control the timing of patches and upgrades.

Here is a quick summary of what to remember.

Key takeaways

Your deployment model directly shapes outcomes like security boundaries, compliance readiness, performance consistency, operational effort, and long-term cost control. Bold BI® supports both cloud and on-premises deployments, so you can align analytics with your broader infrastructure strategy: cloud emphasizes speed and elasticity, while on-premises prioritizes control and governance. To select the right Bold BI model, evaluate your business goals, in-house technical capacity, and compliance requirements, then choose the approach that supports both today’s needs and future growth.

Sign up for a free trial to evaluate Bold BI in your environment, explore pricing to find the plan that matches your team’s scale and infrastructure, or schedule a personalized demo to validate the best deployment for your needs. If you’d like a tailored recommendation, contact our team, and for faster setup across environments, check out our blog on Bold BI auto deployment on Windows, Linux, Kubernetes, and Docker to streamline installation, automate configuration, and accelerate deployment.

Frequently asked questions

Which deployment option should I choose?

Choose cloud if you want faster setup, elastic scaling, and less infrastructure work. Choose on-premises (self-hosted) if you need strict control over data residency, governance, or deployment behind your firewall.
Which platforms can I deploy on-premises BI on?

You can deploy on Windows, Linux, Docker, or Kubernetes, and even cloud virtual machines like AWS, Azure, or GCP.
Is Bold BI secure?

Yes, Bold BI is secure because it supports SSL encryption, role-based access controls, and enterprise-grade security measures designed to protect sensitive data.
What is Bold BI self-hosted or on-premises deployment?

Bold BI on-premises (self-hosted) deployment is the version you install on your own servers, giving you full control over your data, security, and deployment environment.
Who should use the self-hosted deployment?

The self-hosted deployment is best for teams that need strict privacy and full control over their analytics environment.

6 Red Flags Your Analytical Dashboard Needs a Refresh

Bold BI by Syncfusion — Mon, 23 Feb 2026 13:09:53 +0000

Is your analytical dashboard still providing valuable insights? A once-useful analytical dashboard can quickly become irrelevant. Don't let outdated data and ineffective visualizations hold you back. Learn how to spot the warning signs of a stale analytical dashboard with these 6 red flags.

Warning signs of a stale Analytical Dashboard

An Analytical Dashboard provides a clear picture of data in service. Having a running service-line dashboard provides insights to efficiently drive a business. However, troubleshooting your dashboard may hinder most of your operations. You will be unable to figure out the story behind your data and struggle to make your work run smoothly. This happens due to stopping or altering tools within a dashboard because they’re outdated.

These are the key signs that your dashboard is outdated:

It displays static data from the source.
It takes time to load information.
There’s a drop in dashboard functioning and AI features.
Incomplete data is displayed.
There’s a reduction in ideas generated from dashboard graphics.
Technical changes cause delayed data or errors.

1. It displays static data from the source.

When your dashboard begins to provide static data, your dashboard needs an update. You will realize this when your dashboard takes in data from the database or source but cannot process it in the moment, or when it leads to wrong analysis. Thus, resources are wasted on a particular task.

2. It takes time to load information.

The dashboard taking too much time loading data from the server indicates the need for an update. It may be one of the signs of a slow API response or dashboard connection affecting data rendering or showing errors.

3. There’s a drop in dashboard functioning and AI features.

Some of the tools for customization may drop in performance or concurrently run without any configuration to produce a single result that is hard to compare to make a decision. This affects the function of AI features that enable the dashboard to convey information, impacting its ability to report accurate data.

4. Incomplete data is displayed.

A dashboard displaying cut images or incomplete charts and graphs signifies that it needs improvement. This is because the operation has been reduced due to the developer's time frame, which means it needs an update. Sometimes the information may appear blurry, and you cannot gain any insight to apply to your business. When optimized, an update can be made to correct a specific issue or widget to help the dashboard display all the data faster at once.

Incomplete data is displayed

5. There’s a reduction in ideas generated from dashboard graphics.

Does your dashboard summarize your data, generate ideas, or provide metrics and KPIs that yield insights? If it has reduced any of those, or stopped them altogether, you need to upgrade the dashboard to visualize correct data.

6. Technical changes cause delayed data or errors.

Technical changes are key to maintaining and improving dashboard functionality for rendering and displaying data in widgets. But when such changes are done, the dashboard tends to respond slowly, or an error occurs in the process of fetching data from the source. This signifies the need for a refresh to update changes swiftly.

Worried about what to do next? We have you covered. Have all your data at your fingertips, including real-time data, with a timely update of your dashboard. Run through the following information for the solution to your business dashboards!

Benefits of updating your analytics dashboard

With an updated dashboard, you will enjoy the following benefits:

1. Improved decision-making

When you have new features in your dashboard, you will gain more interactive features and ideas to help you understand the conveyed data and make decisions accordingly for the betterment of the business.

2. Time management

In an updated dashboard, you will save time when loading or designing it because its speed will increase. Also, connecting to other data sources, like SQL, and collaborating with other teams will achieve each goal faster.

3. Increase accurate, actionable analytics

With the new updates, data from the user database will be fully displayed as expected. A user will be able to visualize information keenly and do proper analysis to accurately perform data-driven choice of actions.

4. Advance data evaluation

An updated dashboard keeps you informed of your data to help you evaluate it before putting it into practice. This helps select compatible information to complete each task seamlessly.

5. Provision of meaningful reports

An updated dashboard provides data ready for application to your business. You can simply select a piece of detailed information from your dashboard to develop your business and keep a steadily progressing graph for your productivity.

How to improve and keep a dashboard up to date

First, identify the problem, evaluate it, separate it, if possible, then work on it. In this process, new features will be installed.

After updating the dashboard, you need to improve its efficiency. Follow these steps:

1. Erase the widgets that had errors conveying data

Removing the problematic widgets and restarting the design process supports the data configured in and the dashboard itself. Also, clear the cache to remove all data traces to avoid risk affecting your new installation.

2. Re-examining newly designed metrics per expectation

Recheck the new design related to the need of the use case. This will help you know its performance and determine whether the change is worthwhile. Check the update to see if it is compatible with your business.

3. Schedule trial time to observe the performance of the update

Plan a period to monitor the function of the newly updated features and metrics. This will determine the implications being prevailed by the new widgets, leading to improvements to take the right actions to keep your dashboard running. Keep stats for each session and compare the variables. If the dashboard runs orderly and progressively as expected, then its goal has been achieved.

4. Observe your dashboard’s display

See whether your KPIs and metrics are readable. Can you see the actions reflected per the data rendered? If it is visible as a whole, and you can draw some hints of operation, then it is fine to interact with the data.

5. Refresh the whole dashboard to see how it's displaying information in general

When done with all the updates, refresh the whole dashboard to fully insert those updates. You will be able to see the entire dashboard display the data that will help you make the right conclusion.

Bold BI^® helps keep your dashboard updated through its data refresh feature, enabling your dashboard data to refresh automatically. It also lets you schedule refreshes for the whole dashboard or specific widgets at regular intervals.

With a free trial, you can sign up to Bold BI^® and create your own interactive business intelligence dashboards. If you already have an account, you can log in to get access to Bold BI.

DEV Community: Bold BI by Syncfusion

How to Connect Snowflake Data to BI Dashboard

Introduction

What is Snowflake?

Benefits of integrating Snowflake data into a BI platform

How to Connect Snowflake Data to Bold BI Dashboard

Step 1: Open the Bold BI dashboard Designer

Step 2: Create a new data source

Step 3: Select Snowflake connector

Step 4: Enter Snowflake connection details

Step 5: Load Snowflake Schemas in the Data Source Designer

Step 6: Select tables from Snowflake schema

Step 7: Select a view, drag and drop, and preview data

Step 8: Save the data source

Creating a dashboard using Snowflake data in Bold BI

Real-world use case of visualizing snowflake data

Warehouse cost optimization

Workload analysis by query type

User activity and data adoption tracking

Scheduling Snowflake data refresh in Bold BI

Collaborating and sharing Snowflake insights in Bold BI

Step 1: Open the dashboards listing page

Step 2: Review the dashboard options

Step 3: Add users or groups

Step 4: Apply sharing permissions

Transform Snowflake data into business insights

Frequently asked questions

Does Bold BI support real-time Snowflake data?

Can I use Snowflake tables and custom SQL?

How is performance handled for large Snowflake datasets?

Does chart switching work in embedded mode?

Is Snowflake data secure in Bold BI?

Top 5 Use Cases for Embedded Analytics for SaaS in 2026

Introduction

What is embedded analytics for SaaS?

Why does embedded analytics for SaaS matter?

Top 5 use cases for embedded analytics for SaaS to know in 2026

1. Real-time executive dashboards

2. Embedded analytics inside workflows

3. Customer-facing analytics

4. Real-time alerts and exception monitoring

5. Self-service exploration for power users

What to look for in an embedded analytics platform for SaaS

Ready to embed analytics in your SaaS Product?

Frequently asked questions

What are embedded analytics tools?

Are embedded analytics tools only relevant for SaaS?

How do embedded analytics tools differ from traditional BI platforms?

What should teams prioritize when choosing an embedded analytics tool?

What are embedded analytics tools used for?

12 Key Twilio Dashboard Metrics and KPIs You Should Track

Introduction

Who this guide is for

What are Twilio dashboard metrics and KPIs?

Why track Twilio metrics and KPIs?

12 Twilio dashboard metrics and KPIs to track

Average call duration

Total call duration

Total calls

Cost per call

Cost per message

Total cost of messages

Total messages

Total cost of calls

Messages by delivery status

Calls by completion status

Messages by date

Calls by date

Tips for tracking Twilio KPIs effectively

Build an interactive Twilio dashboard with Bold BI

Frequently asked questions

Can Bold BI display real-time Twilio data?

Can we segment data by carrier, region, or channel?

How does this dashboard help reduce failures?

Can we analyze long-term performance trends?

Is Twilio data secure in Bold BI?

Bold BI Support and Service Level Agreement in 2026

A more structured support model

Clearer and more structured onboarding

Improved hosting availability for dedicated cloud deployments