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Top 26 Sales Dashboard Examples for Data-Driven Sales Teams

Bold BI by Syncfusion — Mon, 03 Aug 2026 14:30:03 +0000

Introduction

Take a scenario where a VP of Sales walks into a forecast review with the board. She's asked how confident she is in this quarter's number. The honest answer is “not very” because the pipeline data in the CRM doesn't match what her regional managers reported last week, and nobody's had time to reconcile the two. She gives a number anyway. Three weeks later, it's wrong.

This is a familiar problem, and it's rarely about a lack of data. Sales data is often scattered across CRM systems, spreadsheets, and other business applications, making it difficult to get a complete, trustworthy picture of performance. That fragmentation is what leads to slower decisions, inaccurate forecasts, and missed revenue. It is not a shortage of numbers, but a shortage of numbers anyone fully trusts.

A sales dashboard solves this by consolidating sales data, KPIs, and performance metrics into one place, updated as new data comes in. It helps teams track revenue, monitor the pipeline, measure conversions, and check progress toward sales goals all from the same view, with everyone looking at the same numbers.

This article covers the essential sales KPIs every team should monitor and 26 examples spanning sales analytics dashboards for pipeline, revenue, subscriptions, and e-commerce that can help sharpen sales performance and pipeline management.

What is a Sales Dashboard?

A sales dashboard is a visual interface that pulls revenue, pipeline, and quota data from your CRM into a single view so sales teams can track performance without building spreadsheets or waiting on manual updates. Instead of digging through spreadsheets, you open one view and see:

How much revenue closed this month vs. target.
Where deals are stuck in the pipeline.
Which reps or regions are ahead or behind quota.
Which deals are at risk of slipping.

Sales dashboards update in real time, so the number on the screen is the same number everyone else on the team is looking at.

Spreadsheets vs. Sales Dashboards

These terms are often used interchangeably, but they refer to solutions for different problems. Choosing the wrong one can mean decisions made on stale or inconsistent numbers. Here's a quick breakdown:

Feature	Spreadsheets	Sales dashboards
How you find an answer	Pull data from the CRM, rebuild in a spreadsheet	Open one live view
Data freshness	As current as the last export	Real time
Who can access it	Usually one person maintains it	Anyone with role-based access
Risk of error	High, due to manual copy and paste actions and version drift	Low due to acting as a single source of truth
Best for	One-off analysis	Ongoing tracking and forecasting

These aren't mutually exclusive, most teams still pull one-off analyses by hand from time to time. But the moment a number needs to be trusted by more than one person, a dashboard is doing a job a spreadsheet can't.

Why Sales Dashboards Matter

The value shows up differently by role. Leaders close the gap between what's happening and what can be proven to the board, reps spend less time updating spreadsheets and more time selling, and analysts get their time back from routine "where do we stand" questions to focus on forecasting and root-cause analysis. Before looking at dashboards in action, it helps to know which metrics they're built to track.

Essential Sales KPIs to Monitor

Tracking the right sales KPIs is critical for understanding performance and driving consistent growth. These metrics provide a clear view of revenue trends, pipeline health, and team effectiveness, enabling sales teams to make informed, data-driven decisions and improve overall outcomes.

Revenue Performance

This tracks total revenue and growth trends over time. It helps teams evaluate overall business health, compare results against targets, and identify opportunities to drive consistent revenue growth.

Pipeline Stage

This shows how opportunities are distributed across each sales stage while comparing deal volume with the average time spent. This helps teams identify bottlenecks, track deal progression, and optimize stage-wise performance for faster conversions.

Conversion Rate

This measures how effectively leads or opportunities are converted into customers. It helps teams identify bottlenecks in the sales funnel and improve targeting, qualification, and closing strategies.

Average Deal Size

This reflects the typical value of closed deals. It helps sales teams understand revenue patterns, refine pricing or targeting strategies, and improve forecasting accuracy over time.

Customer Retention Rate

This measures the percentage of customers a business retains over a specific period. It helps evaluate customer loyalty, identify churn trends, and assess long-term growth and satisfaction.

Customer Acquisition Cost

This measures the cost required to acquire a new customer. It helps teams evaluate efficiency in sales and marketing efforts and ensures acquisition strategies remain cost-effective.

Customer Lifetime Value

This estimates the total revenue a customer generates over their entire relationship. It helps businesses focus on high-value customers and optimize long-term sales and retention strategies.

Forecasted Revenue

This estimates future sales based on pipeline and historical data. It helps organizations plan resources, set realistic expectations, and improve decision-making with more reliable projections.

With these KPIs in mind, here's what they look like in practice, across 26 real sales dashboard examples.

26 Sales Analytics Dashboard Examples to Try Out in 2026

Sales dashboards should support easy sharing and collaboration across teams. Features such as export options, embedding, and role-based access help stakeholders stay aligned and make informed decisions together.

The following is a quick reference comparison table for your reference:

Dashboard Example	Platform or CRM	Primary Use case	Key Metrics Tracked
CRM & Sales Platforms
Salesforce Opportunity Dashboard	Salesforce	Opportunity pipeline management	Pipeline value, stage distribution, and forecast accuracy
HubSpot Deals Report	HubSpot	Deal pipeline tracking	Stage conversion, deal velocity, and revenue
Pipedrive Sales KPI Dashboard	Pipedrive	Sales KPI monitoring	Activity-to-deal ratio, win rate
Pipedrive Sales Activities Dashboard	Pipedrive	Sales activity tracking	Calls, meetings, and activities per rep
Freshsales Pipeline Performance Dashboard	Freshsales	Pipeline performance	Stage conversion and deal progression
Insightly Opportunities Analysis Dashboard	Insightly	Opportunity analysis	Win/loss rate and sales by rep
Copper Sales Opportunities Dashboard	Copper	Pipeline management	Stage dwell time and pipeline value
PipelineCRM Deals Analysis Dashboard	PipelineCRM	Lead source analysis	Lead source ROI and close rate
Keap Revenue Analysis Dashboard	Keap	Revenue and customer management	Payments, refunds, and subscription revenue
ActiveCampaign Deals Performance Dashboard	ActiveCampaign	Sales automation performance	Deal velocity and win probability
Zendesk Sell Sales Performance Dashboard	Zendesk Sell	Sales performance tracking	Revenue, win rate, and forecast accuracy
Microsoft Dynamics CRM
Sales Leads Tracking Dashboard	Microsoft Dynamics CRM	Lead management	Lead-to-revenue and campaign ROI
Sales Opportunities Dashboard	Microsoft Dynamics CRM	Opportunity forecasting	Pipeline value and projected vs. actual revenue
Sales Manager Summary Dashboard	Microsoft Dynamics CRM	Team performance	Quota attainment and sales by rep
Sales & Revenue Analytics
Sales Analysis Dashboard	Multisource	Revenue analysis	Revenue by region and channel performance
Sales Performance Dashboard	Multisource	Overall sales performance	Revenue trends and product performance
Sales Activity Tracker Dashboard	Any CRM	Sales activity monitoring	Activities, commissions, and productivity
SingleStore Sales Funnel Analytics Dashboard	SingleStore	Sales funnel analysis	Lead-to-close rate and funnel conversion
Subscription & Billing
Stripe Subscription Performance Dashboard	Stripe	Subscription analytics	MRR, ARR, and churn rate
Chargebee Subscription Overview Dashboard	Chargebee	Subscription billing	Revenue per account and renewals
Customer & E-commerce
Customer Profile Overview Dashboard	Any CRM	Customer insights	Customer lifetime value and segmentation
Shopify Product Monitoring Dashboard	Shopify	Inventory monitoring	Stock levels and stock-to-demand ratio
Keen E-commerce Sales Analysis Dashboard	Keen	E-commerce performance	Conversion rate and cart abandonment
Ticket Sales Analysis Dashboard	Ticketing platforms	Event sales	Revenue per event and ticket sales
Finance & Operations
Financial Analysis Dashboard	Finance systems	Financial performance	Profit margin, expenses, and profitability
Northwind Products & Suppliers Dashboard	ERP systems	Inventory and supplier management	Inventory levels and fulfillment rate

See these KPIs live before you keep reading. Bold BI® connects directly to your CRM and builds a sales KPI dashboard in minutes. Start a free trial today.

Salesforce Opportunity Dashboard

The Salesforce Opportunity Dashboard gives sales teams greater visibility into open opportunities, pipeline value, and deal outcomes throughout the sales cycle. Built directly on Salesforce CRM data, it makes it easier to identify high-value opportunities, evaluate lead sources by campaign, and monitor conversion performance without leaving the tools reps already use daily.

HubSpot Deals Report Dashboard

The HubSpot Deals Report Dashboard focuses on analyzing deal outcomes, conversion performance, and future revenue opportunities. Pulling directly from HubSpot's CRM pipeline stages, sales teams can use it to understand how deals move from first contact to close, identify the most effective acquisition channels, and track progress against revenue goals in the same system where deals are actually managed.

Pipedrive Sales KPI Dashboard

The Pipedrive Sales KPI Dashboard allows sales leaders to monitor the metrics that directly impact revenue and deal performance. Built on Pipedrive's activity-based pipeline data, teams can quickly evaluate sales efficiency, track deal outcomes, and identify areas for improvement without switching out of the CRM reps use for daily prospecting.

Pipedrive Sales Activities Dashboard

The Pipedrive Sales Activities Dashboard helps sales teams monitor activities, opportunity progress, and conversion performance throughout the sales cycle. It highlights how sales representatives engage with prospects, making it easier to measure productivity and identify opportunities to improve deal outcomes.

Freshsales Pipeline Performance Dashboard

The Freshsales Pipeline Performance Dashboard gives sales teams a live read on deal progression, conversion efficiency, and revenue potential across the pipeline. It provides a complete view of pipeline health, helping managers identify high-value opportunities and improve forecasting accuracy.

Insightly Opportunities Analysis Dashboard

The Insightly Opportunities Analysis Dashboard focuses on evaluating opportunity outcomes, sales pipeline health, and representative performance. By analyzing won, lost, and active opportunities, sales teams can identify successful sales strategies, uncover common deal blockers, and improve conversion rates.

Copper Sales Opportunities Dashboard

The Copper Sales Opportunities Dashboard focuses on measuring opportunity performance, deal progression, and pipeline efficiency. Sales teams can use it to monitor conversion metrics, evaluate opportunity status, and identify stages that may be slowing down the sales process.

PipelineCRM Deals Analysis Dashboard

The PipelineCRM Deals Analysis Dashboard focuses on measuring deal performance, tracking pipeline progression, and evaluating revenue potential across different sales stages. It gives sales teams the insights needed to monitor conversion trends, identify high-performing lead sources, and improve forecast accuracy.

Revenue Analysis Dashboard

The Revenue Analysis Dashboard helps businesses monitor revenue trends, payment collection performance, refunds, and subscription growth. It merges key revenue metrics, enabling teams to identify cash flow patterns, track payment statuses, and forecast future revenue with greater confidence.

ActiveCampaign Deals Performance Dashboard

The ActiveCampaign Deals Dashboard is designed to help sales teams evaluate deal performance, monitor pipeline activity, and measure conversion success. By tracking deal progression and win probability, teams can identify high-potential opportunities and improve overall sales efficiency.

Zendesk Sales Performance Dashboard

Sales managers rely on this dashboard to monitor deal progression, revenue performance, and pipeline health from a single view. It combines deal metrics, sales forecasts, and source analysis for a comprehensive read on sales performance.

Using Salesforce, HubSpot, Pipedrive, or any other integrations discussed here? Bold BI has prebuilt connectors for all of them. See how it connects to your CRM data in real time.

Sales Leads Tracking Dashboard

Lead performance and campaign effectiveness are the focus of this Microsoft Dynamics CRM Sales Leads Tracking Dashboard, built for teams prioritizing high-value accounts. It tracks revenue opportunities throughout the sales cycle and highlights key lead management metrics, making it easier to evaluate conversion performance.

Sales Opportunities Dashboard

The Microsoft Dynamics CRM Sales Opportunities Dashboard brings together opportunity performance, customer revenue potential, and forecasting metrics to support more informed sales decisions. Sales managers can use it to monitor deal progress, compare projected and actual revenue, and identify opportunities that have the greatest impact on business growth.

Sales Manager Summary Dashboard

The Microsoft Dynamics CRM Sales Manager Summary Dashboard gives sales managers a high-level view of pipeline performance, revenue trends, and team contributions. It highlights key sales metrics and revenue opportunities, helping managers evaluate progress toward targets and identify areas that require attention.

Sales Analysis Dashboard

The Sales Analysis Dashboard brings revenue growth, customer acquisition, and product performance together in one operational view. It also tracks sales channel effectiveness, helping teams identify top-performing markets, customers, and products while improving forecasting accuracy.

Sales Performance Dashboard

The Sales Performance Dashboard helps organizations measure revenue growth, customer retention, product performance, and sales trends across multiple channels. Built as a channel-agnostic view rather than tied to one CRM, it brings together key sales metrics that enable teams to evaluate performance, identify top-selling products, and understand customer purchasing behavior across every sales channel side by side.

Sales Activity Tracker Dashboard

The Sales Activity Tracker Dashboard focuses on monitoring ticket sales, event performance, buyer activity, and commission earnings. It helps organizations understand which events generate the most revenue, identify high-performing locations, and track ticket sales performance across categories.

SingleStore Sales Funnel Analytics Dashboard

The SingleStore Sales Funnel Analytics Dashboard helps sales teams track lead progression, opportunity conversion, and overall funnel performance. By visualizing the entire sales journey, the dashboard enables teams to identify high-performing lead sources, optimize conversion rates, and improve revenue generation.

Stripe Subscription Performance Dashboard

The Stripe Subscription Performance Dashboard helps subscription-based businesses monitor recurring revenue, customer retention, and subscription growth. The dashboard shows subscription health at a glance, helping teams spot churn risks, measure customer value, and forecast recurring revenue more accurately.

Chargebee Subscription Overview Dashboard

Billing performance and customer value are the focus of the Chargebee Subscription Overview Dashboard, built for teams managing recurring billing at scale. By tracking revenue across customers and companies, teams can evaluate subscription growth, identify top-performing accounts, and better understand long-term revenue streams.

Customer Profile Overview Dashboard

The Customer Profile Overview Dashboard makes understanding customer behavior and purchasing patterns easier by consolidating activity and transaction data into one view. This helps sales and account management teams identify high-value customers and improve retention strategies.

Shopify Product Monitoring Dashboard

E-commerce teams running Shopify can track product performance, inventory, and customer demand from this single dashboard view. By combining sales and inventory metrics, it helps identify best-selling products, optimize stock levels, and reduce the risk of overstocking or stockouts.

Keen E-Commerce Sales Analysis Dashboard

Purchasing behavior, site engagement, and product performance connect in this Keen E-Commerce Sales Analysis Dashboard for online retailers. By linking sales, traffic, and cart activity data, it helps teams spot where conversions are dropping off and where revenue opportunities are being missed.

Ticket Sales Analysis Dashboard

Ticket demand, revenue generation, and event performance come together in this dashboard, spanning channels and regions. By bringing together ticket sales data, teams can evaluate marketing effectiveness, compare event performance, and optimize sales strategies.

Financial Analysis Dashboard

Revenue, profitability, and cash flow sit in one view with the Financial Analysis Dashboard. It incorporates financial and sales metrics to help teams evaluate business performance, identify profitable products and sales channels, and make data-driven decisions.

Northwind Products and Suppliers Dashboard

Supplier performance, product category sales, and operational efficiency come together in the Northwind Products and Suppliers Dashboard. It blends sales, fulfillment, and supplier metrics to help businesses optimize inventory planning, improve supplier relationships, and enhance customer satisfaction.

Visit our website demo page to view all the key metrics and KPIs used in each of these dashboards in details.

How to Choose the Right Sales Dashboard for Your CRM

With 26 examples to choose from, start narrowing by two questions:

1. What CRM or platform are you already using?

Match your existing stack first. A dashboard built for Salesforce data won't map cleanly onto HubSpot fields, and vice versa. Use the comparison table above to jump straight to your platform.

2. Who's the primary audience?

Executives and VPs: Revenue, forecast accuracy, and margins for examples Copper Sales Opportunities Dashboard and PipelineCRM Deals Analysis Dashboard.
Sales managers: Team quota attainment, pipeline coverage, rep performance for example Sales Opportunities Dashboard and Sales Manager Summary Dashboard.
Individual reps: Activity tracking, deal velocity, and personal quota for example Sales Leads Tracking Dashboard and SingleStore Sales Funnel Analytics Dashboard.
RevOps/analysts: Funnel conversion, lead source ROI, forecasting models for example Sales Performance Dashboard and Pipedrive Sales Activities Dashboard.

Once you know your platform and audience, you can adapt the matching template rather than building from a blank canvas. Most of the examples above can be recreated in Bold BI in under 30 minutes using our prebuilt connectors.

How Bold BI Brings it All Together

Bold BI® turns scattered sales data into a single source of truth your team can actually act on, connecting to CRMs, ERPs, cloud apps, and databases out of the box so nothing stays siloed.

Self-service exploration: Built-in filters, drill downs, and role-based dashboards let reps, managers, and executives dig into the data themselves, without waiting on IT.
Unified AI Agent: Ask a direct question like "which region is behind quota this month?" and get an instant answer, with smart narrations explaining what a trend means in plain language.
Mobile-ready access: Field and remote sales teams stay connected to real-time numbers from anywhere.
Embeddable dashboards: Live numbers live directly inside the CRMs or portals your team already uses daily.
Enterprise-grade security: Role-based access control, single sign-on, and data-level security protect sensitive pipeline and revenue data without adding friction.

Ready to turn your scattered sales data into one number everyone trusts? Start a free trial of Bold BI and build your first sales dashboard in minutes. Prefer to look before you sign up? Explore our sample page or schedule a demo to see how it fits into your existing sales stack.

Frequently Asked Questions

1. What is a sales dashboard?
  A sales dashboard is a visual tool that consolidates sales data into a single interface, helping teams track key metrics like revenue, pipeline value, and conversion rates in real time.
2. Why are sales dashboards important for sales teams?
  Sales dashboards provide real-time visibility into performance, improve forecasting accuracy, and enable faster, data-driven decision-making so your team can identify opportunities and optimize strategies effectively.
3. What KPIs should be included in a sales dashboard?
  Common KPIs include revenue, pipeline value, win rate, average deal size, sales cycle length, quota attainment, and conversion rate, as they provide a complete view of sales performance.
4. How do sales dashboards improve forecasting accuracy?
  Sales dashboards use real-time and historical data to analyze pipeline trends, helping sales teams generate more accurate revenue forecasts and make better strategic decisions.
5. What are the benefits of using real-time sales dashboards?
  Real-time dashboards enable instant access to data, helping teams respond quickly to changes, identify bottlenecks, and make timely decisions without relying on outdated reports.
6. How do sales dashboards differ from reports and spreadsheets?
  Unlike static reports and spreadsheets, sales dashboards are interactive and continuously updated, allowing users to explore data dynamically and gain actionable insights faster.
7. How can Bold BI help create sales dashboards?
  Bold BI enables organizations to build interactive, real-time dashboards with seamless data integration, role-based access, and advanced visualizations, making it easier to track KPIs and drive sales performance.
8. How do I choose the right sales dashboard example for my CRM?
  Start with the dashboard example built for the CRM or platform your team already uses, such as Salesforce, HubSpot, or Pipedrive. This ensures the KPIs and data fields match what's already being tracked, so the dashboard can be adapted quickly instead of built from scratch.
9. Are these sales dashboard templates free to use?
  Many of the dashboard examples in this list are available as prebuilt templates that can be customized to fit your data and branding. Availability may vary by platform, so check the specific dashboard's sample page for template access.

AI Credits in Bold BI: How to Purchase, Manage & Track Usage

Bold BI by Syncfusion — Wed, 29 Jul 2026 09:01:29 +0000

TL;DR: Bold BI's AI credit management system helps organizations monitor, control, and optimize their AI usage from a centralized platform. By tracking credit consumption across AI features such as the AI Assistant, dashboard summaries, and the Q&A widget, teams gain better visibility into their budgets, strengthen governance, and scale AI adoption with confidence.

Introduction

You're mid-conversation with the AI Assistant, three questions deep into understanding a revenue dip, when the responses suddenly stop coming. The culprit: your organization's AI credit balance has been exhausted, and nobody noticed until the moment the team needed AI the most.

This is one of the more common challenges with metered AI services. When the credit balance runs out, AI-powered features such as AI assistant, dashboard summaries, and Q&A widgets can’t process new requests until additional credits are purchased.

Bold BI® runs its AI capabilities on a straightforward credit-based system designed to make AI usage measurable, controllable, and scalable. This guide explains how Bold BI AI credits work, where they're consumed, how to purchase additional credits, and how to monitor usage so your organization can avoid unexpected interruptions.

Why manual AI usage tracking doesn't scale

Manually tracking AI feature usage across an organization is harder than it looks, and the problems tend to surface only after they've already cost time or money. Organizations commonly face the following challenges:

Unpredictable costs: Without a structured consumption model, teams will run through their AI limits as fast as possible, resulting in surprise charges that will have finance teams scrambling.
Risk of runaway consumption: Resource-intensive AI workflows, such as large dashboard summarizations or frequent assistant interactions, can consume credits quickly if usage isn't monitored.
Limited governance and compliance tracking: Organizations that need accountability around AI adoption often require visibility into who is using AI features, how frequently the features are being used, and where credits are being consumed.
Reactive scaling: Without historical usage data, organizations typically make purchasing decisions only after credits have already run low, creating unnecessary interruptions.

These challenges make it harder to scale AI adoption responsibly. Bold BI's AI credit system addresses this by making AI consumption visible, measurable, and easier to manage.

Understanding AI credits in Bold BI

AI credits are the units Bold BI uses to measure consumption of its AI-powered features. Every dashboard summary, natural language Q&A query, and AI Assistant interaction deducts a specific number of credits from your balance automatically.

It’s easy to manage the system once you understand how it works:

License-level tracking: Credits belong to the Bold BI license, not to individual users, and can't be transferred or shared across separate licenses.

Example: If your sales and marketing team operate on separate Bold BI licenses, unused credits from one license cannot be moved to the other.

Administrator-controlled access: All users can view the available AI credit balance. However, only the license owner or an authorized administrator can purchase additional credits and review the purchase history.

Example: A regular user can check the available AI credits before running a large summarization task but must contact an administrator if additional credits are required.

Variable consumption: Credit usage scales with prompt complexity and input size.

Example: Asking "What were last month's top products?" typically consumes fewer credits than generating a comprehensive quarterly summary spanning multiple dashboards and regions.

Want to understand how AI Assistant usage affects credit consumption and billing? Read our guide: How AI Assistant Data Consumption Impacts Your Billing.

Where AI credits are typically used

Credits are drawn from the same license balance regardless of which feature triggers the action:

Asking the AI Assistant a direct question about performance or trends.
Generating a plain-language dashboard summary for a stakeholder review.
Running the Q&A widget embedded in a live dashboard.
Scheduling a recurring AI-generated summary for leadership analytics.
Using AI Copilot to autogenerate a visualization from a prompt.
Requesting widget insights about an individual chart or KPI.

Each of these uses a different number of credits, which is why it’s easy to misjudge how many your organization needs without checking usage regularly.

How many AI credits will you need?

AI credit consumption depends on several factors:

Frequency of AI Assistant queries.
Dashboard summary usage.
Q&A widget activity.
Dataset complexity.
Volume and complexity of AI interactions.

In general, simpler AI interactions consume fewer credits than larger analytical workloads.

Bold BI’s AI Usage Dashboard provides historical consumption data that helps administrators estimate future usage and choose the most appropriate subscription plan.

Example

Here's what a single credit-consuming action looks like in practice:

Scenario: A sales manager needs to understand why regional orders dropped last week, right before a leadership check-in.
The action: Instead of digging through multiple dashboards, the manager asks the AI Assistant directly: "Why did Northeast orders drop last week?"
What happens behind the scenes: The request automatically deducts AI credits from the license balance. Because this is a focused question, credit consumption is relatively low compared to a larger request, such as creating a full executive dashboard summary spanning multiple business units.
The result: The manager receives an answer within seconds, while the AI credit balance updates automatically to reflect consumption.

Over time, hundreds of similar interactions can create highly variable usage patterns, which explains why two organizations with the same credit allocation may consume credits at very different rates.

AI credit plans and purchasing options

Bold BI offers flexible AI credit options to support different usage needs, from a free monthly allowance for getting started to subscription plans and add-on credits for scaling AI adoption across teams.

Free plan: 500 credits every month

Every active Bold BI license includes:

500 free AI credits per month.
Automatic monthly refresh.
No setup required.
Immediate availability.

When you activate a paid AI credit subscription, the free monthly allocation is replaced by the subscription allocation for that billing cycle.

Note: Organizations evaluating Bold BI receive 500 free AI credits. For more information, contact the Bold BI team.

Purchasing additional AI credits

Once the free AI credit allowance runs low, Bold BI gives admins two options in Settings: subscriptions plans and add-on credits.

Subscription plans

Subscription plans deliver a fixed number of credits every month for a recurring price. The higher tiers include bonus credits.

Plan	Monthly price	Base credits	Bonus credits	Total credits
Standard	$20	2,000	None	2,000
Enterprise	$50	5,000	500	5,500
Pro	$100	10,000	2,000	12,000

Subscriptions renew automatically against the card on file. To cancel recurring billing, contact Bold BI support. Your license drops back to the free plan at the end of the current billing cycle.

Add-on credits

Need additional credits before your next billing cycle? Add-on credits provide instant top-ups without changing your subscription.

Key details include:

Pricing: 100 credits per $1. A $5 purchase adds 500 credits. A $10 purchase adds 1,000.
No expiration: Unused add-on credits carry over indefinitely, so there's no pressure to burn them before month’s end.
Eligibility: Only customers with an active paid AI credit subscription can purchase add-on credits. Customers on the free tier cannot buy additional credits.

Customers who purchase Bold BI through the Bank Transfer payment method are classified as Direct Transaction (DT) customers. For active DT customers, subscription plan and add-on credit purchases follow a request-based process.

After a purchase request is submitted through the credit portal, the request is displayed as Pending. The Bold BI Support team contacts the customer to complete the purchase process and approve the request. Credits are added to the license wallet after the request is processed.

Free plan vs. paid plan

Here are the differences between the free and paid credit plans:

Feature	Free plan	Paid plans
Monthly credits	500	2,000–12,000, depending on your tier.
Bonus credits	No	Available with the Enterprise and Pro AI credit plans.
Add-on credits	No	Yes. $1/100 credits.
Monthly cost	Free	$20–$100/month
Best for	Occasional AI usage.	Frequent AI usage across teams.

The gap between "fine for now" and "constantly running dry" usually comes down to one thing: how often your team reaches for the AI Assistant.

AI governance and usage control

As AI adoption expands, visibility becomes increasingly important. Bold BI's AI credit management capabilities help organizations:

Monitor AI consumption across users and teams.
Identify unusual usage patterns.
Improve budget planning.
Support cost-control initiatives.
Track AI adoption trends.
Make informed credit-purchasing decisions.

By centralizing consumption data, administrators can scale AI initiatives while maintaining appropriate oversight and control.

AI credits for embedded deployments

Embedded Bold BI deployments follow the same AI credit allocation model as cloud and on-premises deployments. AI credits are allocated according to your licensed plan and are not provisioned as separate tenant-specific credit pools.

How to use AI credits in Bold BI

In Bold BI, administrators purchase AI credits through the AI and Billing settings in Bold BI. Whenever an AI feature is used, the operation’s credit cost is deducted from the balance automatically.

How to purchase AI credits

Sign in to Bold BI as an administrator.
Go to Settings > AI > Credits.
Click Buy AI Credits and then sign in to the AI credits portal using the account associated with the license.
In the AI credits purchase portal, click the settings icon in the left navigation pane, open Billing and Subscription, and then select the required subscription plan.
Complete the payment and confirm the purchase. Upon successful payment, the purchased credits are added directly to the license’s AI credit balance. This usually takes only a few minutes. The AI credits purchase flow in Bold BI

Tracking usage before it becomes a problem

Effective AI credit management means keeping an eye on consumption patterns over time. The Bold BI AI Usage Dashboard provides administrators with a centralized view of AI activity across their organization.

Using the dashboard, administrators can:

Filter usage by user.
Filter usage by site.
Analyze usage based on custom date ranges.
View total credits consumed.
Identify top AI users.
Review consumption trends.
Monitor adoption levels across teams.

AI credit usage management

These insights make it easier to forecast future requirements and ensure credits are being used effectively.

Organizations with multiple Bold BI licenses can switch between licenses from the AI credits purchase portal, and the AI Usage Dashboard automatically updates to show usage for the selected license.

Common issues and how to resolve them

Credits not reflecting after a successful purchase: Give it a few minutes for processing, and then refresh the AI Usage Dashboard. If the credits still haven’t shown up, contact Bold BI Support with your receipt, transaction reference, and license ID.
AI features unavailable because the AI credit balance ran out: An admin can purchase add-on credits or upgrade the subscription plan from Site Settings > AI > Buy AI Credits. Regular users without purchase permissions should loop in their admin rather than wait it out.
Payment failed with no retry option: Contact Bold BI Support directly with your license ID, transaction reference, and any payment details shown in the portal.

Who benefits most from Bold BI’s AI credit management feature

Bold BI’s AI credit management is designed to support a wide range of analytics and business professionals:

1. License owners and admins

Those ultimately responsible for the credit balance. With a clear grasp of how credits work, admins can:

Monitor balances proactively instead of reacting to complaints.
Time subscription upgrades to make the most of the free credits already in the wallet.
Approve add-on purchases before a shortfall interrupts a team mid-project.

2. Teams running frequent AI Assistant or Q&A sessions

Heavy, recurring usage is where the free tier runs out fastest. Understanding consumption patterns empowers these teams to:

Budget for a subscription tier that matches usage, instead of guessing.
Compare add-on costs against a subscription tier to see which works out cheaper long-term.
Keep AI features running without interruption during active work.

3. Organizations rolling out dashboard summarization at scale

Large, data-heavy summaries consume more credits per action than simple queries. Teams scaling this feature can:

Plan credit needs before a large rollout, rather than reacting once usage climbs.
Use the AI Usage Dashboard's per-user breakdown to spot outlier consumption early.
Plan Enterprise-tier or Pro-tier bonus credits into the rollout budget from day one.

Final thoughts

Manually tracking AI usage works fine until a team leans on it daily, then it quickly becomes hard to predict, and features can stop working without warning.

The AI credit management features in Bold BI® solve this by giving administrators a clear, centralized view of consumption. As AI adoption grows across teams, staying ahead of usage becomes essential to keeping features running smoothly and scaling with confidence.

For more details, explore the AI credit documentation or request a personalized demo to see how you can manage AI usage in Bold BI more effectively across your organization.

Related resources

Want to learn more about Bold BI's AI capabilities?

Explore the AI Assistant for natural language analytics.
Learn how dashboard summaries automatically generate insights.
Discover how the Q&A widget enables conversational data exploration.

Frequently asked questions

1. How are AI credits managed in Bold BI?
  AI credits are managed through centralized system that tracks AI usage, providing visibility into consumption and supporting organizational governance. AI credits are managed through centralized system that tracks AI usage, providing visibility into consumption and supporting organizational governance.
2. Who can purchase and manage AI credits?
  AI credits are managed at the license level. The license owner and authorized administrators can purchase AI credits, manage AI credit subscriptions, and monitor credit usage. Users can view the available AI credit balance.
3. What happens when an AI credit balance is exhausted?
  AI-powered features become unavailable until additional credits are added through a subscription plan or add-on purchase.
4. What happens if AI credits run out during active use?
  When the available balance is exhausted, no new AI requests are processed until credits are replenished. Organizations should monitor usage regularly to avoid interruptions.
5. How are AI credits consumed?
  
  Credit consumption depends on the feature being used, the complexity of the request, the size of the input, and the processing requirements involved.
6. Are AI credits shared across licenses?
  No. Credits are associated with a specific Bold BI license and cannot be transferred or shared between licenses. However, administrators managing multiple licenses can view usage for each license separately through the AI Usage Dashboard.
7. How can I estimate how many AI credits my team will need?
  Review factors such as user count, AI Assistant activity, dashboard summaries, and Q&A usage. The AI Usage Dashboard provides historical consumption data that can help forecast future requirements.
8. Do unused subscription credits roll over?
  No. Subscription credits are allocated monthly based on your plan and expire at the end of the billing cycle if unused. Add-on credits work differently: they carry over indefinitely with no expiration. The free 500-credit monthly allowance resets at the start of each new cycle.
9. Can I cancel or downgrade my AI credit subscription?
  Yes. To cancel recurring billing or stop future charges, contact Bold BI Support. Your subscription remains active until the end of the current billing cycle, after which your license reverts to the free AI credit plan. Plan upgrades and downgrades are not currently supported. To switch plans, you must cancel your current subscription and then subscribe to the desired plan.
10. Do embedded or tenant deployments use the same AI credit system?
  
  Yes. The AI credit system is the same across cloud, on-premises, and embedded deployments. AI credits are allocated based on your licensed AI credit subscription and are consumed when users use AI-powered features. Embedded deployments do not have separate tenant-specific credit allocations.
11. Are AI credits refundable?
  AI credit purchases and subscription adjustments are subject to Bold BI's applicable billing policies. Contact Bold BI Support for account-specific guidance.
12. Do trial licenses receive free AI credits?
  Yes. Trial licenses receive 500 free AI credits.

The Art of Retention: How Bold BI Helps Me Save Orders

Bold BI by Syncfusion — Mon, 27 Jul 2026 06:31:38 +0000

In the digital world, selling software requires earning trust. At first saving an order from being returned doesn’t seem like a big deal. It’s just a product, just a transaction. Yet it became a meaningful lesson, behind every license is a human decision, and every return is an opportunity.

What the returns means to me

Returns aren’t about undoing sales; they’re about making things right. Not every transaction ends perfectly and returns acknowledge that reality with honesty. When handled with clarity and care, they build trust. Behind the scenes, returns also impact revenue, reporting, and learning. Being part of this process means supporting integrity and continuous improvement, not just closing tickets.

Why customers return software licenses

Most software license returns aren’t due to product issues. They happen because expectations don’t align. Customers may struggle to see value or understand features. And honestly, I understand that. In a fast-paced world, if something doesn’t work quickly, we move on. While these moments can feel final, they’re often the best chance to save the order or at least preserve the relationship.

Starting with insight, not assumption

Every day, the return process begins with a look at the dashboard. This is where Syncfusion® Bold BI® stands out. It organizes data into clear insights without overwhelming details.

In seconds, I can see which tickets are open, in progress, saved, returned, or under review, and I ask, “Where does this request truly stand, and what’s still possible?”

The dashboard doesn’t just show status; it guides my focus toward conversations that can still make a difference.

Saving an order is a team effort

Once the dashboard gives clarity, teamwork begins. Saving an order is never a solo effort. A ticket marked as in progress or open is my cue to act. From there, it requires coordinating follow ups, cross team conversations, and approvals. The dashboard signals where attention is needed, but teamwork determines the outcome. Over time, I’ve learned that saved orders happen when the right people act at the right time with thoughtful decisions.

Balancing customer care with policy commitments

Empathy guides every conversation, while policy integrity anchors our approach. Each license represents a shared commitment throughout its active term, not as a restriction, but as a means to maintain fairness for all customers. Our focus shifts from reversal to retention through clarity and support. Following policy does not mean overlooking customer concerns; instead, it builds trust, clarity, and lasting relationships.

Returns as feedback, and a Life Lesson

Even when an order can’t be saved, it isn’t a failure. It’s feedback, insight into what held the customer back and where improvement is needed. This process also changed how I view my own life. How often do we try to “return” something because it feels uncomfortable, a new role, responsibility, or opportunity, before giving it time to reveal its value? Taking care of returns reminded me that not everything clicks instantly, and patience often reveals value.

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Final thoughts

Saving an order isn’t about dashboards or policies alone. It’s about understanding hesitation and responding thoughtfully. This experience reminded me that when we pause, look beyond the surface, and act with care, outcomes can change, for customers, and for ourselves.

Want to see how real-time dashboards help teams track customer journeys, identify opportunities to improve retention, and make informed decisions? Try Bold BI® free trial or book a demo to explore actionable insights, workflow visibility, and data-driven customer success strategies.

Natural Language Analytics: Instant Dashboard Answers

Bold BI by Syncfusion — Mon, 27 Jul 2026 06:28:28 +0000

TL;DR: Natural language analytics lets business users ask data questions in plain English and receive answers in the form of charts, KPIs, tables, or AI-generated explanations. This post explains how natural language analytics works, how it differs from natural language query (NLQ), where it delivers value across the business, and how Bold BI® brings conversational data exploration into existing dashboards.

Introduction

Business intelligence has evolved significantly over the years. Organizations have invested heavily in dashboards and analytics platforms to make better decisions. Despite that investment, getting a straight answer to a straight question is still often harder than it should be. A 2022 survey of 214 companies by BARC and Eckerson Group found that only about 25% of employees actively use the BI/analytics tools their organization provides, a number that has barely moved in seven years. You know the number exists somewhere in your BI tool. You just have to find the right dashboard, apply the right filter, or ask the right person.

Natural language analytics closes that gap. By pairing AI and natural language processing with a modern BI platform, it lets people ask data questions the same way they'd ask a colleague and get an answer back in seconds. You can ask a question such as, "Which region beat target this quarter?" and receive an answer without navigating menus or waiting on a dashboard.

This is becoming a core part of how modern BI platforms like Bold BI work. Here's what it is, how it works under the hood, and where it fits alongside the dashboards you already have.

What Is Natural Language Analytics?

NLA lets you explore data using conversational language instead of technical queries or manual dashboard navigation. Instead of digging through dashboards, you type or speak question directly, like:

Which products generated the highest revenue this month?
How did sales perform compared to last quarter?
What are the top reasons for customer churn?
Which marketing channel has the best conversion rate?

The platform interprets the question, pulls the relevant data, and returns the answer as a chart, a KPI card, a table, or a short written explanation, whichever format fits the question best. The point isn't to replace analysis; it's to remove the technical barrier between having a question and getting an answer, so the people who understand the business best can explore data directly.

Why Traditional BI Isn't Always Enough

Business intelligence platforms have traditionally relied on dashboards, filters, and predefined visualizations. While these tools remain essential, they can create friction when users need answers outside predefined analytics paths. Traditional BI isn't always enough because you'll have to deal with:

Learning dashboard structures: Users often need to understand how dashboards are organized before they can locate the information they need.
Navigating multiple dashboards: Answering a single business question may require moving across several dashboards, filters, and visualizations.
Dependence on analysts: Business teams frequently rely on data teams for one-off questions that aren't already represented in existing dashboards.
Limited self-service exploration: Non-technical users may know what they want to ask but not how to navigate the platform efficiently.
Hidden insights: Important trends can remain buried when users don't know where to look or which dashboards contain relevant information.

As organizational data continues to grow, many teams are looking for a more intuitive way to interact with information. Natural language analytics helps by allowing users to start with a question rather than a dashboard.

How Natural Language Analytics Works in BI Platforms

The experience may feel simple for the user, but several technologies work together behind the scenes to provide accurate, context-aware answers.

1. Understanding the question

NLP analyzes what you typed to identify the metric, the time frame, and the dimension you're asking about. Ask "Show quarterly revenue by region," and the system identifies revenue as the metric, quarter as the time frame, and region as the breakdown.

2. Mapping it to real data

The system translates your question into an actual query against your data source, matching your wording to the correct fields and relationships without requiring you to know the schema, table names, or SQL syntax underneath.

3. Running the analysis

Once the data is retrieved, the platform performs whatever operation the question calls for: aggregating totals, comparing periods, calculating trends, or measuring variance against a target.

4. Delivering the answer

Results come back as a chart, a KPI card, a table, or a plain-language summary, whichever format makes the answer clearest, rather than a raw table of rows for you to interpret yourself.

Key Capabilities to Look for in a Natural Language Analytics Platform

Not every tool that accepts a typed question delivers real analytics. These are the capabilities that separate a shallow keyword-search feature from a platform that can genuinely answer business questions:

Natural language understanding: Interprets free-form questions, not just rigid keyword syntax matched to column names.
Query transparency: Shows the underlying data and logic behind an answer, so users can verify it rather than take it on faith.
Automated visualization selection: Picks the chart, KPI card, or table that best fits the question, instead of returning a generic table every time.
Narrative generation (NLG): Explains the "why" behind a result in plain language alongside the visual.
Context Retention: Supports follow-up questions without making the user repeat the full question from scratch.
Synonym and vocabulary handling: Understands that "revenue" and "sales" refer to the same metric and can learn new business terminology over time.
Governed, connected data: Works against the organization's existing data sources and permission model, rather than a siloed copy of the data.

Natural Language Analytics in Action

Imagine a sales director asks "Which region exceeded its revenue target this quarter?" Instead of searching through multiple dashboards, the system analyzes the question and returns:

The Northeast region exceeded its quarterly revenue target by 12.4%, generating $4.8M against a target of $4.27M. Growth was primarily driven by enterprise software sales, which increased 18% compared to the previous quarter.

The response could also include:

A KPI card showing target vs. actual revenue.
A regional comparison chart.
A short AI-generated explanation highlighting key drivers.

The user gets both the metric and the business context without needing to navigate dashboards or write a query.

Natural Language Analytics vs. Traditional Business Intelligence

NLA doesn't replace BI dashboards. Instead, it enhances how users interact with them.

Traditional BI	Natural language analytics
Navigate dashboards and filters	Ask a direct question
Requires training on the tool	Requires minimal training
Analysts and BI teams	Anyone, at any skill level
Structured, recurring analytics	Ad hoc, in-the-moment questions
Static and built in advance	Conversational and built on demand

By reducing the effort required to explore data, natural language analytics can make business intelligence more accessible to a wider group of users across the organization.

Natural Language Analytics vs. Natural Language Query

The two terms get used interchangeably, but they describe different depths of capability, and the distinction matters when evaluating a platform. Specifically:

Natural language query (NLQ) is the retrieval layer. It interprets a plain-English question and returns the matching data, a number, a filtered table, a single chart.
Natural language analytics goes a step further. It doesn't just retrieve the data behind a question; it analyzes the data to identify trends, compare periods, calculate variance against a target, and package the result with context instead of providing just a raw answer.

Put simply, NLQ answers what happened, while NLA can also surface why it happened and what's notable about it. Every NLA platform relies on NLQ underneath, but not every NLQ tool performs full analytics.

Why Natural Language Analytics Matters

The value shows differently depending on who's using it. For example:

For business leaders, it closes the lag between having a question and getting an answer. A CFO can ask how marketing costs compared to pipeline this quarter directly, instead of opening a ticket and waiting for a dashboard to queue up.
For data and BI teams, the win isn't fewer questions from stakeholders; it's fewer repetitive ones. When business users can self-serve the routine "what were last week's numbers" questions, analysts get more time back for the work that actually needs their expertise, like forecasting, root-cause analysis, and building new data models.
For analysts and non-technical users, it has the most direct benefit: someone who's never written a query can ask "Which products sold the most in the Northeast last month?" and get a real answer, without waiting for someone else to translate the question for them.

A lot of business knowledge lives with people who aren't data specialists, such as sales reps who know their territory and support teams who know which issues keep recurring. Natural language analytics lets that knowledge connect directly to the data instead of routing through someone else first.

Real-World Business Intelligence Use Cases

Natural language analytics is already transforming how organizations use data across departments.

Sales performance analysis

Which region exceeded target this quarter? Which accounts show early churn signals? A sales leader can ask a question directly and get an answer on the spot, rather than waiting for an analyst to pull a custom dashboard ahead of a forecast call.

Marketing Analytics

Marketing teams can ask which campaign produced the strongest ROI or which channels generated the most leads, replacing what would normally take switching between several channel-specific dashboards with a single question.

Customer support analytics

Support managers no longer need to build a new dashboard every time leadership asks for an update. A direct question is enough to surface common ticket categories, escalation trends, and resolution times.

How Bold BI Approaches Natural Language Analytics

Bold BI brings natural language analytics into the dashboards and data you already work with, through generative AI and a unified AI Agent.

AI Agent

The AI Agent lets users ask questions in plain English and get answers directly from their data.

Smart Narrations

Smart narrations automatically generate plain-language explanations for dashboard visualizations. Instead of interpreting charts manually, users get a concise summary of what the data is showing. The same capability is also available in scheduled dashboards through AI-generated summaries.

AI Copilot

The AI Copilot simplifies dashboard creation by turning prompts into visualizations. Users can describe what they want, like "Create a bar chart of units sold by category," and the AI Copilot will generate a chart without you having to manually configure every field.

Together, these capabilities help users ask questions, understand insights, and create visualizations using natural language.

Getting Started with Natural Language Analytics

Teams evaluating natural language analytics for the first time tend to get the most value by starting narrow rather than rolling it out everywhere at once:

Start with the questions your analysts already field repeatedly, weekly sales numbers, campaign performance, ticket volume, since those are the fastest wins.
Connect it to governed, existing data sources rather than a separate copy, so answers stay consistent with what's in your dashboards.
Give a small group of business users early access and use their real questions to test accuracy before a wider rollout.
Keep query transparency on by default, so users can see what the system queried and build trust in the answers.

Final Thoughts

Natural language analytics adds a new way to interact with business intelligence. Dashboards remain essential for monitoring recurring metrics and performance trends, while conversational analytics helps users investigate new questions as they arise.

For organizations looking to expand self-service analytics, the most effective approach is often to start with common business questions and enable users to explore the answers directly through natural language. Combined with dashboards and visual analytics, this creates a more flexible experience for both business and technical teams.

Ready to see it in action? Start a free trial of Bold BI® and try asking your own data a question. Prefer to explore first? Browse the documentation or schedule a demo to see how conversational analytics fits into your existing dashboards.

Frequently Asked Questions

1.
How does natural language analytics improve business intelligence?
Natural language analytics makes it easier for users to interact with data by asking questions in plain English. Instead of navigating multiple dashboards, users can explore data directly and receive answers in the form of charts, KPIs, tables, or AI-generated explanations.
2.
Is natural language analytics the same as natural language query?
Not quite. Natural language query (NLQ) focuses on retrieving data in response to a question. Natural language analytics goes a step further by analyzing that data, identifying patterns or trends, and presenting insights in a more meaningful format.
3.
Can non-technical users use natural language analytics?
Natural language analytics is designed for business users, executives, and operational teams who need insights from data without writing queries or understanding database structures.
4.
Does natural language analytics replace dashboards?
It works alongside dashboards rather than replacing them. Dashboards are ideal for monitoring recurring metrics and performance trends, while natural language analytics helps answer ad hoc questions as they arise.
5.
What industries use natural language analytics?

Natural language analytics can be used in any industry that relies on data-driven decision-making, including finance, healthcare, retail, manufacturing, SaaS, logistics, and telecommunications.
6.
How is Bold BI's natural language analytics different from tools like ThoughtSpot or Power BI Q&A?
Many analytics platforms allow users to ask questions about their data. Bold BI combines conversational analytics with AI-powered dashboard summarization, prompt-based dashboard creation through AI Copilot, and AI Agent capabilities that help users interact with dashboards and connected data from a unified experience.
7.
How does Bold BI support natural language analytics?
Bold BI supports natural language analytics through AI Agent for conversational data exploration, AI Copilot for creating visualizations from prompts, and Smart Narrations for generating plain-language explanations of dashboard insights.
8.
How accurate are the answers from a natural language analytics tool?
Accuracy depends on data quality and how clearly the platform shows its work. Look for query transparency, the ability to see the underlying data and logic behind an answer, so business users can verify results rather than take them on faith.

AI Dashboard Summaries: Turn Data into Business Insights

Bold BI by Syncfusion — Tue, 21 Jul 2026 12:58:16 +0000

TL;DR: Dashboards present valuable business data, but interpreting multiple charts and KPIs often takes time and expertise. AI dashboard summaries turn dashboard data into clear, natural-language sentences by highlighting trends, anomalies, and business context. Teams that use them make faster decisions, hold shorter meetings, and get more people to use their dashboards. Bold BI® delivers AI dashboard summaries across full dashboards, individual widgets, scheduled analytics, and customizable AI prompts.

Introduction

AI dashboard summaries turn complex dashboards filled with charts and KPIs into clear, concise insights anyone can understand in seconds. Instead of asking a sales manager or operations leader to interpret multiple widgets and draw conclusions manually, the dashboard does the work first and delivers the headline.

Dashboards themselves are not the problem. Organizations already rely on them to track revenue, churn, inventory, and operational metrics in real time. The challenge begins after someone opens the dashboard. A modern dashboard can contain dozens of visualizations, and identifying what matters, why performance changed, and what action to take still depends on the user. Two people could look at the same dashboard and walk away with entirely different conclusions.

This is the gap AI dashboard summaries close. They analyze dashboard data the way an experienced analyst would, spotting trends, detecting anomalies, and connecting related metrics, then translate those findings into simple, natural language.

In this blog, you’ll learn what AI dashboard summaries are, why they matter, how they transform dashboards into actionable insights, and how Bold BI enables organizations to deliver these insights across dashboards, widgets, and scheduled analytics.

Why don’t dashboards always tell the full story?

Dashboards visualize data. They are not built to explain why metrics change or what actions to take. As dashboards become more detailed, interpreting the information often becomes just as challenging as collecting it.

1. Too much to scan, too little time

A single dashboard can hold dozens of widgets. Finding the few that matter often means clicking through all of them first. Instead of acting on insights, users spend time navigating charts and metrics, trying to figure out what changed and what needs attention.

2. Charts show what changed, not why

A chart can show revenue dropping or churn increasing. It can’t explain why. Users still need to cross-reference metrics, compare past performance, and piece together the cause, manually.

3. Interpretation takes time

Comparing widgets, spotting anomalies, and connecting related metrics can take longer than expected, especially under deadlines. The time saved by dashboards often gets lost in analysis.

4. The same dashboard, different conclusions

Different teams read the same dashboard differently. Sales may see growth, while finance sees margin pressure. Without shared context, teams spend more time debating insights than acting on them.

5. Insights still need to be communicated

Even after understanding the data, users must summarize it in emails and meetings. That becomes a second round of manual work.

As organizations generate more data, these challenges make it increasingly important to move beyond simply visualizing information toward helping users understand it.

What is an AI dashboard summary?

An AI dashboard summary is a short, natural-language explanation, generated automatically, that tells you what a dashboard's data means. Rather than simply describing individual charts, dashboard summaries examine related metrics together to identify meaningful trends, highlight unusual changes, and provide business context. A good AI dashboard summary highlights:

The most important trends across the dashboard, not just one widget at a time.
Anomalies or changes that fall outside the expected range.
Key statistical context, such as totals, averages, and period comparisons.
How related metrics move together across the business.
A clear, plain-language summary of overall performance.

By combining data interpretation with natural-language explanations, AI dashboard summaries make dashboards more accessible to both technical and non-technical users.

What data feeds an AI dashboard summary

AI dashboard summaries don’t create new business context. They use what already exists inside each widget: titles, chart types, axis values, and underlying data. AI reads what the dashboard shows and turns it into clear, complete sentences. That’s why naming matters. A widget called “Chart 3” gives the AI little to work with. A name like “Monthly Revenue by Region” provides clear context and leads to better summaries.

The same applies at the widget level with Widget Insights, which focus on explaining a single chart rather than the full dashboard. Simpler elements like KPI cards or filters are less useful here because they don’t provide enough trend data to describe.

When are AI dashboard summaries most valuable?

Dashboard summaries prove most useful on dashboards that are opened under time pressure, especially by people who didn’t build them. Common examples include:

Executive performance reviews: Leaders often want the headline before the detail.
Sales pipeline reviews: Sales might want summaries ahead of forecast discussions.
Financial close reviews: Timing is critical for financial reviews.
Operational monitoring: Operations is often tracked across teams, shifts, or locations.
Customer success check-ins: Data is best reviewed before renewal or account conversations.
Healthcare and service analytics: Administrators need quick visibility into capacity and risk matters.

In each case, the summary becomes the starting point, not the final answer. Users read it first, then explore the specific widgets behind what stands out.

How AI dashboard summaries turn data into a decision

Turning numbers and charts into decisions that managers can act on happens in four steps.

The transformation typically follows this process:

1. From metrics to patterns

The AI looks across a dashboard’s KPIs, charts, and tables together, not one at a time, and identifies the trends, anomalies, and shifts that stand out.

2. From patterns to context

Once it flags what changed, it connects those changes to related metrics instead of presenting them in isolation.

Rather than simply stating that revenue declined, it can show that the drop aligns with weaker performance in a specific region while another channel remained steady, providing a clear “why,” not just a “what.”

3. From context to narrative

The patterns and context are turned into a short, plain-language summary as shown in the following example:

Revenue decreased by 12% this quarter, primarily driven by lower sales in the West region, while online revenue remained stable. Customer retention improved among enterprise accounts despite slower new customer acquisition.

This delivers the dashboard’s story in a few sentences, instead of requiring users to interpret multiple charts.

4. From narrative to decision

Because the interpretation is already done, users can focus on what to do next, or dive deeper into the specific area that needs attention.

AI dashboard summaries don’t replace dashboards; they make them easier to act on by providing clarity upfront.

The result is a more efficient analytics experience where dashboards present the evidence, while AI dashboard summaries explain its business significance.

Real-world business examples of AI dashboard summaries

AI dashboard summaries help organizations quickly understand performance by turning complex data into clear insights.

Here’s what that looks like across different dashboards.

Sales: Monitoring sales performance trends

A Sales Performance Dashboard tracks revenue, growth, units sold, and performance by category, region, and channel.
Instead of reviewing multiple visuals, an AI summary highlights key insights as shown:

Revenue reached $12.2M with 111,842 units sold. Online sales ($8.5M) outperformed stores ($3.67M). Accessories and Kids’ Wear led sales, while Florida underperformed.

Business outcome: A sales leader quickly sees top channels, categories, and weak regions to guide marketing spend.

Retail: Understanding inventory trends

A Retail Inventory Management Dashboard tracks stock levels, supplier activity, and sales trends.
AI summaries highlight key insights as shown:

Sales totaled $1.31M, with a 16-day turnover and 2-day lead time. Inventory remains strong with 29 suppliers, 97,383 units in stock, and 46,522 incoming units.

Business outcome: An inventory manager can assess turnover, lead time, and stock at a glance and adjust orders quickly.
AI Dashboard Summary in a Retail Management Dashboard[/caption]

Healthcare: Improving operational visibility

A Hospital Management Dashboard monitors admissions, occupancy, and specialist demand.
AI summaries highlight key insights as shown:

214 admissions were recorded, with 70.39% occupancy. Ophthalmology had the highest demand, supported by 20 doctors and a ~7% readmission rate.

Business outcome: A hospital administrator gets a clear snapshot of capacity, demand, and care quality in one view.

Best practices for AI dashboard summaries

AI dashboard summaries deliver the greatest value when dashboards are designed with clear business context and high-quality data. The following practices help organizations improve the relevance and usefulness of AI-generated narratives.

Use clear, descriptive widget titles. AI summaries rely on widget names to interpret data, so labels like Monthly Revenue by Region deliver more accurate insights than generic names.
Build around one business question. Dashboards focused on a single goal, like growth or retention, produce summaries that stay on topic instead of drifting.
Set a custom prompt for your team's language. Custom AI summarization prompts tell the AI which metrics and terms to prioritize, so it sounds like your team wrote it.
Review before you share widely. AI speeds up interpretation, not the final sign-off. Give it the same quick check you'd give any analytics before it reaches a client or the board.
Turn feedback into fuel. Flag summaries that miss the mark. Consistent feedback is what sharpens the output over time.
Start with the summary, then dig in. It's the entry point to the story, not the whole analysis.

How Bold BI delivers AI dashboard summaries

Bold BI integrates AI dashboard summaries throughout the analytics experience, helping organizations interpret dashboard data more efficiently instead of manually reviewing every visualization.

Rather than providing a single summarization feature, Bold BI supports AI-powered narratives across dashboards, widgets, scheduled analytics, and customizable business prompts.

1. Dashboard Summaries, for the full picture

Dashboard summaries generate a high-level narrative that highlights significant trends, anomalies, and statistical observations across the dashboard. This helps business executives get an overall read before they explore individual widgets.

2. Widget Insights, for one chart at a time

Some questions require a closer look at a specific chart rather than the entire dashboard. Widget Insights provide AI-powered explanations for supported visualizations, helping users understand trends, statistical observations, and important changes within individual widgets.

3. Custom AI Summarization Prompts, for your team's own language

Every organization has its own terminology, KPIs, and analytics priorities. With Custom AI Summarization Prompts, administrators can tailor how AI generates dashboard summaries, so the output better reflects business context and internal analysis needs.

Custom Prompt

4. AI-generated scheduled summaries, for recipients who never open the dashboard

Bold BI can include AI-generated scheduled summaries, providing recipients with concise explanations generated when the schedule runs. This helps stakeholders review key dashboard and widget insights more efficiently before opening the full dashboard.

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Turn dashboard data into business insights with Bold BI

Dashboards are good at showing what happened. AI dashboard summaries are what tell you what it means, so your team spends less time interpreting charts and more time deciding what to do about them.

With Dashboard summaries, Widget Insights and Custom AI Summarization Prompts in scheduled analytics, Bold BI® covers the summarization layer end to end, not just one point of it.

Turn your dashboards into actionable business insights today.

Start a free trial. No credit card required.
Request a personalized demo.
Explore Bold BI's AI-powered analytics capabilities.

Related Resources

Frequently Asked Questions

What are AI dashboard summaries?
AI dashboard summaries are AI-generated explanations that read a dashboard’s data and describe its important trends, anomalies, and statistics in plain language so users understand the dashboard’s insights without reviewing every chart.
How do AI dashboard summaries work?
They’re generated from information already present in the dashboard, including widget titles, chart types, axis data points, and column names, which the AI processes to write a natural-language explanation of what the data shows.
What information is used to generate a dashboard summary?
**Bold BI generates dashboard summaries from each widget’s title, chart type, axis data points, and column names. Descriptive widget titles produce noticeably better summaries than generic ones like “Chart 1.”
**Can AI summarize individual dashboard widgets, not just the whole dashboard?
Yes. Bold BI’s widget insights generate an AI explanation for a single supported widget, for the moments when someone needs to understand one chart rather than the entire dashboard.
Do AI dashboard summaries replace the dashboard itself?
No. A summary is a starting point that tells readers what matters before they explore the underlying charts and metrics. It doesn’t replace drilling into the data behind it.
Can organizations customize how AI dashboard summaries are written?
Yes. Custom AI summarization prompts let administrators guide the tone, terminology, and priority metrics, so the summary matches how the business actually talks about its numbers.
Can AI dashboard summaries be delivered on a schedule, without opening the dashboard?
Yes. Bold BI can include AI-generated summaries in scheduled analytics deliveries, giving recipients a concise narrative from the latest data as soon as the schedule runs.
How accurate are AI-generated dashboard summaries?
Accuracy depends on the quality of the underlying dashboard: clear widget titles, clean data, and well-defined metrics all improve it. Even so, someone familiar with the business should review a summary before it goes to a wide audience, the same way they’d review any analytics data before sending it out.
Do AI dashboard summaries replace the need for a data analyst?
No. They remove the repetitive first pass of interpretation, comparing widgets, spotting anomalies, restating trends in words, so analysts can spend their time on the deeper questions a summary can’t answer, like root causes or what to do next.
How can organizations get better results from AI dashboard summaries?
Use descriptive widget titles, build dashboards around a specific business question, set up a custom prompt for your team’s terminology, review summaries before wide distribution, and use feedback tools to refine quality over time.

Embedded Analytics ROI: How Enterprises Meaure Success

Bold BI by Syncfusion — Fri, 17 Jul 2026 14:26:55 +0000

TL;DR: Measuring embedded analytics ROI helps organizations determine whether their analytics investments are driving real business value. By tracking metrics like adoption, engagement, efficiency, retention, and revenue impact, businesses can optimize analytics performance and demonstrate measurable outcomes with tools like Bold BI®.

Introduction

“What is our embedded analytics ROI?” This is the question every enterprise eventually has to answer. Once dashboards are live inside your application, how do you know the investment is paying off?

It is a fair question, and not always an easy one to answer. A 2026 report by the Product-Led Alliance found that 57.2% of organizations that have implemented embedded analytics report no measurable ROI so far.

Simply embedding dashboards into an application is not enough. Organizations need to understand whether customers are using the analytics, whether the analytics improve business outcomes, and whether the investment contributes to long-term product and operational success.

In this article, we will explore how enterprises measure embedded analytics ROI, walk through a sample calculation, examine the metrics that matter most, and highlight common mistakes organizations make when evaluating success.

What is embedded analytics ROI?

Embedded analytics ROI refers to the measurable value an organization gains by integrating analytics capabilities directly within applications, compared to the total cost of implementation, maintenance, and ongoing optimization.

The purpose of embedded analytics is to help users access relevant insights without leaving the applications they use every day. Instead of switching between multiple tools, users can analyze data, monitor KPIs, and make informed decisions within their existing workflows.

Embedded analytics creates value when users can:

Access interactive dashboards directly within applications.
Explore data through filtering and drill-down capabilities.
Perform self-service analytics and gain insights without technical expertise.
Monitor business performance in real time.
Make faster and more confident decisions.

While these capabilities create value, organizations still need a reliable way to measure that impact, which begins with understanding ROI.

How to calculate embedded analytics ROI

Embedded analytics ROI compares the measurable benefits generated by embedded analytics against the total investment required to implement and maintain the solution. A commonly used formula is:

ROI (%) = ((Total Benefits - Total Costs) / Total Costs) × 100

Example

Consider a midsize SaaS company that invests in embedded analytics and incurs the following annual costs:

Cost category	Annual cost
Software licensing	$60,000
Implementation and development	$40,000
Infrastructure and maintenance	$20,000
User training	$10,000
Total investment	$130,000

The company measures the following annual benefits:

Benefit category	Estimated value
Reduced manual analytics	$80,000
Improved customer retention	$110,000
Productivity gains from faster decisions	$50,000
Total benefits	$240,000

ROI = (($240,000 - $130,000) / $130,000) × 100 = 85%

An 85% ROI provides a figure for leadership discussions and budget planning. It offers far stronger evidence than simply reporting that dashboard usage has increased.

Typical benefits include high analytics adoption, stronger customer engagement, improved operational efficiency, and new revenue opportunities. Common investments include software licensing, development costs, infrastructure, training, and ongoing administration.

Although financial calculations provide a useful starting point, organizations should evaluate ROI through a combination of operational, behavioral, and business metrics.

Who should measure embedded analytics ROI?

Measuring embedded analytics ROI is valuable for organizations focused on product growth, customer success, and digital transformation. It is particularly relevant for the following groups:

SaaS product managers: Product managers can evaluate whether embedded analytics improves product adoption, customer engagement, and overall product value.
Customer success managers: Customer success leaders can monitor customer engagement and identify opportunities to improve product adoption, retention, and long-term satisfaction.
Product analytics teams: Analytics teams can analyze usage patterns, monitor engagement trends, and optimize embedded analytics experiences over time.

The CTO/CIO lens: Beyond adoption metrics

CTOs and CIOs often focus on questions that go beyond adoption metrics alone. Their concerns typically include:

Total cost of ownership, including licensing, infrastructure, and maintenance costs.
Build-versus-buy decisions and long-term platform economics.
Security, scalability, and support requirements across users and tenants.

When building a business case for executive leadership, these factors often carry as much weight as engagement metrics. Next, let’s explore why measuring ROI is essential for maximizing the value of an analytics strategy.

Why measuring embedded analytics ROI matters

Many organizations invest significant time and resources into embedded analytics initiatives. Without a clear measurement framework, it becomes difficult to determine whether that investment is producing meaningful business value.

According to a 2025 industry study conducted by Hanover Research, 99% of organizations reported seeing ROI from embedded analytics within 12 months, while 70% achieved returns within six months.

Key questions for evaluating ROI in terms of more than just money include:

Are users actively engaging with analytics?
Is analytics improving business efficiency?
Are decisions being made faster?
Has self-service analytics reduced reporting requests?
Is analytics contributing to retention and customer satisfaction?

Organizations that continuously monitor these indicators are better positioned to demonstrate measurable value and improve outcomes over time.

Key metrics for measuring embedded analytics ROI

Successful enterprises evaluate ROI using a combination of adoption, engagement, operational, and business metrics.

Analytics adoption

Organizations often measure adoption through active users, dashboard views, and self-service analytics usage. Consistent use indicates that analytics is becoming part of day-to-day workflows rather than an underutilized feature.

Analytics engagement

Adoption alone does not guarantee ROI. Higher engagement, reflected through interactions with filters, drill-downs, and self-service features, indicates that users rely on analytics for decision-making rather than for occasional viewing.

Time to insight

Reducing the time required to move from a question to an answer improves productivity and accelerates decision-making. Faster access to insights minimizes delays and reduces manual effort across teams.

Operational efficiency

Embedded analytics helps organizations streamline data-driven workflows by reducing dependence on IT and BI teams for routine reporting. Its impact can be evaluated through metrics such as the volume of self-service analytics usage, the number of analytics-related support requests, and the reduction in manual effort required to deliver insights.

Customer retention and satisfaction

Embedded analytics can improve customer experience by making insights easier to access and act on. Its impact is measured through metrics such as customer retention, renewal rates, churn reduction, customer satisfaction (CSAT) scores, which measure how satisfied customers are with a product or experience, and net promoter score (NPS), which measures how likely customers are to recommend the product to others.

Revenue impact

Embedded analytics can influence revenue by helping users make faster, data-driven decisions and uncover growth opportunities. Revenue impact is measured by tracking changes in metrics such as conversion rates, customer retention, expansion revenue, and overall revenue growth following analytics adoption.

A practical framework for connecting usage to outcomes

Rather than evaluating each metric independently, leading organizations follow a structured sequence:

Step	Action	What it reveals
1	Measure adoption	Whether analytics is becoming part of daily workflows.
2	Evaluate engagement	Whether usage reflects genuine reliance.
3	Assess operational improvements	Whether analytics effort and support requests are decreasing.
4	Monitor business outcomes	Whether retention, productivity, and satisfaction are improving.
5	Connect usage to results	Whether outcomes differ between high-adoption and low-adoption users.

The final step is the one most organizations overlook. Comparing outcomes before and after implementation, or among groups with different adoption levels, transforms usage data into a meaningful ROI story.

Want a head start on steps four and five? Bold BI's key performance indicators (KPIs) whitepaper provides practical guidance on selecting KPIs that connect analytics initiatives directly to business outcomes, rather than focusing solely on usage metrics.

Common mistakes enterprises make when measuring ROI

Many enterprises invest heavily in embedded analytics but struggle to prove its business value. In many cases, the challenge is not a lack of data but an inability to connect analytics usage with meaningful outcomes.These challenges can be addressed through proven measurement strategies and governance practices. Let’s look at them.

Best practices for measuring embedded analytics ROI

Organizations that achieve the highest returns on embedded analytics initiatives typically follow these best practices:

Define success metrics early: Establish clear business objectives and KPIs before implementation so success can be measured consistently.
Align analytics with business goals: Ensure analytics initiatives directly support organizational priorities such as efficiency, customer satisfaction, retention, or growth.
Promote self-service analytics: Enable users to access insights independently so they can make decisions without relying on technical teams.
Continuously monitor adoption: Regularly evaluate adoption and engagement trends to identify opportunities for improvement.
Track outcomes alongside usage: Measure business impact alongside analytics activity to gain a complete understanding of value.
Optimize user experiences: Continuously improve dashboards, navigation, and accessibility to encourage broader adoption and engagement.

While these best practices provide a strong foundation for measuring ROI, seeing them applied in a real-world scenario helps illustrate how embedded analytics can deliver measurable business value.

Real-world example

The following example demonstrates how an organization measured success through improvements in analytics efficiency, faster decision-making, and better operational performance.

How Vialto Partners improved reporting efficiency with Bold BI

Vialto Partners, a global business consulting and mobility services organization, struggled with fragmented data sources and a lack of centralized, real-time insights. This made reporting more time-consuming and slowed decision-making across the enterprise.

After implementing Bold BI, Vialto integrated multiple data sources into interactive dashboards, enabling teams to access actionable insights more quickly. The results included:

60% reduction in report generation time.
40% faster decision-making cycles.
Improved data accuracy across reporting processes.
Greater process improvements and responsiveness to client needs.

Rather than focusing solely on dashboard usage, Vialto demonstrated ROI through measurable improvements in efficiency and decision-making speed.

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Turn embedded analytics into measurable business outcomes with Bold BI

Measuring embedded analytics ROI goes beyond tracking dashboard usage. It requires organizations to connect adoption, engagement, operational efficiency, and long-term business outcomes into a comprehensive view of performance.

Bold BI® helps organizations move beyond basic usage metrics by linking analytics activity to measurable business improvements. With interactive dashboards, self-service analytics capabilities, and support for multiple data sources, Bold BI enables enterprises to not only embed analytics directly into their applications but also build ROI scorecards that connect their analytics usage with operational and business KPIs.

Ready to maximize the value of your embedded analytics investment? Start your free 30-day trial or request a personalized demo to see how Bold BI can help you measure embedded analytics ROI with an ROI scorecard tailored to your business and analytics goals.

Frequently asked questions

What is embedded analytics ROI?
Embedded analytics ROI measures the value generated by delivering analytics within applications compared to the costs associated with implementation, maintenance, and ongoing management.
Why is measuring embedded analytics ROI important?
Measuring ROI helps organizations justify investments, identify opportunities for improvement, and demonstrate business value.
Which metrics are most important for measuring embedded analytics ROI?
Key metrics include analytics adoption, engagement, time to insight, operational efficiency, customer retention, decision-making effectiveness, and revenue impact.
What challenges do organizations face when measuring embedded analytics ROI?
Common challenges include focusing on vanity metrics, managing fragmented data sources, lacking baseline measurements, failing to segment users, and struggling to connect analytics usage with business outcomes.
How can Bold BI help organizations measure embedded analytics ROI?

Bold BI enables organizations to embed analytics, track adoption and engagement, monitor business KPIs, and build customized dashboards that support ROI measurement.
How long does it take to measure embedded analytics ROI?
The timeframe depends on business goals and implementation scope.
How often should embedded analytics ROI be reviewed?
Organizations should review embedded analytics ROI monthly to monitor usage and engagement trends, quarterly to evaluate performance against business goals and identify optimization opportunities, and annually to assess long-term business impact and overall return on investment.

Top 5 AI-Powered BI Tools to Explore in 2026

Bold BI by Syncfusion — Fri, 17 Jul 2026 11:59:41 +0000

TL;DR: AI-powered BI tools combine conversational querying, automated insights, and predictive capabilities to help teams explore their data without relying heavily on analysts. Key features include AI-driven analysis, dashboard creation, embedded analytics, and governance. Leading tools in 2026 include Bold BI®, Power BI, Zoho Analytics, Domo, and Looker. The right platform improves visibility, reduces reporting delays, and scales analytics across users and customers.

Introduction

Having data isn’t the same as having answers. Many teams still depend on analysts to build dashboards or respond to ad hoc questions, which slows decisions and limits visibility across the business. AI-powered BI tools eliminate that bottleneck by helping users explore data using natural language, automatically surfacing insights, and creating dashboards more efficiently. Instead of spending hours preparing dashboards, teams can focus on understanding what the data means and acting on it.
This article explains what AI-powered BI software is, why it matters, which features to evaluate, and how the leading platforms compare in 2026.

What is an AI-powered BI tool?

An AI-powered BI tool is a business intelligence platform that uses AI to help users analyze data, uncover patterns, and generate insights. It simplifies analytics by reducing the need for manual queries or advanced technical skills. Modern AI-powered BI platforms can:

Process natural-language questions.
Automatically generate dashboards and summaries.
Detect unusual trends and anomalies.
Provide predictions based on historical data.

This makes analytics more accessible to business users across departments while still supporting enterprise governance and scalability.

Why AI-powered business intelligence tools matter

AI-powered business intelligence tools help organizations move from reporting to decision-making faster. Key benefits include:

Key features to look for in modern BI tools

Choose a modern BI tool that balances self-service usability with governance and control.

Data management

Connected data across systems: The platform should bring together information from different tools so users can analyze business performance without switching between tools.
Structured data preparation: It should help organize raw data into reliable datasets, ensuring consistency in analytics.

AI features

No-code dashboard creation: Users should be able to design dashboards through simple interactions, reducing the need for technical support.
AI-assisted querying and insights: The system should allow users to ask questions in natural language and receive meaningful answers, visualizations, or summaries.
Automated insight discovery: It should automatically surface key patterns, unusual activity in data, and trends, helping users focus on what matters most.
Plain-language explanations: The platform should provide context behind metrics, helping users understand not just what happened, but also why it happened.
Interactive exploration capabilities: Users should be able to drill into data and explore follow-up questions without building new dashboards.

Enterprise requirements

Embedding dashboards into apps: Dashboards should be easily integrated into applications, delivering insights directly within user workflows.
Access control and governance: The system should ensure secure and consistent access to data across users.
Collaboration and shared visibility: Teams should be able to share dashboards and align around a single source of truth.
Scalability and performance: The platform should handle increasing data volumes and users without performance issues.
Customization and branding support: Organizations should be able to tailor analytics experiences for internal teams or external customers.

AI-powered BI trends shaping 2026

AI-powered business intelligence continues to evolve rapidly. The most important trends shaping analytics platforms in 2026 include:

Conversational analytics through AI assistants and copilots.
Automated dashboard creation from datasets.
AI-generated summaries and automatic written explanations of data.
Predictive forecasting and business planning.
Real-time anomaly detection that identifies unusual activity and triggers smart alerts.
Embedded AI-powered analytics within applications.
Explainable AI and strong governance controls.
Self-service analytics for non-technical users.
Generative AI experiences that allow users to build dashboards, generate summaries, and explore data through simple conversations.

Modern AI-powered BI tools are increasingly centered around AI copilots that help users interact with analytics using natural language instead of traditional reporting workflows. These innovations are helping organizations move beyond traditional reporting and toward faster, more proactive decision-making.

How to choose the right AI-powered BI platform in 2026

Many AI-powered BI platforms offer similar capabilities, but the best choice depends on your goals, users, and data environment. When evaluating a platform, consider the following:

Business size and requirements: Small businesses often prioritize ease of use and affordability, while larger organizations may require advanced governance, scalability, and security controls.
AI capabilities: Look for capabilities like natural language querying, automated insights, predictive analytics, AI-generated dashboards, and conversational analytics.
Data connectivity: The platform should connect easily with your existing databases, cloud applications, spreadsheets, and business systems.
Embedded analytics support: Organizations that deliver analytics to customers should evaluate whether dashboards and AI-powered insights can be embedded directly into applications and portals.
Security and governance: Ensure the platform provides role-based access controls, data governance features, and compliance support to protect sensitive information.
Scalability: The solution should continue to perform effectively as data volumes, dashboards, and user counts increase.

5 AI-powered business intelligence tools to consider in 2026

1. Bold BI

Bold BI is an AI-powered analytics platform designed for organizations that need both self-service dashboards and embedded analytics. It helps teams create dashboards, explore data, and deliver insights inside applications with governed access.

AI-powered features

AI assistant for analytics: Ask questions in natural language to get charts, summaries, and guided follow-up analysis.
AI-generated dashboards and narratives: Generate draft dashboards and plain-language summaries from your datasets.
Insight discovery: Surface trends, anomalies, and key drivers to speed investigation.
Insights from existing dashboards: Get explanations and insights from dashboards you already built.
Embedded AI analytics: Deliver AI-powered dashboards inside your product with controlled access.
Governed AI experience: Role-based permissions help ensure users see only authorized data.

Best for: SaaS companies, ISVs, and enterprises that need embedded analytics, customization, and governance.

Learn how Bold BI helps teams uncover insights faster with AI-assisted analytics.

2. Power BI

Power BI integrates AI capabilities into its analytics environment, enabling users to explore data, identify influences, and generate insights through both visual and automated methods.

AI features

Natural language Q&A: Visual answers for plain-language questions.
Automated insights: Pattern, trend, and change detection across datasets.
Copilot assistance: AI-generated report drafts and summaries.
Azure AI integration: Machine learning models and AI services that extend analysis.

Best for: Organizations already standardized on Microsoft tools and Azure.

3. Zoho Analytics

Zoho Analytics provides AI-assisted analytics focused on ease of use, helping teams generate insights and reports without deep technical expertise.

AI features

Zia conversational assistant: Chat-style data interaction with natural-language responses.
Automatically generated narratives: Automatically generated explanations that help users understand reports faster.
Predictive forecasting: Built-in models that estimate future trends from historical data.
Anomaly detection: Irregular pattern identification with alerts to unusual behavior.

Best for: Small and midsize businesses that want approachable AI analytics.

4. Domo

Domo combines real-time data processing with AI capabilities to support operational monitoring and faster decision-making.

AI features

AI-driven alerts and anomaly detection: Notifications when metrics deviate from expected patterns.
Conversational exploration: Natural-language querying for non-technical users.
Predictive analytics: Forecasted outcomes like sales performance or operational trends.
Real-time insight automation: Continuous analysis of incoming data that surfaces relevant insights.

Best for: Teams that need real-time operational analytics and rapid alerting.

5. Looker

Looker is Google Cloud’s business intelligence platform that combines governed analytics with AI-assisted data exploration. Built on a governed semantic model, Looker helps teams standardize metric definitions while enabling natural-language experiences.

AI features

Visualization assistant: Charts created and customized using natural-language prompts.
Formula assistant: Calculated fields generated faster with AI-assisted formula creation.
Advanced analytics via conversational workflows: Support for forecasting and anomaly detection through guided AI experiences.
Conversational analytics API: Embedded natural-language analytics in applications using a trusted semantic model.

Best for: Organizations operating in Google Cloud environments that prioritize governed metrics.

AI-powered BI tool comparison

Platform	AI assistant	Automated insights	Embedded analytics	Natural language queries	Best for
Bold BI	✓	✓	✓	✓	Embedded analytics and enterprise BI
Power BI	✓	✓	Limited	✓	Microsoft-centric organizations
Zoho Analytics	✓	✓	Limited	✓	Small and mid-sized businesses
Domo	✓	✓	✓	✓	Real-time operational analytics
Looker	✓	✓	✓	✓	Governed analytics and Google Cloud environments

Use cases for AI-powered BI solutions in various industries

Modern organizations use AI-enabled BI platforms not only to visualize data but also to automate analysis, predict outcomes, and act faster on insights.

Sales: Improve pipeline visibility and forecasting

Sales teams need real-time insight into pipeline health, conversion rates, and forecast accuracy. AI-powered BI can highlight slow-moving deals, shifts in win rates, and emerging revenue opportunities while forecasting future performance based on historical patterns.

This helps sales leaders improve forecast accuracy, make faster decisions, and focus resources on the opportunities most likely to generate revenue.

Marketing: Optimize campaigns with faster performance insights

Marketing teams run campaigns across multiple channels, making consistent measurement difficult. AI-powered BI consolidates campaign data, flags underperformance early, and can surface drivers behind changes in engagement or conversion.

By reducing the time spent analyzing campaign performance, marketing teams can optimize spending, improve targeting, and respond to trends more quickly.

Supply chain: Detect bottlenecks and anticipate disruptions

Supply chain teams need visibility into shipments, inventory movement, and supplier performance. AI-powered BI can identify delays and inefficiencies, detect unusual activity in logistics data, and support predictive planning for inventory and demand.

These capabilities help organizations reduce disruptions, improve operational efficiency, and make more informed inventory decisions.

HR: Track workforce trends and improve planning

HR teams analyze retention, hiring velocity, workforce distribution, and engagement signals. AI-powered BI helps identify turnover trends, performance gaps, and staffing risks, supporting better workforce planning and resource allocation.

With faster access to workforce insights, HR leaders can improve retention strategies, support hiring decisions, and align staffing plans with business goals.

Why choose Bold BI for AI-powered analytics

AI-powered BI makes it easier to explore data and generate insights using natural language. Many organizations, especially SaaS companies, ISVs, and enterprises, need a platform that supports internal analytics and customer-facing use cases while maintaining flexibility, scalability, and control. Bold BI supports these requirements by combining:

AI-powered analytics.
Embedded dashboards.
Flexible deployment options.
Enterprise-grade security and governance.
Customization and white-labeling support.

This enables organizations to deliver analytics across applications and teams without adding unnecessary complexity.

Customer success story

How ai-InteleKt accelerated analytics delivery with Bold BI
ai-InteleKt, a company focused on helping businesses leverage data-driven intelligence, needed a solution that could support custom dashboards, integrate seamlessly into its application, and adapt to a variety of analytics use cases. Existing tools lacked the flexibility, ease of use, and cost efficiency needed to support these goals.

By adopting Bold BI, ai-InteleKt was able to:

Simplify dashboard creation through an intuitive drag-and-drop experience.
Deliver analytics more efficiently within its application.
Accelerate product rollout with the support of Bold BI's responsive implementation team.
Provide a flexible experience for both business users and advanced analysts.

As Ravi Srinivasan, Founder and CEO of ai-InteleKt, noted: "I recommend the dashboarding software very highly from Bold BI due to price, performance, and due to the support team."

Ready to deliver AI-powered analytics to your teams or customers? Start your free trial or book a demo to see how Bold BI® supports embedded BI, governed AI insights, and flexible deployment.

Frequently asked questions

How do AI-powered business intelligence tools reduce reliance on IT teams?
They enable users to ask questions, generate dashboards, and analyze data independently using AI, reducing the need for manual report creation and repeated requests to technical teams.
What should businesses consider when choosing an AI-powered BI platform?
Evaluate AI capabilities, ease of use, data connectivity, security, embedded analytics support, governance, and scalability.
What role does AI play in business intelligence?
AI automates data analysis, identifies trends, provides predictions, detects anomalies, and delivers insights in plain language, making analytics faster and more accessible.
Are AI-powered BI tools suitable for non-technical users?
Yes. Natural-language queries, automated insights, and AI copilots help non-technical users explore data without writing SQL or building complex models.
Can AI-powered BI tools handle multiple data sources?
Most platforms connect to databases, cloud applications, and business systems to create a unified analytics environment.
How do AI-powered BI tools improve decision-making?
They provide faster access to insights, real-time monitoring, predictive analytics, and anomaly alerts, helping teams respond quickly and plan more effectively.
How can you get started with Bold BI?
You can start a free trial on the Bold BI website and begin creating AI-powered dashboards.
What is the difference between traditional BI and AI-powered BI?
Traditional BI focuses on dashboards, reports, and historical analysis. AI-powered BI adds natural-language querying, automated insight generation, anomaly detection, forecasting, and conversational exploration, making analytics more proactive and accessible.

What's New in v16.1.90 for Self-Service & Embedded Analytics

Bold BI by Syncfusion — Wed, 15 Jul 2026 13:59:55 +0000

TL;DR: Bold BI® v16.1.90 introduces Anthropic Claude BYOK support, smarter AI-generated insights with currency-aware formatting, faster dashboard rendering, enhanced dashboard navigation, expanded duration formatting options, stronger Keycloak authentication support, and numerous reliability, performance, and usability improvements across the platform.

Introduction

We're excited to announce Bold BI v16.1.90, a release focused on giving organizations greater flexibility in AI adoption, improving dashboard performance, and delivering a more streamlined analytics experience. This release introduces Bring Your Own Key (BYOK) support for Anthropic Claude models, allowing organizations to securely integrate Claude using their own API credentials. In addition, AI-powered analytics are now more reliable with enhanced summary generation and currency-aware insight formatting that improves the readability of financial data.

We've also enhanced the dashboard experience with improved navigation, faster rendering performance, expanded duration formatting options, and stronger authentication support through Keycloak OpenID enhancements.

Alongside these updates, numerous improvements and bug fixes across Designer, Data Sources, Data Hub, Server, and IDP modules help create a more stable, scalable, and dependable analytics platform.

Whether you're building self-service analytics solutions or embedding analytics into customer-facing applications, Bold BI v16.1.90 helps teams create, manage, and consume analytics more efficiently. Let's take a closer look at what's new.

AI Enhancements

We're continuing to expand AI flexibility and model choice, giving organizations greater control over how AI is integrated into analytics workflows.

Bring Your Own Key (BYOK) Support for Anthropic Claude Models

Every organization has unique requirements for AI adoption, security, governance, and compliance. To provide more flexibility, Bold BI now extends its Bring Your Own Key (BYOK) capability to support Anthropic Claude models.

With this enhancement, users can configure and access Claude models using their own Anthropic API credentials directly within Bold BI. Organizations retain full ownership of API usage, billing, and governance policies while taking advantage of Anthropic's AI capabilities.

By expanding support beyond existing AI providers, Bold BI enables organizations to choose the models that best fit their business goals while maintaining compliance with internal security and data governance standards.

BYOK Support for Anthropic Claude Models

Why it matters: Teams can choose the AI provider that best aligns with their security, governance, and operational requirements without sacrificing control over API usage or costs.

Improvements

This release includes enhancements designed to improve AI-generated insights, dashboard accessibility, performance, and authentication reliability.

Smarter Currency Formatting for AI-Generated Insights

Financial insights are most useful when they are easy to understand. Previously, monetary values generated by AI could appear without consistent currency formatting, making financial analysis more difficult, especially in environments that work with multiple currencies.

Bold BI now introduces currency-aware formatting for AI-generated insights. Monetary values are automatically displayed using the appropriate currency symbols and standardized number formats, creating more consistent and readable AI-generated narratives and summaries.

Whether analyzing revenue, costs, profits, or other financial metrics, users can interpret results more quickly and confidently.

Why it matters: Financial figures are easier to read and validate, enabling users to focus on decision-making instead of interpreting data formats

Enhanced Dashboard Navigation Experience

Finding frequently used dashboards is now faster and more intuitive.

The Dashboard Listing page has been enhanced with:

Recent Dashboards for quick access to recently viewed content.
Favorite Dashboards for keeping important dashboards readily available.
Compact and Card view options within Quick Access.
Improved Category Tree alignment.
Child dashboard counts displayed within parent categories.

These enhancements make it easier to browse and manage large dashboard collections while reducing the time required to locate critical analytics resources.

Why it matters: Users can spend less time searching for dashboards and more time focusing on analysis and decision-making.

Faster Dashboard Rendering Performance

Dashboard responsiveness plays a critical role in the analytics experience. To improve loading performance, Bold BI now optimizes dashboard rendering by intelligently grouping widgets that share the same data source.

This approach reduces redundant data retrieval requests and allows shared-source widgets to be processed more efficiently during dashboard loading.

Why it matters: Fewer duplicate data requests lead to faster dashboard rendering, particularly for dashboards containing many widgets connected to the same source.

Extended Duration Format Support

The Duration Format feature has been expanded to support customizable fixed-duration formats, including Year-Month-Day representations.

This enhancement provides dashboard authors with greater flexibility when displaying time-based metrics such as project timelines, service durations, employee tenure, and other elapsed-time measurements.

Duration Format Support

Why it matters: Organizations can present duration-based metrics in formats that better match reporting requirements and business expectations.

Keycloak SSO Token Storage Support for OpenID Provider

Organizations that use Keycloak for authentication can now benefit from enhanced token storage support within OpenID-based single sign-on workflows.

This improvement strengthens session management and authentication reliability, helping users experience smoother sign-in and sign-out processes while improving the stability of identity management integrations.

Why it matters: Organizations running Keycloak-based SSO deployments can deliver more reliable authentication experiences and reduce session-related issues.

Other Bug Fixes and Minor Improvements

In addition to the major enhancements, Bold BI v16.1.90 includes numerous fixes and optimizations across AI, Designer, Data Sources, Data Hub, Server, and IDP modules

Reliability: Improved platform stability through fixes related to:

AI-generated visualizations, dashboard summary generation, and widget insight generation.
Dashboard filtering, chart rendering, and widget configurations.
Dashboard migration, Excel exports, and data previews.
Group management, authentication workflows, and Data Hub connectivity.

Performance: Enhanced dashboard loading and data retrieval through:

Dashboard rendering optimizations.
Elasticsearch connector improvements, including API optimizations, retry mechanisms, and query metrics processing.

Compatibility: Addressed issues across:

PostgreSQL, Elasticsearch, Trino, and Microsoft SQL Server.
Azure Active Directory, OpenID providers, and Keycloak integrations.
Data Hub Web Connectors, cloud deployments, and dashboard migration scenarios.

Usability: Improved the dashboard authoring and viewing experience through fixes related to:

Column Charts, Period-over-Period widgets, and Pie and Doughnut charts.
Date Pickers, Range Sliders, Number Cards, Pivot Grids, and Tab Widgets.
Modern Widget Panel components, calculated expressions, map interactions, and dashboard filtering workflows.

Real-World Use Cases Enabled by Bold BI v16.1.90

The Bold BI v16.1.90 release focuses on helping organizations improve AI flexibility, dashboard usability, analytics performance, and enterprise authentication. Here's how these enhancements translate into real business value.

Adopt AI While Maintaining Control Over Provider Credentials

Challenge: Organizations want to adopt AI-powered analytics without giving up ownership of API credentials, usage policies, and governance controls.
Bold BI solution: Anthropic Claude BYOK support enables teams to connect and use Claude models with their own Anthropic API credentials.
Business impact: Organizations gain greater flexibility in AI provider selection while maintaining control over compliance, billing, security, and governance policies.

Improve Understanding of Financial Insights

Challenge: Inconsistent presentation of currency values can make AI-generated financial insights difficult to interpret.
Bold BI solution: Currency-aware formatting automatically applies appropriate symbols and standardized formatting to AI-generated monetary values.
Business impact: Users can understand financial metrics more quickly and make decisions with greater confidence.

Help Users Find Dashboards Faster

Challenge: Large analytics environments can make dashboard discovery time-consuming.
Bold BI solution: Recent Dashboards, Favorite Dashboards, enhanced Quick Access options, and improved Category Tree navigation simplify dashboard browsing.
Business impact: Users can reach important business insights faster, improving productivity and reducing navigation overhead.

Accelerate Analytics Experiences for Large Dashboards

Challenge: Dashboards containing multiple widgets can experience longer load times when data is retrieved repeatedly from the same source.
Bold BI solution: Intelligent widget grouping reduces redundant data retrieval operations during dashboard rendering.
Impact: Faster dashboard loading improves user experience and helps organizations deliver responsive analytics at scale.

Simplify Analytics for Duration-Based Metrics

Challenge: Organizations often need flexible ways to represent project timelines, service durations, employee tenure, and other time-based measures.
Bold BI solution: Enhanced Duration Format support introduces customizable formats, including Year-Month-Day representations.
Impact: Teams can present duration-based data in a format that aligns with business reporting requirements and improves readability.

Strengthen Enterprise Authentication Workflows

Challenge: Large organizations require reliable authentication and seamless single sign-on experiences across analytics deployments.
Bold BI solution: Keycloak OpenID token storage enhancements, Azure AD synchronization fixes, OpenID logout improvements, and user-management stability updates strengthen authentication workflows.
Impact: Administrators can reduce authentication-related issues while providing users with a more reliable and secure sign-in experience.

Conclusion

Bold BI® v16.1.90 delivers meaningful improvements across AI-powered analytics, dashboard performance, navigation, authentication, and platform reliability. From Anthropic Claude BYOK support and currency-aware AI insights to enhanced dashboard navigation, faster dashboard rendering, advanced duration formatting, and improved Keycloak authentication support, this release helps organizations build, manage, and consume analytics more effectively.

Ready to explore what's new? Existing customers can download Bold BI v16.1.90 today and new users can start a free 30-day trial to experience the latest enhancements in self-service and embedded analytics. For a complete list of features, improvements, and fixes included in this release, visit our release history page. If you have any questions or need assistance, contact our support team for help.

How to Embed Dashboards in React with a Python Backend

Bold BI by Syncfusion — Wed, 15 Jul 2026 13:40:07 +0000

TL;DR: Embedding Bold BI® dashboards in a React application with a Python backend enables secure, scalable, and seamless analytics integration. The Python backend handles token-based authentication, while React delivers a responsive embedded experience with routing and component-based rendering.

Introduction

Modern applications are evolving beyond basic functionality to deliver data-driven experiences directly within the product interface. Users expect real-time insights without switching between tools, making embedded analytics a core feature in today's software landscape.

To meet this expectation, developers need an approach that ensures both seamless user experience and strong security.

In this guide, you'll learn how to embed Bold BI dashboards into a React application using a Python backend (Django), following the latest Embed SDK approach. More importantly, we'll explain why each step matters, so you can confidently implement this in real-world applications.

Before you start: Bold BI's getting started guide covers the core embedding concepts and is worth a quick read before you dive into the sample below.

Why Embed Dashboards in React Applications

React's component-driven architecture allows developers to integrate dashboards as part of a unified interface, rather than treating analytics as a separate system.

This creates a more connected product experience. As a result, you can:

Deliver a consistent and seamless user experience.
Provide real-time insights within workflows.
Increase product engagement through interactivity.
Differentiate your application with built-in analytics.

Why a Python Backend Is Essential

When embedding dashboards, security is non-negotiable. Since dashboards often contain sensitive data, authentication should never be handled on the frontend.

A Python backend, commonly implemented using Django, is responsible for:

Generating secure embed tokens.
Managing authentication and user authorization.
Protecting secret keys and credentials.
Acting as a secure bridge to the Bold BI server.

By keeping these operations on the server side, no sensitive information is exposed in the browser. This backend-first approach establishes a secure and scalable architecture that’s suitable for production environments.

How to Embed a Dashboard in a React Application with Python

The following steps demonstrate how to configure embedding, set up the required components, and render dashboards in a React application using a Python backend and the Bold BI Embedded SDK.

Prerequisites

Before you begin, ensure the following requirements are met:

Python.
Node.js and npm (required to install and run the React app).
Visual Studio Code.
Python Extension for Visual Studio Code.

Step 1: Get the React with Python Sample

Start by cloning the official React with Python sample application from the Bold BI GitHub repository:

git clone https://github.com/boldbi/react-with-python.git

This sample demonstrates:

Rendering a single dashboard.
Rendering a list of dashboards.
Embedding Bold BI dashboards using React and Python.

Step 2: Embed Authentication in Bold BI

Before embedding a dashboard, Embed Authentication must be enabled.

To enable embedding:

Log in to your Bold BI instance.
Go to Administration.
Navigate to Embed Settings.
Enable Embed Authentication.
Save the changes.

This step generates the Embed Secret needed for secure dashboard access.

Step 3: Download the Embed Configuration file

After enabling embed authentication:

Go to the dashboard you want to embed.
Open the Embed tab.
Click Embed Configuration.
Download the embedConfig.json file.

The downloaded file contains all the required configuration details for embedding.

Step 4: Add the Embed Configuration file

Copy the downloaded embedConfig.json file and place it in the application location shown in the sample project structure.

The file contains the following settings:

Property	Description
ServerUrl	The URL of your Bold BI server. Example: http://localhost:5000/bi or https://demo.boldbi.com/bi
SiteIdentifier	Enterprise Edition: site/site1; Bold BI Cloud: empty string
Environment	BoldBI.Environment.Cloud or BoldBI.Environment.Enterprise
DashboardId	The dashboard item ID to be embedded
EmbedSecret	The embed secret generated after enabling embed authentication
UserEmail	Admin user email address used for dashboard access
ExpirationTime	Token expiration time in seconds. Default: 10,000 seconds

Step 5: Start the Python Backend

Navigate to the Python project and run:

python manage.py runserver

The Python application will start on:

http://localhost:8000

The backend is responsible for generating secure authentication tokens required by the Embedded SDK.

Step 6: Install React Dependencies

Move to the React application folder and install all required packages:

npm install

Step 7: Start the React Application

After package installation completes, run:

npm start

The React application will launch on:

http://localhost:3000

and connect to the Python backend to securely retrieve dashboard tokens.

Step 8: View Dashboard Listing (Optional)

By default, the sample displays a single embedded dashboard.

To view the dashboard listing page, navigate to:

http://localhost:3000/dashboardlisting

This page displays the available dashboards in a sidebar for selection.

For more guidance on integrating dashboards into a React application, refer to our help documentation.

How the Sample Works

1. Dashboard Initialization in React

The React application uses BoldBI.create() to initialize the embedded dashboard. The configuration includes:

Dashboard information.
Server information.
Token generation endpoint URL.

The tokenGenerationUrl property points to the Python backend's tokenGeneration endpoint.

2. Token Generation

The Python backend exposes a token generation API. It:

Receives requests from React.
Communicates with the Bold BI server.
Retrieves an authentication token.
Returns the token to the React application.

This ensures the Embed Secret is never exposed in the browser.

3. Dashboard Rendering

Once the token is generated, it's returned to Dashboard.js, Bold BI validates it, and the dashboard renders inside the React application. Users can then interact with the dashboard directly from the embedded page.

Verify the Dashboard

Confirm that:

The Python backend is running on port 8000.
The React application is running successfully.
The dashboard renders inside the React application.
Authentication occurs securely through the Python backend.

Best Practices for Production Deployment

After successfully embedding dashboards, the next step is to ensure your implementation is production-ready.

Secure token handling: Always generate tokens on the backend, use HTTPS, and set expiration limits.
Multi-tenant architecture: Assign roles and restrict access to ensure users only see relevant data.
Performance optimization: Use caching and optimize queries to improve load times.
UI and UX consistency: Make dashboards match your application's design and ensure responsiveness.
Monitoring and logging: Track usage and performance to detect and resolve issues early.

By following these practices, you ensure a stable, scalable, and secure analytics experience.

Why Choose Bold BI for Embedding Dashboards in a React App with Python?

Bold BI® enables secure analytics embedding in React applications using Python for token generation and authentication. With interactive dashboards, the Embedded SDK, and server-side authentication, developers can quickly integrate analytics into their applications.

Bold BI allows users to:

Embed dashboards directly into React workflows.
Access the dashboard securely with Python token generation.
Deliver interactive analytics experiences.
Simplify integration using the Embedded SDK.
Scale analytics across users and applications.
Support multi-tenant SaaS analytics.

Whether you are building SaaS platforms, enterprise applications, or customer portals, Bold BI provides a secure and scalable way to embed analytics into your React applications with Python.

Ready to get started? Download the React with Python sample, configure your embedding settings, and start delivering interactive dashboards. To learn more, explore the documentation, start a free trial, or request a demo to see how Bold BI fits your use case.

Frequently Asked Questions

1.
Why use Django for embedding dashboards?
Django securely generates embed tokens and manages backend authentication.
2.
Can I embed dashboards without a backend?
For secure token-based embedding, a backend should generate tokens and protect secrets.
3.
What is tokenGenerationUrl?
It is the backend API endpoint used by the SDK to generate secure embed tokens.
4.
Does Bold BI support React integration?
Yes, through its latest Embed SDK.
5.
Is this architecture scalable?
Yes, it supports enterprise and multi-tenant deployments.

Conversational Analytics in BI: Why It Matters in 2026

Bold BI by Syncfusion — Thu, 09 Jul 2026 08:23:22 +0000

TL;DR: Conversational analytics means users ask data questions in natural language and get answers fast. It reduces routine dashboard navigation and analyst dependency while making BI easier to use across teams.

Introduction

In many BI workflows, users still need to search through dashboards, apply filters, or wait for analysts to answer routine questions about their business data.

A dashboard may show the right metrics, but it does not always answer the question a business user has. Sales teams may want to understand why revenue dropped. Finance teams may need a quick variance explanation. Operations teams may want to identify what has changed since the last analytics period.

That is why conversational analytics matters. It enables users to ask questions in natural language, receive relevant insights, and continue exploring data through follow-up questions. With AI and natural language processing, conversational analytics can help teams find answers more efficiently while allowing analysts to focus on more complex analysis.

What is conversational analytics?

Conversational analytics is an approach that allows users to interact with data using natural language. Instead of writing traditional queries or navigating dashboards, users can ask questions such as:

“How many orders did we place in China last year, by month?”
“Which region has the highest growth?”

The system interprets the request and returns:

Visualizations
Data summaries
Insights

This makes analytics accessible to a broader range of business leaders while helping teams explore data more efficiently.

Why organizations are adopting conversational analytics

BI tools often rely on static dashboards, predefined queries, and technical expertise, making it harder to adapt to dynamic business questions and quickly find answers.

Conversational analytics helps address these challenges by enabling organizations to:

Accelerate business decision-making: Users can ask questions in natural language and receive timely insights, helping teams respond more quickly to changing business conditions.
Expand self-service analytics: Business leaders can independently explore data through conversational interactions, reducing routine requests to analysts.
Improve productivity across teams: Routine business questions can be answered more efficiently, allowing analysts to focus on strategic analysis, data modeling, and advanced analytics.
Explore data more naturally: Users can refine questions through follow-up conversations, making it easier to investigate trends, understand performance changes, and uncover insights.
Increase analytics adoption: By simplifying how users interact with data, conversational analytics encourages broader BI usage across departments and supports more data-driven decision-making.

Conversational analytics helps organizations access insights more easily and make faster, smarter decisions by overcoming the limitations of traditional BI.

Challenges to consider before adoption

Before implementing conversational analytics, organizations should carefully evaluate potential challenges to ensure successful adoption and maximize business value. Key considerations include:

Data quality issues: Conversational analytics is only as reliable as the data it analyzes. Inaccurate or incomplete data can cause misleading insights.
Security and privacy concerns: Organizations must ensure sensitive data is protected through proper access controls and compliance measures.
AI accuracy and reliability: AI may occasionally misinterpret queries or provide inaccurate results.
User trust and adoption: Employees may be hesitant to rely on AI-generated insights without transparency and validation mechanisms.
Integration complexity: Connecting conversational analytics with existing data sources, BI tools, and business systems can require significant effort.

For the end user, these challenges are invisible: they simply ask questions and get answers more efficiently.

Key features of conversational analytics

Conversational analytics combines AI and natural language technologies to make data exploration more intuitive, interactive, and accessible. Some of its key features include:

Natural language processing (NLP): Enables users to ask questions in plain language and allows the system to understand and interpret human speech or text.
Intent recognition: Identifies the purpose behind a user's query and delivers relevant insights based on the user's goals.
Context awareness: Maintains conversation history, allowing users to ask follow-up questions without repeating previous information.
Sentiment analysis: Detects emotions, opinions, and overall sentiment within conversations to help organizations better understand customer experiences.
AI-powered insights: Goes beyond answering questions by automatically identifying trends, anomalies, patterns, and opportunities within data.
Topic extraction: Identifies recurring themes and discussion topics, helping organizations uncover common issues, interests, and trends.
Data visualization and analytics: Presents data through charts, graphs, dashboards, and visual dashboards that make findings easier to understand and act on.

How conversational analytics differs from traditional BI and NLQ

Conversational analytics goes beyond traditional BI and natural-language querying by enabling context-aware, multistep data exploration through natural conversations, helping users refine questions and uncover deeper insights.

Aspect	Traditional BI	Natural Language Querying (NLQ)	Conversational analytics
Interaction	Dashboards	Single natural language query	Natural language conversations
Scope	Predefined analysis	Single question and response	Continuous exploration through follow-up questions
Context Awareness	Limited	Limited	Context-aware and guided
Accessibility	Often requires BI knowledge	Accessible to business users	Accessible to a broader range of users (with appropriate permissions)
Flexibility	Predefined insights	Limited to the submitted query	Dynamic and iterative exploration
Dependency on Analysts	Higher	Moderate	Reduced
Outcome	Dashboards	Query results	Insights with follow-up exploration

Note: In many BI platforms, NLQ is a feature that enables users to ask individual questions in plain language. Conversational analytics builds on that capability by supporting context-aware, multi-turn interactions that help users explore data through an ongoing dialog.

Real-world use cases of conversational analytics

Conversational analytics is used across industries and business functions.

Sales performance analysis

Sales teams often need quick visibility into pipeline performance and deal trends across regions and time periods. Conversational analytics enables leaders to ask questions like “Why did the pipeline value drop in Q2?” or “Which region underperformed?” and get immediate answers. This helps identify root causes faster, improve forecasting accuracy, and prioritize high-impact opportunities.

Conversational analytics in Sales

Distributor delivery performance analysis

Operations teams often need to monitor distributor performance and track delivery reliability across locations and time periods. Conversational analytics enables users to ask questions like “Which region has the lowest on-time delivery rate?” or “Which distributor handled the highest shipment volume?” and get instant answers. This helps uncover performance gaps, optimize logistics operations, and strengthen supply chain efficiency.

Conversational analytics in Distributor Delivery Performance Dashboard

Financial and invoice analysis

Finance and project management teams often need timely visibility into revenue generation, billable utilization, and client profitability across projects. Conversational analytics enables users to ask questions like “Which clients generated the most revenue?” or “How have billable hours changed over time?” The answers help them monitor financial performance, optimize resource allocation, and improve invoicing accuracy.

Conversational analytics in Finance

How to evaluate conversational analytics platforms

To get the most value, organizations should:

Start with clear business questions: Focus on common use cases like revenue tracking, customer churn, or operational performance.
Ensure high-quality data: Accurate insights depend on clean, well-structured data sources.
Combine with existing BI tools: Conversational analytics works best when paired with dashboards, not as a replacement.
Encourage user adoption: Train teams to ask better questions and explore data actively.

How Bold BI supports conversational analytics

Conversational analytics becomes more valuable when it is built on governed data, understands business terminology, and provides meaningful responses instead of simple keyword matches. Bold BI® supports this experience through AI-powered capabilities that help users explore data using natural language while maintaining enterprise governance.

1. Ask questions in Natural Language with Unified AI Agent

The Unified AI assistant allows users to interact with connected data sources using everyday language instead of manually searching dashboards or writing queries. Users can ask questions such as "How did quarterly sales perform?" or "Compare revenue across regions," and receive contextual responses with supporting visualizations.

The conversational experience also supports follow-up questions, enabling users to continue exploring trends, comparisons, and business metrics without restarting their analysis.

Unified AI Assistant

2. Improve question understanding with AI Synonyms

Organizations often use different business terms to describe the same data. Bold BI allows administrators to configure column and value synonyms so the AI can recognize alternative names, abbreviations, and business-specific terminology.

This improves intent recognition and helps conversational analytics deliver more accurate responses, even when users phrase questions differently.

3. Generate widgets from natural language

Beyond answering questions, Bold BI can transform natural language prompts into visualizations. The AI Copilot automatically recommends appropriate charts based on the requested analysis, making it easier for business users to understand trends, comparisons, and key metrics without manually designing dashboards.

AI Copilot in Bold BI

4. Create dashboards from a single prompt

For broader analysis, the prompt-to-dashboard feature converts a natural language prompt into a fully functional, interactive dashboard. Instead of creating multiple widgets individually, users can describe the information they need, and Bold BI automatically builds a dashboard connected to the relevant data.

This helps accelerate dashboard creation while making analytics more accessible to both technical and nontechnical users.

Prompt to dashboard

5. Explain Insights with AI-Generated Narrations

Bold BI also supports AI-generated narrations that summarize dashboards and widgets in plain language. These explanations help users quickly understand important trends, anomalies, and performance metrics without interpreting every visualization individually.

Combined with conversational analytics, AI-generated narrations make insights easier to find, share, and act upon across business teams.

Smart narration for data clarity

Together, these capabilities help organizations bring conversational analytics into the full analytics lifecycle, from asking questions and exploring insights to creating dashboards and embedding analytics into business applications.

Ready to make conversational analytics easier to explore?

Modern BI teams need more than dashboards. They need a governed analytics experience where users can make natural language inquiries, ask follow-up questions, and understand data with less friction.

With Bold BI®, organizations can utilize conversational analytics through AI capabilities that make data exploration more accessible across teams.

Try Bold BI with a 30-day free trial or request a personalized demo to see how conversational analytics can support your analytics workflow.

Frequently asked questions

1. What is conversational analytics?
Conversational analytics is AI that allows users to interact with data using natural language to quickly get insights.
2. How does conversational analytics work?
It uses NLP and AI to interpret user queries, retrieve relevant data, and generate insights.
3. How is it different from traditional BI?
Traditional BI relies on dashboards, while conversational analytics allows direct interaction with metrics using natural language.
4. What is the difference between conversational analytics and NLQ?
NLQ handles single queries, while conversational analytics supports continuous, context-aware interactions.

Integrate MySQL with Elasticsearch using Bold Data Hub

Bold BI by Syncfusion — Thu, 09 Jul 2026 08:17:45 +0000

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

Introduction

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

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

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

Benefits of integrating MySQL and Elasticsearch data for analytics

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

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

Challenges of integrating MySQL and Elasticsearch

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

How Bold Data Hub simplifies data integration

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

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

Integrate MySQL with Elasticsearch data using Bold Data Hub

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

Step 1: Prepare the data sources

Before integration, ensure that:

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

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

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

Step 2: Create a data pipeline in Bold Data Hub

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

version: 1.0.1

plugins:

extractors:

- name: mysql

connectorname: MySQL

config:

host: localhost

port: 3306

database: sales_db

username: root

password: ********

This configuration extracts transactional data from the MySQL database.

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

Step 3: Add Elasticsearch as a data source

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

Example configuration:

version: 1.0.1

plugins:

extractors:

name:     elastic_search

connectorname: Elasticsearch

config:

host: http://localhost:9200

index: product_feedback

username: elastic

password: ********

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

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

Step 4: Save the datasets

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

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

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

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

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

Joining tables

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

Step 1: Open the data source editor

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

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

Step 2: Identify the common fields

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

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

Step 3: Define the join condition

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

Example:

mysql.products.product_id = elasticsearch.feedback_id

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

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

Step 4: Preview the combined datasets

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

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

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

How to create a dashboard using MySQL and Elasticsearch data

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

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

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

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

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

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

[elementor-template id="150021"]

Start building unified analytics today

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

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

Frequently asked questions

1.
What data sources does Bold Data Hub support?

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

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

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

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

Bold BI Wins 23 Badges in the G2 Summer 2026 Awards

Bold BI by Syncfusion — Mon, 06 Jul 2026 08:19:50 +0000

TL;DR: The G2 Summer 2026 Awards recognized Bold BI with 23 badges across Embedded Business Intelligence, Business Intelligence, and Analytics Platform categories. Based on verified customer reviews, Bold BI earned multiple Leader, High Performer, and Momentum Leader distinctions for excellence in usability, implementation, customer satisfaction, and embedded analytics. These recognitions reflect the platform's ability to help organizations deliver scalable, secure, and data-driven analytics experiences.

Introduction

We are excited to announce that Bold BI® has earned 23 badges in the G2 Summer 2026 reports across Embedded Business Intelligence, Business Intelligence, and Analytics Platform categories.

These badges are based on verified G2 reviews and reflect how organizations use Bold BI to build dashboards and embed analytics into their applications. Each badge reflects customer-rated performance in areas such as usability, implementation, and support.

As embedded analytics continue to play a critical role in modern business applications, these achievements reinforce our commitment to delivering meaningful value and helping organizations transform data into actionable insights.

Bold BI Earns G2 Summer 2026 Recognition

Our customers are at the heart of every recognition we receive. Based on verified G2 reviews, Bold BI earned 23 badges in the G2 Summer 2026 reports across Embedded Business Intelligence, Business Intelligence, and Analytics Platform categories.

This recognition reflects customer experiences with Bold BI's usability, implementation, support, and embedded analytics capabilities. From Leader and High Performer distinctions to Momentum Leader recognition, the badges highlight our continued commitment to delivering scalable analytics solutions that help organizations make data-driven decisions with confidence.

Let's take a closer look at the badges Bold BI earned across each category.

Analytics Platforms G2 Badges: Consistent Performance Across Markets

Bold BI delivered strong performance this season, earning recognition across global, regional, and business segments. These badges reflect consistent customer satisfaction and growing adoption across diverse markets.

Here's a breakdown of the badges earned in the Analytics Platform category.

Badge	Recognition
Summer 2026 Americas	High Performer
Summer 2026 Asia	High Performer
Summer 2026 Asia Pacific	High Performer
Summer 2026 Enterprise	High Performer
Summer 2026	High Performer
Summer 2026 Mid-Market	High Performer
Summer 2026 Americas Small Business	High Performer
Summer 2026 Asia Small Business	High Performer
Summer 2026 Asia Pacific Small Business	High Performer
Summer 2026 Small Business	High Performer

Why they matter: These badges highlight Bold BI's ability to deliver consistent value across global and regional markets, as well as enterprise, mid-market, and small business segments.

Business Intelligence G2 Badges: Recognized as a Leader

Bold BI continues to be recognized for delivering intuitive dashboards, flexible reporting, and a seamless analytics experience. Its ability to address diverse business needs has earned recognition across multiple market segments.

Here's a breakdown of the badges earned in the Business Intelligence category.

Badge	Recognition
Summer 2026	Leader
Summer 2026 Mid-Market	Leader
Summer 2026 Small Business	Leader

Why they matter: These badges reinforce Bold BI's position as a trusted analytics solution that delivers consistent value, usability, and performance across different business segments.

Embedded Business Intelligence G2 Badges: Strong Global Presence

Bold BI's embedded analytics capabilities continue to gain global recognition, helping organizations integrate dashboards directly into their applications and workflows. These badges highlight the platform's flexibility, scalability, and performance across various regions and business segments.

Here's a breakdown of the badges earned in the Embedded Business Intelligence category.

Badge	Recognition
Summer 2026 Americas	High Performer
Summer 2026 Asia	High Performer
Summer 2026 Asia Pacific	High Performer
Summer 2026 EMEA	High Performer
Summer 2026 India	High Performer
Summer 2026 Mid-Market	High Performer
Summer 2026 Americas Small Business	High Performer
Summer 2026 Overall	Leader
Summer 2026 Small Business	Leader
Summer 2026 Overall	Momentum Leader

Why they matter: These recognitions demonstrate Bold BI's strength in delivering scalable embedded analytics experiences across industries and regions while continuing to earn customer trust through innovation, performance, and customer satisfaction.

Why Leading Organizations Choose Bold BI

The G2 Summer 2026 recognitions are backed by feedback from customers who use Bold BI to power analytics across their organizations. User reviews consistently highlight several areas where Bold BI delivers value and enables teams to succeed.

Customers appreciate Bold BI for:

Exceptional customer support: Get fast, reliable assistance from a responsive support team that helps organizations navigate implementation, customization, and deployment with confidence.
Ease of use: Create and manage dashboards effortlessly with an intuitive interface that simplifies analytics for both technical and business users.
Flexible data integration: Connect to a wide range of data sources and unify business data to create comprehensive, real-time analytical views.
Advanced dashboard customization: Build tailored analytics experiences using customizable dashboards, widgets, and visualizations that align with specific business requirements.
Seamless embedded analytics: Integrate interactive dashboards directly into applications, portals, and workflows to deliver insights where users need them most.
AI-powered insights: Ask questions in plain English, generate dashboards faster, and turn complex data into clear summaries with Bold BI’s AI capabilities.
White-labeling capabilities: Customize dashboards, colors, fonts, logos, and branding to deliver analytics that look and feel like your own product.

In addition to earning recognition in the G2 Summer 2026 reports, Bold BI continues to help organizations overcome real-world analytics challenges and achieve meaningful business outcomes.

Customer Success Stories

Customer success stories demonstrate how organizations use Bold BI to simplify analytics, improve operational efficiency, and make better business decisions.

Backwind LLC: Making Business Data More Accessible Through Interactive Analytics

Backwind LLC needed a way to transform business data into meaningful insights that stakeholders could easily access and understand. Using Bold BI, the company implemented interactive dashboards that simplified data analysis, improved visibility into key metrics, and enabled more informed, data-driven decision-making.

Challenge: Limited visibility into business performance and difficulty accessing actionable insights.
How: Implemented interactive dashboards and visual analytics to transform complex business data into meaningful insights.
Why it matters: Enabled stakeholders to make faster, more confident business decisions.

Precision Systems: Reducing Manual Reporting with Embedded Dashboards

Precision Systems wanted to reduce the effort required for manual reporting while giving teams faster access to operational insights. By embedding Bold BI dashboards into existing workflows, the organization centralized reporting and made real-time analytics readily available across teams.

Outcome: Reduced manual reporting and improved access to operational analytics.
How: Embedded interactive dashboards and centralized reporting to provide real-time visibility into critical business data.
Why it matters: Increased operational efficiency by allowing teams to spend less time preparing reports and more time acting on insights.

Explore more real-world customer success stories on the Bold BI Case Studies page.

What Customers Say About Bold BI

Powerful and Flexible Analytics Platform with Intuitive Design
"Bold BI provides a powerful and flexible analytics platform that is easy to implement and integrate with various data sources. The dashboard designer is intuitive, allowing both technical and non-technical users to build meaningful visualizations quickly. Its ability to connect directly to databases, APIs, and web services makes it highly suitable for enterprise-level reporting. I also appreciate the strong customization options, embedding capabilities, overall performance with large datasets, and responsive customer support." By Mohamed Zulfadli A., G2

Simple, Powerful and Easy to Use
"I've been using Bold BI regularly, and it's been easy to set up and use. It has a clean, intuitive interface while still allowing you to build powerful dashboards and reports. I also like how easily it connects to a wide range of data sources and supports embedded analytics. Whether you're building for a small team or a large organization, it provides a practical, reliable, and flexible analytics experience." By Muhdin M., G2

Read more verified customer feedback on the Bold BI G2 Reviews page.

Turning Recognition into Better Analytics Experiences with Bold BI

We sincerely thank our customers and community for their continued support and valuable feedback. These 23 badges reflect real experiences from users who rely on Bold BI® to power their analytics workflows.

This recognition reinforce our commitment to delivering an intuitive, scalable, and high-performing analytics platform. Each milestone motivates us to continue improving usability, performance, and embedded analytics capabilities.

As organizations increasingly rely on data to drive growth, Bold BI remains focused on helping teams create powerful analytics experiences that deliver measurable value.

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DEV Community: Bold BI by Syncfusion

Top 26 Sales Dashboard Examples for Data-Driven Sales Teams

Introduction

What is a Sales Dashboard?

Spreadsheets vs. Sales Dashboards

Why Sales Dashboards Matter

Essential Sales KPIs to Monitor

Revenue Performance

Pipeline Stage

Conversion Rate

Average Deal Size

Customer Retention Rate

Customer Acquisition Cost

Customer Lifetime Value

Forecasted Revenue

26 Sales Analytics Dashboard Examples to Try Out in 2026

Salesforce Opportunity Dashboard

HubSpot Deals Report Dashboard

Pipedrive Sales KPI Dashboard

Pipedrive Sales Activities Dashboard

Freshsales Pipeline Performance Dashboard

Insightly Opportunities Analysis Dashboard

Copper Sales Opportunities Dashboard

PipelineCRM Deals Analysis Dashboard

Revenue Analysis Dashboard

ActiveCampaign Deals Performance Dashboard

Zendesk Sales Performance Dashboard

Sales Leads Tracking Dashboard

Sales Opportunities Dashboard

Sales Manager Summary Dashboard

Sales Analysis Dashboard

Sales Performance Dashboard

Sales Activity Tracker Dashboard

SingleStore Sales Funnel Analytics Dashboard

Stripe Subscription Performance Dashboard

Chargebee Subscription Overview Dashboard

Customer Profile Overview Dashboard

Shopify Product Monitoring Dashboard

Keen E-Commerce Sales Analysis Dashboard

Ticket Sales Analysis Dashboard

Financial Analysis Dashboard

Northwind Products and Suppliers Dashboard

How to Choose the Right Sales Dashboard for Your CRM

1. What CRM or platform are you already using?

2. Who's the primary audience?

How Bold BI Brings it All Together

Frequently Asked Questions

What is a sales dashboard?

Why are sales dashboards important for sales teams?

What KPIs should be included in a sales dashboard?

How do sales dashboards improve forecasting accuracy?

What are the benefits of using real-time sales dashboards?

How do sales dashboards differ from reports and spreadsheets?

How can Bold BI help create sales dashboards?

How do I choose the right sales dashboard example for my CRM?

Are these sales dashboard templates free to use?

AI Credits in Bold BI: How to Purchase, Manage & Track Usage

Introduction

Why manual AI usage tracking doesn't scale

Understanding AI credits in Bold BI

Where AI credits are typically used

How many AI credits will you need?

AI credit plans and purchasing options

Free plan: 500 credits every month

Purchasing additional AI credits

Subscription plans

Add-on credits

Free plan vs. paid plan

AI governance and usage control

AI credits for embedded deployments

How to use AI credits in Bold BI

Tracking usage before it becomes a problem

Common issues and how to resolve them

Who benefits most from Bold BI’s AI credit management feature

1. License owners and admins

2. Teams running frequent AI Assistant or Q&A sessions

3. Organizations rolling out dashboard summarization at scale

Final thoughts

Frequently asked questions

How are AI credits managed in Bold BI?