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From Insight to Impact: How Embedded BI Powers Transformation

Jason Beres
Infragistics

With data, we can drive digital transformation by turning insights into action. At the heart of any meaningful digital transformation strategy lies a critical component that often gets overlooked: Embedded Business Intelligence (BI).

I've spent the last two decades helping organizations build great software. I've seen trends come and go. But if there's one truth that's remained constant, it's this: data wins arguments. It removes guesswork and replaces gut instinct with data-based insights. Embedded BI puts the power of data directly where it belongs: into the hands of your users, in the flow of their work, and in context.

Decision-Making in Context

One of the biggest challenges we face in the DevOps, IT, and software spaces is decision latency. We have the data, but it's siloed, locked away in legacy BI systems, behind login walls, or buried in spreadsheets.

With embedded BI, you can remove the "click tax." No more hopping from your operational app to a standalone analytics tool and back. No more trying to remember a URL or searching for the right dashboard. With embedded analytics, your users stay in their flow and make smarter decisions, faster. In fact, in a recent Reveal survey, 75% of respondents cited informed decision-making as the top reason they adopted embedded analytics.

Productivity without Context Switching

Many organizations today use multiple BI tools. That might work theoretically, but in practice, it worsens productivity. In fact, task switching has been shown to reduce productivity by up to 40%.

Embedded BI solves this by integrating visual analytics seamlessly into your existing applications, whether that's a web app, mobile platform, or internal dashboard. Dashboards become the centerpiece of conversations across departments, for everyone from dev teams to for sales, marketing, and ops. That's because good design, in-context insight, and usability breed adoption.

Embedded BI as a Competitive Differentiator

When you're building commercial software, user experience is everything. A clunky UI can be a dealbreaker, even if the software contains powerful features. Embedded BI enriches your application's feature set, as well as enhancing the perceived and real value of your product.

According to Forrester, firms with advanced, insight-driven capabilities are 8.5x more likely to grow revenue by 20% or more. Why? Because customers want visibility and they want self-service. They'll choose a modern app with built-in analytics over a legacy tool that requires exporting to Excel every time.

Embedded BI helps ISVs go to market with beautifully integrated, fully interactive analytics that feel native, not bolted on.

From Reporting to Culture

BI success is as much cultural as it is technical. You don't start by giving everyone access to a firehose of dashboards. You start small, perhaps with read-only dashboards, then allow users to customize a chart. Later, give them the power to build their own visualizations.

We've seen this journey play out across our own company. It starts at the top. When executives use dashboards in meetings, others follow. Suddenly, data becomes a common language, not a specialized skill. That's where digital transformation actually takes root — in behavior, not code.

Deloitte research supports this: 82% of companies that democratize analytics across their workforce exceed their business goals. Compare that to just 48% for companies with siloed analytics efforts. Empower your people, and the results will follow.

Developer-First Deployment

For those of us building and maintaining software, we need tools that respect our tech stack. Embedded analytics tools run inside your app, on your infrastructure, and under your control. You can be up and running in a day with dashboards that may have taken months to create if you built your own analytics, rather than embedding them.

The Bottom Line

Embedded BI isn't just a feature. It's a strategy. It's the foundation for a data-literate culture, a smarter workforce, and software that delivers not just utility, but insight.

If you're looking to drive adoption, improve your UX, increase revenue, and make your users feel empowered instead of overwhelmed, embedded analytics is an essential.

If you're just starting your analytics journey, keep it simple. Start with one dashboard. Let it spark curiosity. Then iterate. Before long, you'll realize that digital transformation wasn't about tools, it was about bringing the power of data to everyone.

Jason Beres is COO and Senior Software Development Executive at Infragistics

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From Insight to Impact: How Embedded BI Powers Transformation

Jason Beres
Infragistics

With data, we can drive digital transformation by turning insights into action. At the heart of any meaningful digital transformation strategy lies a critical component that often gets overlooked: Embedded Business Intelligence (BI).

I've spent the last two decades helping organizations build great software. I've seen trends come and go. But if there's one truth that's remained constant, it's this: data wins arguments. It removes guesswork and replaces gut instinct with data-based insights. Embedded BI puts the power of data directly where it belongs: into the hands of your users, in the flow of their work, and in context.

Decision-Making in Context

One of the biggest challenges we face in the DevOps, IT, and software spaces is decision latency. We have the data, but it's siloed, locked away in legacy BI systems, behind login walls, or buried in spreadsheets.

With embedded BI, you can remove the "click tax." No more hopping from your operational app to a standalone analytics tool and back. No more trying to remember a URL or searching for the right dashboard. With embedded analytics, your users stay in their flow and make smarter decisions, faster. In fact, in a recent Reveal survey, 75% of respondents cited informed decision-making as the top reason they adopted embedded analytics.

Productivity without Context Switching

Many organizations today use multiple BI tools. That might work theoretically, but in practice, it worsens productivity. In fact, task switching has been shown to reduce productivity by up to 40%.

Embedded BI solves this by integrating visual analytics seamlessly into your existing applications, whether that's a web app, mobile platform, or internal dashboard. Dashboards become the centerpiece of conversations across departments, for everyone from dev teams to for sales, marketing, and ops. That's because good design, in-context insight, and usability breed adoption.

Embedded BI as a Competitive Differentiator

When you're building commercial software, user experience is everything. A clunky UI can be a dealbreaker, even if the software contains powerful features. Embedded BI enriches your application's feature set, as well as enhancing the perceived and real value of your product.

According to Forrester, firms with advanced, insight-driven capabilities are 8.5x more likely to grow revenue by 20% or more. Why? Because customers want visibility and they want self-service. They'll choose a modern app with built-in analytics over a legacy tool that requires exporting to Excel every time.

Embedded BI helps ISVs go to market with beautifully integrated, fully interactive analytics that feel native, not bolted on.

From Reporting to Culture

BI success is as much cultural as it is technical. You don't start by giving everyone access to a firehose of dashboards. You start small, perhaps with read-only dashboards, then allow users to customize a chart. Later, give them the power to build their own visualizations.

We've seen this journey play out across our own company. It starts at the top. When executives use dashboards in meetings, others follow. Suddenly, data becomes a common language, not a specialized skill. That's where digital transformation actually takes root — in behavior, not code.

Deloitte research supports this: 82% of companies that democratize analytics across their workforce exceed their business goals. Compare that to just 48% for companies with siloed analytics efforts. Empower your people, and the results will follow.

Developer-First Deployment

For those of us building and maintaining software, we need tools that respect our tech stack. Embedded analytics tools run inside your app, on your infrastructure, and under your control. You can be up and running in a day with dashboards that may have taken months to create if you built your own analytics, rather than embedding them.

The Bottom Line

Embedded BI isn't just a feature. It's a strategy. It's the foundation for a data-literate culture, a smarter workforce, and software that delivers not just utility, but insight.

If you're looking to drive adoption, improve your UX, increase revenue, and make your users feel empowered instead of overwhelmed, embedded analytics is an essential.

If you're just starting your analytics journey, keep it simple. Start with one dashboard. Let it spark curiosity. Then iterate. Before long, you'll realize that digital transformation wasn't about tools, it was about bringing the power of data to everyone.

Jason Beres is COO and Senior Software Development Executive at Infragistics

Hot Topics

The Latest

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...

40% of organizations deploying AI will implement dedicated AI observability tools by 2028 to monitor model performance, bias and outputs, according to Gartner ...

Until AI-powered engineering tools have live visibility of how code behaves at runtime, they cannot be trusted to autonomously ensure reliable systems, according to the State of AI-Powered Engineering Report 2026 report from Lightrun. The report reveals that a major volume of manual work is required when AI-generated code is deployed: 43% of AI-generated code requires manual debugging in production, even after passing QA or staging tests. Furthermore, an average of three manual redeploy cycles are required to verify a single AI-suggested code fix in production ...

Many organizations describe AI as strategic, but they do not manage it strategically. When AI plans are disconnected from strategy, detached from organizational learning, and protected from serious assumptions testing, the problem is no longer technical immaturity; it is a failure of management discipline ... Executives too often tell organizations to "use AI" before they define what AI is supposed to change. The problem deepens in organizations where strategy isn't well articulated in the first place ...

Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...

Today's modern systems are not what they once were. Organizations now rely on distributed systems, event-driven workflows, hybrid and multi-cloud environments and continuous delivery pipelines. While each adds flexibility, it also introduces new, often invisible failures. Development speed is no longer the primary bottleneck of innovation. Reliability is ...

Seeing is believing, or in this case, seeing is understanding, according to New Relic's 2025 Observability Forecast for Retail and eCommerce report. Retailers who want to provide exceptional customer experiences while improving IT operations efficiency are leaning on observability ... Here are five key takeaways from the report ...

Technology leaders across the federal landscape are facing, and will continue to face, an uphill battle when it comes to fortifying their digital environments against hostile and persistent threat actors. On one hand, they are being asked to push digital transformation ... On the other hand, they are facing the fiscal uncertainty of continuing resolutions (CR) and government shutdowns looming near and far. In the face of these challenges, CIOs, CTOs, and CISOs must figure out how to modernize legacy systems and infrastructure while doing more with less and still defending against external and internal threats ...

Reliability is no longer proven by uptime alone, according to the The SRE Report 2026 from LogicMonitor. In the AI era, it is experienced through speed, consistency, and user trust, and increasingly judged by business impact. As digital services grow more complex and AI systems move into production, traditional monitoring approaches are struggling to keep pace, increasing the need for AI-first observability that spans applications, infrastructure, and the Internet ...

If AI is the engine of a modern organization, then data engineering is the road system beneath it. You can build the most powerful engine in the world, but without paved roads, traffic signals, and bridges that can support its weight, it will stall. In many enterprises, the engine is ready. The roads are not ...