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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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Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

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The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

The gap is widening between what teams spend on observability tools and the value they receive amid surging data volumes and budget pressures, according to The Breaking Point for Observability Leaders, a report from Imply ...

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

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

The gap is widening between what teams spend on observability tools and the value they receive amid surging data volumes and budget pressures, according to The Breaking Point for Observability Leaders, a report from Imply ...