Dashboards alone aren’t cutting it. The market’s moving toward something new: data apps, live collaboration, and AI that works the way teams actually work.

See what's driving the rise of Next Gen BI, how Sigma earned a top debut on the Gartner Magic Quadrant, and what’s next for our roadmap.

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Welcome to BIPro 105 — Special Edition

Yes, you're seeing that right: two issues in a single week.
We know that’s not our usual cadence. But this moment in BI isn’t usual either.

As Generative BI accelerates and reshapes how teams interact with data, we’re testing a new format: deep dives that cut through the noise and go straight to what matters. This isn't just about keeping up with trends, it’s about keeping up with the urgency we hear from BI engineers, data leaders, and product teams across the ecosystem.

We’re paying attention to what’s shifting, and this edition is us responding in real time.

Because sometimes what you need isn’t more content, it’s more context.

🔍 Deep Dive: Ask, Don’t Analyze, The Gen BI Era Is Here

This isn’t your average BI update. Thisis a special edition that explores one of the most critical transformations in analytics today: the shift from dashboards to dialogue.

We’re moving past static reports and into natural language interactions, where insights are surfaced through simple questions, and delivered with speed, clarity, and relevance. This deep dive explores:

If you're thinking about how to scale insight, reduce overhead, or future-proof your stack, this one's for you.

👉 It’s not just a trend. It’s a turning point.

✉️ Have tips or tools to share? Reply and contribute to our next edition.

Cheers,

Merlyn Shelley

Growth Lead, Packt

Empowering teams with live data and actionable stories, not static charts.

“Today’s diversity and scale of BI and analytic use cases require new technology, development, and deployment options that can span from on premises to in the cloud and support data and analytic workflows at high scale and security,” said Bill Hostmann, Research Fellow, Dresner Advisory Services.

This evolving landscape demands more than just traditional tools and platforms. Companies need systems that not only adapt to diverse and rapidly changing data needs, but also offer effortless scalability, security, and real-time features. Gen BI rises to meet these challenges by eliminating the barriers between users and data. It empowers decision-makers with direct, dynamic access to real-time insights, breaking down the complexity and delay often associated with older, static BI systems. With Gen BI, organizations can move faster, make smarter decisions, and stay ahead of the competition in a world where data drives success.

The BI Bottleneck That No One Wants to Admit

Have you ever spent weeks building a dashboard, only to find out no one really used it? You're not alone.

Thousands of BI teams across the globe suffer from the same dilemma. So, Forrester found that 74% of companies say they want to be data-driven, but only 29% actually manage to turn their analytics into action. Sounds wild, right? It’s not bad data or ugly dashboards that are the issue, it’s the whole way we’re interacting with data.

People ask for dashboards, self-serve portals... but what they really need are stories. You know, something they can actually use.

So, why are we still giving them static dashboards? Why are we forcing people to analyze when they just want to ask?

The Stagnation of Static Dashboards

1. Overengineering Without Outcomes

We’ve all been there. Weeks of development go into designing dashboards that look impeccable; interactive charts, custom filters, slicers, drill-downs. Yet when it’s finally shared, what happens? Users export it to Excel and send follow-up emails asking for help making sense of the numbers.

According to a 2022 survey by NewVantage Partners, over 97% of companies say they are increasing investment in data initiatives, but only 26.5% report having a data-driven culture. The implication? We're building tools faster than people are learning to use them effectively.

The real irony is that dashboards, once a symbol of democratized data access, are now gatekeepers of understanding. They often reflect more about what data is available than what insight is needed. Like a well-designed PowerPoint deck with no compelling story, static dashboards are attractive but functionally hollow.

2. Cognitive Load and Interpretation Gaps

Most business stakeholders? They’re not data analysts. They’re decision-makers, trying to move fast.But dashboards expect them to think like statisticians: select dimensions, define filters, evaluate trends, avoid correlation fallacies.

It’s like asking a pilot to diagnose engine issues mid-flight using a panel of unlabeled dials. The tools assume a level of comfort with data vocabulary that simply doesn’t exist across the business. Research from Gartner shows that data literacy remains a top barrier, with32% of business executives notfeeling confident interpreting and using data effectively.

The result? Misinterpretations, overreliance on analysts, and critical delays in decision-making. We’re creating insight silos because we’re forcing everyone to self-navigate complex BI environments.

3. Too Many Tools, Not Enough Decisions

It starts small, one team swears by Power BI, another sticks with Tableau, marketing goes with Looker, and ops builds their own dashboard system. Fast forward a year, and you’ve got five BI tools, fifteen redundant dashboards, and zero alignment.

Tool sprawl introduces inconsistency and confusion. Metrics are defined differently across platforms, dashboards are out of sync, and trust in data erodes. A McKinsey report found that an average of 30% of time is lost due to ineffective data governance across platforms. This leads to fragmented decision-making, inconsistent data access, and rising technical debt, all of which create inefficiencies and prevent teams from focusing on high-value tasks. As a result, organizations struggle to make timely, data-driven decisions, slowing down overall productivity.

Gartner predicts that by 2025, 70% of companies will shift from big data to small, wide data, focusing on context-rich, targeted datasets that actually mean something. And guess what? That’s where Generative BI really shines.

Rather than building yet another dashboard, the shift should be toward building centralized semantic layers, shared definitions, and flexible AI interfaces. Gen BI provides that bridge: fewer tools, smarter access, and consistent insight delivery.

Enter Gen BI: A Paradigm Shift

If traditional BI was about delivering data, then Generative BI (Gen BI) is about delivering understanding. It’s a big shift in how businesses interact with information. Ratherhaving to dig through dashboards, Gen BI lets you just ask what you need to know, in plain language.

Let’s picturethis: You find yourself fiddling with 12 filters to track regional sales trends, or else you could just ask, “How did the Northeast do compared to the Midwest last quarter, and why?” Having to see not just a static chart or spreadsheet, the Gen BI system gives you a clear, conversational answer, with live data and visuals that actually help make sense of it.

It’s a complete transformation, BI moves from being a self-service thing to a self-explaining tool. No more clicks, just conversations. No more endless exploration, just clear guidance.

What Powers Gen BI?

Let’s break down what’s really driving Gen BI and why it’s such a game-changer.

⬛ First, we have Large Language Models like GPT-4o, Claude, or Mistral. These aren’t just fancy algorithms. This is like having a super helpful mentor or a resource personwho actually understands what you're asking. It's more like not gettinglost in complex dashboards or endless data sheets, rather you just ask your question out loud, and the model turns it into a meaningful answer. Think about how much time we spend trying to decode charts or filter through endless rows of data. LLMs take that frustration away by giving you an answer that makes sense, quickly.

⬛ Then there’s the Semantic Layer. This is a huge one, especially when you're working with a lot of different teams and data sources. The issue we’ve all run into at some point is getting conflicting numbers or terms that just don’t align. Tools like dbt, Cube.dev, and LookML fix this by ensuring your data and business logic are consistent across all tools. It’s like everyone finally speaking the same language. When you ask a question, you don’t get a different answer depending on where it came from.

⬛ Next up, RAG systems. These systems pull in fresh, live data from your structured sources in real time. No more working off last week's numbers or waiting for that quarterly report to be updated. If you’re asking for data now, you’re getting it now. This kind of speed is critical for real-time decision-making, and it’s one of those things that makes all the difference in a fast-paced environment.

⬛ And here’s where it gets really cool: Natural Language Query (NLQ) engines. I’m sure you’ve heard of tools like Seek AI, ThoughtSpot, or Vanna.ai. These let you ask questions in plain English, no need to understand SQL or spend hours building out queries. You can just ask, “What’s my sales this month?” and it figures out the exact query needed to pull that info. That’s huge for anyone who isn’t a data scientist but still needs quick, actionable insights.

In a nutshell, Gen BI makes working with data more intuitive, more aligned with your business needs, and way faster. It’s taking all those little annoyances and friction points we’ve dealt with for years and smoothing them out. We’re moving from static, report-heavy environments to something far more dynamic where you ask a question and get exactly what you need, when you need it.

Who’s Leading the Charge?

You’ve probably been hearing a lot about Gen BI tools lately, but who's actually making it happen in the real world? Well, the big players are already jumping in, and they’re seeing some pretty impressive results.

⬛ Take Microsoft Copilot for Power BI. Ever wish you could just ask your BI tool a question in plain English and get a report back instantly? That’s exactly what Microsoft is offering now. No more sifting through endless filters and dashboards. You can just type your question in natural language, and Copilot will generate a report for you right there within Power BI. It’s almost like having a personal assistant who just gets what you need. How much time could that save you in a typical workday?

⬛ Then there’s Narrative BI, a tool that turns your raw metrics into actual stories. Sounds simple, but it's a game-changer. Imagine getting a summary of your key metrics, not as dry numbers but as a story that’s easy to digest. And it doesn’t stop there, it delivers this info right to you via Slack or email. It’s like having an automatic briefing in your inbox whenever you need it. If you’re someone who’s constantly hopping between meetings, wouldn't it be great to just have those insights show up where you already work, instead of wasting time logging into a tool?

⬛ And let’s not forget Airbnb’s internal AI layer. They’ve built something pretty powerful here. Product managers and marketers can ask open-ended questions about live data and get summarized insights in response. It's not about just pulling static reports from a database. It’s about asking real-time questions and getting actionable answers based on the most current data. Think about how often you’ve had to make decisions based on stale or incomplete data, Airbnb’s system is changing that, giving their teams the ability to act on insights as they come in.

These companies aren’t just experimenting with Gen BI, they’re leading the way, showing us what’s possible when data becomes more accessible, faster, and smarter. It’s about freeing up time, making better decisions, and ultimately, getting things done more efficiently. Who wouldn’t want that?

Read the full article on Packt’s Medium. If you’re new, make sure to follow our Medium handle and subscribe to our newsletter for more insights like this!

See you next time!