Democratizing Data: The AI Tools Making BI Accessible to Everyone

Introduction

Exhausted  hearing about AI? For the past few years, I’ve felt overwhelmed by the constant stream of AI headlines—first it was generative AI, then large language models (LLMs), retrieval-augmented generation (RAG), and now the buzz is all about model context protocol (MCP) and agentic AI. What’s next?

Adding to this is the rapid evolution of cloud tools, which at times can feel like trying to keep your footing during an earthquake. But despite the noise, AI’s ubiquity means it’s showing up in more meaningful, applied ways across industries. Until recently, though, nothing in the AI landscape really stood out to me.

That changed this year with the introduction of Gemini in Looker and a host of AI enhancements in BigQuery. Having specialized in Google Cloud Platform (GCP) for the past five years, I finally felt the promise of AI becoming real—especially in the space I care most about: Business Intelligence (BI) and data warehousing.

One of the persistent challenges in BI is helping businesses make sense of their data. Raw data, by itself, isn’t very useful—mainly because the way it's ingested is rarely optimized for reporting. This is why data modeling is so critical: it shapes data into something analysis-ready. So the big question now is: how is AI influencing this space?

BigQuery: AI Gets Practical

BigQuery has seen a significant AI upgrade recently. A great example is the new Insights tab available on tables and views. This feature automatically surfaces key data patterns and generates suggested questions, each accompanied by a pre-written SQL query to kick off your analysis.

These AI-generated queries aren’t always perfect—you may need to tweak them to join additional tables or apply specific filters—but they offer a strong head start, especially for analysts who regularly write complex SQL.

But this is just scratching the surface.

Meet BigQuery Data Canvas

BigQuery Data Canvas is a new, visual and interactive workspace for data exploration. It replaces the traditional linear workflow with a Directed Acyclic Graph (DAG) approach, which allows you to:

• Branch off at any step to explore alternative perspectives
• Revisit earlier stages in your analysis
• Compare multiple outcomes simultaneously

This structure makes data exploration more flexible, iterative, and intuitive—especially when paired with Gemini’s AI capabilities. While it’s a shift from the traditional method of managing SQL queries in multiple tabs, I look forward to adapting and learning how it can streamline the process.

Looker: Conversational BI Comes to Life

On the Looker side, the integration of Gemini agents is a true game changer. Many business users just want to ask a question and get an answer—without having to run a report or build a dashboard. That’s now possible.

Conversational Analytics (Public Preview)

With Conversational Analytics, users can interact with their data using natural language. Just type in a question, and the Gemini agent generates a SQL query behind the scenes, then returns insights and next-step suggestions. You can even ask follow-up questions to drill deeper.

This works well because Gemini leverages Looker’s Explore layer, which is built on the LookML semantic model. This semantic layer is crucial—it provides a governed, business-friendly interpretation of raw data. It's the map Gemini follows to understand and contextualize the data it’s working with.

AI-Powered Development in Looker

Not a fan of writing LookML from scratch? Gemini can now assist with LookML code generation, saving time on model development. And for those unfamiliar with dashboard creation, the new Visualization Assistant allows users to build charts and reports simply by using prompts.

Together, these features lower the barrier to BI for non-technical users while speeding up workflows for technical teams.

Wrapping Up: AI + BI = Better Together

AI can’t work miracles unless your BI foundation is solid. That includes data modeling, quality, and semantic layers. As the old adage goes: Garbage In = Garbage Out—a truth I heard echoed by many data professionals at Google Cloud Next.

But we’re seeing a new narrative emerge: not just BI enabling AI, but also AI enhancing BI. For example, BigQuery now includes tools like data profiling, lineage tracking, and quality checks that boost data engineers’ productivity. AI is even helping unify unstructured and structured data into more coherent models.
 

So, what does this all mean? 

AI is bringing us closer to the long-held BI dream: democratizing data access for all users. It’s speeding up time-to-insight and reducing dependence on analysts for simpler queries. That’s a win—because it frees analysts to focus on higher-order questions where human creativity and domain expertise still reign.

For my clients, this evolution translates to immediate, tangible benefits. With tools like Gemini in Looker and BigQuery’s AI upgrades, even non-technical teams can unlock insights from their data with minimal friction. This empowers clients to make faster, smarter decisions without waiting on analysts or complex reporting pipelines. By streamlining workflows and reducing bottlenecks, these tools enable organizations to become more data-driven—driving better outcomes and giving them a competitive edge in their industries.

Looking ahead, AI-infused BI tools will only get stronger—potentially amplifying human capability by 10x. We may finally get over the hump of "understanding our own data" with ease.

And beyond that? Keep an eye on quantum computing—it’s poised to push these transformations even further.

As a first-ever recipient of the Google Public Sector Partner Expertise Badges in Data and Analytics and a preferred Google AI partner, At Appnovation we are committed to using cutting-edge technologies to empower organizations with actionable insights and drive meaningful progress—helping them navigate complex challenges and unlock new opportunities.