Is AI Analytics the Missing Link Between Business Users and Data Teams?

For years, enterprises have discussed data democratisation as if it were an inevitable end goal. An assumption was made that turning on dashboards and training the business would lead to insight following naturally. But according to Barry McCardel, Co-Founder and CEO of Hex Technologies, the reality has been much more complicated.

In the recent episode of the Don’t Panic, It’s Just Data podcast, McCardel joined host Kevin Petrie, VP Research and Head of Data Management at BARC, to talk about why access alone has never been enough. He also discussed how artificial intelligence (AI) is forcing the analytics community to rethink the purpose of data.

The conversation dives into a familiar issue: how can organisations empower non-technical users without compromising data trust or overwhelming the technical teams responsible for it?

“We’ve spent a decade pretending the problem was solved by self-service,” McCardel says. “But what we actually did was move complexity around instead of removing it.”

As AI becomes part of analytics platforms, that complexity is finally being addressed. This includes long-standing beliefs about roles, ownership, and teamwork.

Tracing many of the analytics issues faced by organisations in the present day, McCardel alludes to the early self-service BI, which promised that business users could explore data on their own. This was supposed to allow analysts and engineers to focus on more important tasks. In reality, the outcome often included duplicated logic, inconsistent metrics, and a widening trust gap between teams.

“Access without context is chaos,” McCardel tells Petrie. “If everyone can answer questions, but everyone answers them differently, you haven’t democratized anything; you’ve just created noise.”

This issue has grown more urgent as organisations expand. Different roles—data engineers, analysts, data scientists, and business stakeholders—approach data with distinct goals and skills. Traditional tools forced everyone into the same interfaces, often designed for one group while ignoring the needs of the others.

Petrie notes that many companies responded by adding layers of control, but this approach had drawbacks. Stricter guidelines slowed insight generation and pushed business users back into reliance on centralised teams.

McCardel argues that the main problem isn’t a lack of governance or tools but a lack of shared understanding. “We’ve treated analytics like a handoff,” he explains. “The data team builds it, the business consumes it. That model doesn’t work when questions are fluid, and decisions are continuous.”

He believes AI is revealing the limits of that model and providing a path forward.

Also Watch: “Data Teams Suffer from Fragmentation” | Charles Schaefer @ Big Data LDN 2025

While much of the industry conversation about AI in analytics focuses on automation and natural language querying, the CEO of Hex is cautious about viewing AI as a quick fix. “If AI just gives you faster wrong answers, that’s not progress,” he points out.

Instead, he presents AI as a bridge that helps different roles collaborate in the same analytical space without flattening their expertise. In this view, AI helps translate: it turns business questions into structured analysis, brings relevant context to the surface, and makes assumptions clear instead of hidden in code.

This is where McCardel sees platforms like Hex playing an important role. Instead of separating technical and non-technical users into different tools, Hex is designed to support collaboration within a single environment. Analysts can create rigorous, transparent logic, while business users can interact with the results, ask follow-up questions, and understand how conclusions were made.

“The goal isn’t to turn everyone into a data scientist,” McCardel clarifies. “It’s to let each person contribute at their level without breaking the chain of trust.”

Trust, he stresses, is essential in modern analytics. As more insights come from AI, organisations will need clear lineage, better validation, and shared visibility into how answers are created. Black-box analytics may be quick, but they are also fragile.

“We’re moving away from the idea that insight is a product you deliver,” McMardel added. “It’s a conversation you participate in.”

As AI changes analytics workflows, the challenge for organisations won’t be just adopting the technology. It will redesign how people collaborate around data. The co-founder of Hex suggests that democratisation was never about removing experts from the process. It was about making expertise visible, accessible, and usable.

And that, finally, may be something worth not panicking about.

1. AI is reshaping the future of data analytics.
2. Data democratisation remains a significant challenge for organisations.
3. Trustworthiness in data outputs is crucial for effective decision-making.
4. Integration of different user personas is essential for collaboration.
5. Organisations can start using analytics tools without perfect data.
6. Expert users can help build trust in data analytics.
7. Natural language interfaces are key to making data accessible.
8. The role of AI in data exploration is becoming increasingly important.
9. Data quality and governance are critical for successful analytics.
10. Successful AI adoption requires a step-by-step approach.

1. 00:00 Introduction to AI and Data Analytics
2. 02:54 The Genesis of Hex Technologies
3. 06:04 Challenges in Data Democratisation
4. 09:10 AI's Role in Data Exploration
5. 12:14 Trust and Context in Data Analytics
6. 15:00 The Evolution of Analytics Tools
7. 18:10 Integrating Different User Personas
8. 21:09 The Importance of Contextual Understanding
9. 23:52 Data Preparation and Governance Challenges
10. 26:46 Incremental Adoption of AI in Organizations
11. 29:57 The Human Element in AI Adoption
12. 32:47 Conclusion and Next Steps for Leaders

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