The Death of Data & Analytics Democratisation: A Personal Journey

The Death of Data & Analytics Democratisation: A Personal Journey

When I first embraced the idea of data & analytics democratisation, I was all in. I believed wholeheartedly that providing more people with access to better data would empower them to make better decisions, drive innovation, and foster a more informed workforce. It was a vision rooted in optimism—a world where data served as a common language, bridging gaps between departments and professionals of all backgrounds. Despite seeing mixed results, I continued to champion the idea, convinced that putting data into the hands of those tasked with making change was the way forward.


A Personal Wake Up Call

Recently, our work on optimising A&E patient flow is transforming my perspective. The tool we have built combines event data, local KPI performance, local context, an extensive body of NHS best practice knowledge, CQC reports, and actual case studies, leveraging process mining and LLM to generate relevant improvement recommendations that guide change.


It now surprises me daily with its ability to sift through vast swathes of data and hundreds of pages of recommendations, producing well-reasoned, prioritised initiatives, action lists, and business cases. The capacity of this tool to synthesise complex data sets and dense documentation into actionable, ready-to-implement outputs is eye-opening. It tailors its recommendations to the specific demands of the local situation, making it the most significant analytics innovation I’ve encountered in my 30+ year career.


A Paradigm Shift: Towards Intelligent Advisors

This project is making it clear that the end goal of an expert advisor for managers is within reach. The notion of empowering users solely with democratised data, dashboards, and analytics now feels outdated. Instead, we are witnessing the emergence of data use as depicted in science fiction—where systems don't just report data but advise on it. The future of data isn’t about accessibility alone; it’s about actionability.


The vision of an expert advisor capable of parsing vast amounts of data and producing strategic recommendations is no longer theoretical—it’s here.


The Arrival of Sci-Fi Data Intelligence

We are now realising the promise of data intelligence seen in sci-fi, where systems do more than inform—they guide. The combination of AI, contextual awareness, and human expertise embodied in tools like our A&E flow optimiser shows that democratising data itself isn’t enough. What we truly need is the democratisation of intelligent assistance, making expert-level support accessible at the click of a button.


The Death of Data & Analytics Democratisation

I believe that this shift from dashboards and raw data to intelligent, context-aware systems marks the death of data & analytics democratisation as we know it. This transformation will disrupt weak professional services firms, consultants, and advisors. But this is not a loss; it’s progress. We are moving towards a future where everyone benefits from systems that don’t just provide data but deliver actionable, impactful advice.

David Cheesman

Founder @ The Prompt Engineers | I've scaled two multi-million dollar businesses and mentor CEOs & Founders on how achieve the same success. Want the blueprint? Book a call [link below] ?? | ex-IBM

4 个月

Mathew, It would be good to catch up. David

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Chris Brasted

Currently developing a 3D design and print practice and dusting off my old illustration skills.

4 个月

Wow! Now that's what I call intelligence amplification.

Leigh Ashton

Enabling Sales Leaders to build a Growth Mindset culture that unlocks potential and drives revenue growth

4 个月

Everything is developing at such a fast pace Matthew. Difficult to keep up ???? Hope all is good in your world ??

Eric Willingham

Founder, Fast Flow Analytics, LLC. | Results-Driven Analytics Engineer | Empowering Businesses Through Accurate Data

4 个月

Insightful post, Matthew, and a great discussion here! Al’s analogy of the data engineer as 'le porteur d'eau' really hits home—clean, structured data is the foundation that makes meaningful insights possible. While democratizing analytics tools empowers broader teams, the role of the data engineer is critical to ensure that decisions are based on accurate and valid data. Equally important is streamlining the process of capturing data to reduce complexity and ensure consistency. A more efficient process means more robust, reliable data that can drive better decision-making. Thanks for sparking such an important conversation!

Al Wightman

Head of Data, Montirex

4 个月

Don't dispute your overall point but would currently challenge whether consultants/agencies that provide the data engineering/data pipeline type services are in trouble i.e. the non-insight services. Yes Ai can certainly help with that and I'm sure AI Agents will get better at data engineering execution but I'm not seeing anything close to AI producing business/context aware 'tidy', 'clean', modelled data for your non-Elon budget conscious business/organisation. There's a football analogy I like to use to describe what a data engineer is like. For those who can remember the French team of the late 1990s/2000s Didier Deschamps was the French captain whose nickname in the team was 'le porteur d'eau' - "the water carrier. His unglamorous job was to get the ball off the opposition and give it to the stars of the team Zinedine Zidane and Thierry Henry etc. Every data team needs a Didier Deschamps and they may not provide the crucial insight but they are just as important in making sure data-informed decisions are as valid as possible.

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