The Data Middle Ages: When Electricity Is Here, but Data Still Flickers by Candlelight

No Data-Driven Operation Without a Shift in Mindset: Lessons from the Front Lines

Why? Because in recent decades, management has often opted for simple, quick solutions. Not enough attention — or budget — was devoted to data quality, structured data collection, or laying the groundwork for data-driven decision-making. This deficit in data technology and governance is now coming back to bite.

Today, even though we should theoretically be ready to implement larger, strategic data projects, we’re faced with a harsh reality: in many places, even the basic quality data needed to build on is missing.

No Maturity Without Foundations

We often assess professional maturity using well-known international models — like Gartner’s. Based on our experience, many Hungarian companies are still at level 1 or 2. This phase is marked by experimentation, isolated successes, and uncoordinated initiatives. Reaching a more formal level — where data-driven processes, defined roles, and data governance are in place — typically requires 2–3 years of development.

The problem? It’s difficult to get support for projects that don’t generate immediate profit. Yet smartly designed foundational data projects — like data quality management — are prerequisites for maturity. Without these, more complex analytics and predictive solutions simply aren’t feasible.

BI Is Not (Just) IT, And Certainly Not Magic

A common misconception is that Business Intelligence (BI) is a purely technical IT domain —after all, the name suggests it. But in truth, BI’s goal is not technical; it’s business-oriented: to provide insight, understanding, and decision support. And achieving this requires more than just a solid tech stack: it needs business thinking first and foremost.

That’s why it’s usually ineffective to organize BI teams under the IT department. Their place is on the business side — under finance, commerce, marketing, or customer service — because data doesn’t exist for its own sake: the value lies in the information the business needs.

That said, BI isn’t independent of engineering tasks either. When you're processing 5 million, or even 5 billion rows, the technology in use becomes a critical factor.

Don’t Build a Cathedral If a Chapel Will Do

At many companies, we see oversized, expensive systems being built instead of starting small and agile with data projects. Often, a much smaller, more flexible solution would suffice — assuming reasonable business risk — and could be developed further later as needs evolve.

Another common overengineering mistake involves availability planning:

“If the system is down for one day a year, it won’t bring the company to a halt. Maybe the sales dashboard won’t update on time, maybe a management report is delayed, but basic operations will still continue.”

Digitalisation Is a Cultural Challenge, Not Just a Technological One

The biggest challenge lies not in technology, but in culture. While younger generations enter the workforce with a natural data literacy and aren't intimidated by databases or config files, at the management level, resistance remains strong. It’s understandable: decision-making responsibility ultimately falls on the leader.

Many executives have successfully led companies for decades, and find it difficult to accept that a new way of operating demands a new way of leading. It’s like turning on adaptive cruise control (or autopilot) in a car for the first time: it takes time for the driver to learn to trust the system.

After the Data Middle Ages Comes the Renaissance

Progress in data management requires a shift in both mindset and organizational structure. The challenge isn’t a lack of tools, but a lack of understanding, commitment, and the ability to evolve culturally.

The renaissance, like any paradigm shift, begins when we’re no longer focused solely on survival, but on comprehension. When data is no longer a risk, but an opportunity. Not a burden, but a resource.

So the only question is: Who among us will be the first to dare to think differently?

Because the map is no longer the same, and the world is more data-rich than ever.