Why I have been fighting for true data industrialisation for over 15 years

Data governance – the sleeping giant of our time – is nothing less than the architect of a sustainable, data-industrialised world. I have been intensively and passionately dedicated to this topic for over 15 years, even though many thought I was crazy to tackle such a supposedly unpopular issue back then. As an advisory board member of DATAGOVKON, I am actively committed to raising awareness of its strategic importance. Even back then, it was clear to me: how can data-driven industrialisation be successful without robust standards and binding architectures?

The consequences of a lack of governance: fragmented landscapes and data chaos

In many companies today, the consequences of this strategic neglect are clearly evident: massive expenditure on data preparation and processing, inefficient data silos and fragmented IT landscapes that have grown over time and are often heavily adapted to local needs, while the actual needs of customers are frequently neglected. Adaptations to local needs and a short-term focus on internal optimisations have meant that a coherent, let alone customer-centric, data strategy has rarely been pursued. Digital initiatives all too often fizzle out in the thicket of these structural weaknesses.

Artificial intelligence in the field of tension between inadequate governance

The situation is particularly dramatic when it comes to integrating modern technologies such as artificial intelligence and, in particular, AI agents: instead of seamless integration and scaling, companies are struggling with inconsistencies, inadequate interfaces and unclear responsibilities. The potential of AI thus falls far short of expectations. End-to-end process-oriented AI systems could become a decisive competitive advantage – provided that a solid governance structure exists that defines clear guidelines, responsibilities and processes.

Why isolated innovations are not enough

Isolated pockets of innovation in labs or individual departments do not solve these deep-rooted structural problems. The question is whether these isolated initiatives will ever result in a genuine, adaptable overall strategy that meets the structural requirements of a data-industrialised world. Instead, a holistic, clearly adaptable and pragmatically operational data and AI strategy is needed.

Data governance as the foundation of the data factory

This is precisely where data governance shows its true strength: it links the dimensions of technology, processes, organisation and law into a holistic and resilient regulatory framework. It is the indispensable master plan that turns unstructured data collections into real data factories that continuously generate added value.

Learning from the automotive industry: standardisation as the key

A look at the automotive industry illustrates this dynamic impressively: if everyone built their own car individually and by hand, mobility would never have become a mass product. The same is true for data. As long as companies do not establish standardised and efficient ways of handling data, the effective use of data as a resource will remain an expensive luxury for many, offering little in the way of sustainable competitive advantages. Without data governance, companies will lose out in the long term – both locally and globally.

Act now: Think about data and AI governance together

But there is a tangible opportunity: Companies that consistently invest in data governance today and strategically embed it in their corporate culture and processes are laying the foundation for sustainable scalability, efficiency and innovation. We are currently at a decisive turning point: the era of artificial intelligence is challenging us to rethink governance – as an integrated unit of data and AI governance. It is not enough to recognise the mistakes of the past; we must learn from them and ensure that we do not repeat them when implementing AI.

Conclusion: the sleeping giant must awaken

The integration of data governance and AI governance is the key lever for mastering the next step on the evolutionary ladder of the digitalised economy. This is the only way to achieve true scalability, systematic innovation and sustainable value creation from data and AI – not just for individual pilot projects, but for the entire corporate landscape. The time to act is now, because the sleeping giant must awaken.

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• The future of data preparation with AI: a revolution in data analysis
• Why do I rarely talk about AI and a lot about data-driven?
• Data to Transition: The superiority of Digital AI-Driven Ecosystems
• Be a Data Punk