4 posts tagged with "Data Lake"

In the previous post we argued that AI-ready data starts with master data — that until an organisation has one definition of "customer", nothing built on top of it can be trusted.

That is the foundation. It is not the whole building.

Master data tells an AI system who your customers are. It cannot tell it what they did. Answers that mean anything to a business — which accounts are growing, which are quietly churning, where the cross-sell actually is — come from transactional data. And transactional data is only useful to AI if it is linked to the master data that gives it meaning.

Often I see companies define their data strategy by simply choosing the top tools from the latest quadrant. Is your approach more about technology selection, or about shaping a strategy that delivers measurable value?

At its core, an asset is something that creates value and drives growth—directly or indirectly. It should strengthen resilience, fuel innovation, and deliver competitive advantage.

Now consider your data: Is your Enterprise Data Platform positioned as an asset, or is it on track to become a liability?

As your business expands, the foundations you set for your data become critical. A true data asset reduces reliance on ever-changing applications and removes the need for specialist technical skills just to interpret the numbers.

"Lakehouses", "Lakebases", "Meshes", and "Medallion Architectures". Regardless of the "buzzword" being used, it's essential to understand the underlying methodology, as these ones all follow the same foundational Data Lake methodology — yet the core business question often remains unanswered or simply assumed. Before committing to a data journey that typically spans 3–5 years and costs in excess $25 million — an approach frequently promoted by industry quadrants, shaping strategic architecture decisions — maybe worth considering the following points: