Mis-managed Data Lakes and Lakehouses will result in a Super Swamp
View All Tags
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:
Data Lakehouses becoming Super Data Swamps highlights the strategic risk organisations face when modern data platforms scale without proper governance or value discipline.
The application of data governance often lags well behind the enthusiasm for filling data lakes and lakehouses. With the rapid adoption of AI, that gap is no longer theoretical—it’s becoming a costly and unavoidable reality for many organisations.