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The data lake trap: why many AI strategies stall?

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The temptation to centralize all data is understandable. It feels like a future-proofing move. But without clear use cases driving it, a data lake often becomes a data swamp: difficult to navigate, hard to trust, and expensive to maintain.

The alternative is to build just enough.

When the starting point is a valuable decision worth improving, the data stack can be smaller, faster, and far more targeted. Instead of a massive upfront investment, you build incrementally as your needs evolve.

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