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Data Swamp

The more things change, the more they remain the same as evidenced by this Axios story. Even twenty five years ago, there was a lot of hand-wringing over how hard it is to tease insight out data, the poor ratio of signal vs noise, the absurdly high cost of learning something truly novel and game-changing from data. Vendors have come and gone with their panaceas to solve all of those problems but there is no comprehensive solution to this day. The problem statement cited in this story is identical to that from many decades ago. 

Many companies are finding their data isn't organized for the AI revolution — saved in different formats, in disparate datasets, and sometimes still on paper — "forcing a complete rethink of how data is stored, managed and processed," said Nick Patience, senior research analyst at S&P Global Market Intelligence.

The reality was and still is that while all companies want to be data companies in theory, very few have what it takes to succeed. Those that do will build platforms and set the rules of engagement that everyone will not like. The platform players have and will continue to build data gravity, giving them disproportionate access and control of insights from data. The rest will continue to struggle trying to eke something of value from their their many data swamps. 

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