Why Your Data Lake Needs BLM, Not LLM

Author(s): MKWriteshere

Originally published on Towards AI.

“Your data lake investment became a ‘cesspool’ the moment you tried solving structured data problems with text-generating AI.” — Bill Inmon

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Your $15+ billion data lake investment just became a liability.

You’re not alone. 85% of big data projects fail according to Gartner research. The $15.2 billion data lake market grew over 20% in 2023, yet most enterprises can’t extract value from their textual data.

Bill Inmon — the “Godfather of Data Warehousing”, calls these failed implementations “cesspools” and “data swamps.”

Here’s why your current approach isn’t working. And what does?

Vendors keep pushing the same broken solution: “Just add ChatGPT to your data lake!”

This advice will cost you dearly.

ChatGPT burns $700,000 daily just to stay operational. Enterprise implementations run $3,000-$15,000 monthly for mid-sized applications. For organizations processing 100K+ queries, you’re looking at $3,000-$7,000 per month in API costs alone.

That’s before infrastructure overhead.

But cost isn’t the real problem. The fundamental issue is much worse.

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When you analyze 10,000 customer support tickets, you don’t want ChatGPT writing essays about customer feelings.

You need structured data.

Sentiment scores. Categorized issues. Trend metrics. Actionable insights that populate dashboards and drive decisions.

ChatGPT gives you more text to read. That’s the opposite… Read the full blog for free on Medium.

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Published via Towards AI