Guest post by Bill Inmon
“Bill Inmon is an American computer scientist, recognized by many as the father of the data warehouse. Inmon wrote the first book, held the first conference, wrote the first column in a magazine, and was the first to offer classes in data warehousing.” Source: Wikipedia.
An article headline — “BIG DATA/AI BLAME FAILURES ON BAD DATA” — caught my attention. Certainly, it is true that Big Data and artificial intelligence (AI) have not lived up to their hype. In fact, the hype was so great that perhaps no discipline or technology could have lived up to what was sold and promised. But to blame the failures of Big Data and AI on bad data is somewhat disingenuous. (Actually, a whole lot disingenuous.)
Now, there's no question that trying to make sense of Big Data and AI with bad data is not a good idea. It's like starting your day with a shot of tequila at 8:00 am. Or trying to slap your spouse into obedience. Or pouring water into your gasoline engine in the hope that the engine will run on water.
These are just not good ideas — for data governance or life in general.
But there is more to this story than meets the eye. For years, IBM has not understood nor supported data warehousing (even though many machines have been sold by IBM for exactly that purpose). For reasons of their own, data warehousing is just not a subject that has been supported by IBM. And that's their right and privilege.
But to the keen observer, it's obvious that data warehouses have served some fundamental needs in the corporation, whether IBM supports the data warehouse or not.
How IBM Concluded Data Warehouses Are Unnecessary
So, one day IBM came out with the brilliant notion that with Big Data you didn’t need a data warehouse. Somebody needed to inform IBM that there's a fundamental difference between technology and architecture. Database systems are a technical discipline. Data warehouses are an architectural discipline. The disciplines are complementary, not mutually exclusive. The difference between a technical solution and an architectural solution is like the difference between a carpenter and an architect. A carpenter is an expert at measuring, sawing, and driving nails. An architect is an expert at understanding load, traffic, structure, and how to withstand the forces of nature. Do you feel safe driving across a bridge designed by carpenters? Do you feel safe on a bridge built by architects? No? How about a bridge designed by an architect, built by a carpenter, with hurricane-force winds howling? A bit more confident, huh? But somewhere along the line, IBM blurred the lines.
How Did This Blame Game Begin?
There is a very real difference between the technician and the architect: Just because you have Big Data does not mean you don’t need a data warehouse.
IBM had the notion that any business working with AI, data science, or statistics didn't need a data warehouse. (IBM will do anything rather than concede that the data warehouse serves a fundamental purpose.) For whatever reason, IBM told data scientists that they should not use data warehouses. Data warehouses were “old.” Today’s technicians are beyond all that old stuff.
How Does This Story End?
One day we wake up and find that in order for data science and AI to work, we need reliable believable data. And guess where that kind of data is? In a data warehouse, of course.
Bill Inmon, considered by many to be the father of the data warehouse, has authored 65 books, including Data Architecture: Second Edition and Turning Text into Gold, and was named by Computerworld as one of the 10 most influential people in the history of computing. Bill’s company, Forest Rim Technology, is a Castle Rock, Colorado, company. Bill Inmon and Forest Rim Technology provide a service to companies by helping companies hear the voice of their customers. See more at www.forestrimtech.com.
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