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Think about these questions.   What data do I have? What is it called? What does it mean? What is its type? Where did it come from? Where does it live? What happened to it along the way? How did it get there? How do I access it? What is its accepted data quality? Who do I call if I have a question?   Can you answer them for the data you’re using on a regular basis?  What about for augmenting data sets?  Creating new datasets?  New reports or dashboards?  AI models?   When I t

I’ve talked to a lot of people recently about their desire to implement AI to help with automation and decision making.  Their problem?  They still can’t do the basics like counting customers (i.e. consumer, patient, provider, student, etc.) or widgets (i.e. product, sale, opportunity, etc.).  The reasons are plentiful but can usually be traced back to key terms not being consistently defined, data that is not trusted, or data that lives in so many places it is hard to pull t

Do you remember your ‘ah ha’ moment that turned you into a data junkie?  The moment that made you believe in the power of data?  I remember mine clearly.    In the early 1990’s I worked for a small naval architecture firm.  Their focus was primarily custom high-end racing sailboat designs, including boats for the America’s Cup.  One day my boss brought in a floppy disk and asked me to ‘take a look.’  Apparently, we had a client who thought his brand-new boat was slow.  The di

How many of you have altered the content of a data set?  Formatting?  Created a new rule because of how an operational system worked?  At the time, were you thinking that it only a few records and the issue would get addressed next time you needed the data set?  Then you did it again, but for more records?  Then again, for even more?      What happens next?  The quick work around has become part of the process, only it takes more and more time to complete because there’s alwa