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History Repeats Itself Through Business Intelligence (Part 1)

History repeats

Unlike some of my friends, History was a subject in high school and college that I truly enjoyed.   I particularly appreciated biographies of favorite historical figures because it painted a human face and gave meaning and color to the past. I also vowed at that time to navigate my life and future under the principle attributed to Harvard professor Jorge Agustín Nicolás Ruiz de Santayana y Borrás that goes, “Those who cannot remember the past are condemned to repeat it.”

So that’s a little ditty regarding my history regarding history.

Forwarding now to the present in which I have carved out my career in technology, and in particular, enterprise software, I’m afforded a great platform where I talk to lots of IT and business leaders.  When I do, I usually ask them, “How are you implementing advanced projects that help the business become more agile or effective or opportunistically proactive?”  They usually answer something along the lines of “this is the age and renaissance of data science and analytics” and then end up talking exclusively about their meat and potatoes business intelligence software projects and how 300 reports now run their business.

Then when I probe and hear their answer more in depth, I am once again reminded of THE history quote and think to myself there’s an amusing irony at play here.  When I think about the Business Intelligence systems of today, most are designed to “remember” and report on the historical past through large data warehouses of a gazillion transactions, along with basic, but numerous shipping and billing histories and maybe assorted support records.

But when it comes right down to it, business intelligence “history” is still just that.  Nothing is really learned and applied right when and where it counted – AND when it would have made all the difference had the company been able to react in time.

So, in essence, by using standalone BI systems as they are designed today, companies are indeed condemned to repeat what they have already learned because they are too late – so the same mistakes will be repeated again and again.

This means the challenge for BI is to reduce latency, measure the pertinent data / sensors / events, and get scalable – extremely scalable and flexible enough to handle the volume and variety of the forthcoming data onslaught.

There’s a part 2 to this story so keep an eye out for my next blog post  History Repeats Itself (Part 2)

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