Play Ball!

It’s baseball season again. As I grow older, I’m more and more interested in the art of the game and less about the success of any particular team, but that’s the perspective of experience, I suppose – or in other words, I’m just getting old!

Back in 2011 (see – told you I was old) the film “Moneyball” showed us how the Oakland A’s built a super-competitive sports franchise on analytics, essentially “competing on analytics”, within relevant business parameters of a major league baseball franchise.  Going back even further, the “Moneyball” saga and other examples of premier organizations competing on analytics were featured in the January 2006 Harvard Business Review article, “Competing on Analytics” (reprint R0601H) by Thomas Davenport, who also authored the book by the same name – a book I have read and that sits on my book self.

Davenport has written a number of books since then. His latest is All in on AI, co-authored with Nitin Mittal, is one which I confess that I have yet to read. However, you can find a review of it by Dr. Mike Watson here.

The noted German doctor, pathologist, biologist, and politician, Rudolph Ludwig Karl Virchow called the task of science “to stake out the limits of the knowable.”  We might paraphrase Rudolph Virchow and say that the task of analytics or as we now say, “AI”, is to enable you to stake out everything that you can possibly know from available data.

In your business, you strive to make the highest quality decisions today about how to run your business tomorrow within the uncertainty that tomorrow brings.  That means you have to know everything you possibly can know today.  In an effort to improve their ability to do this, many companies are investing generative AI – just one of four major areas of AI (for more about AI categories, have a look at Mike Watson’s blog post here).

AI has become an umbrella term for lots of modeling tools that should be in your toolbox. Make sure you are aware of all the tools and their strengths so that you can combine them for the result you want.

I recommend carefully design your objective, understand the root causes you need to impact, and then iterate toward your objective, learning as you go.

Data quality from ERP and other internal systems has improved in recent years, as has the ability to create information from it, but for many firms, it’s still often incomplete, wrong or out of date. So beware of the old addage, “Garbage in. Garbage out.”

Remember that investing in software tools without understanding what you need to do and how, is akin to attempting surgery with wide assortment of specialized tools, but without having gone to medical school.

Are you competing on analytics/AI?

Are you making use of all of the data available to support better decisions in less time?

Can you instantly see what’s inhibiting your revenue, margin and working capital goals across the entire business in a context?

I appreciate everyone who stops by for a quick read.  I hope you found this both helpful and thought-provoking.

As we enter this weekend, I leave you with one more thought that relates to “business intelligence” — this time, attributed to Socrates:

“The wisest man is he who knows his own ignorance.”

Do you know what you don’t know?  Do I?

Have a wonderful weekend!