Evangelizing Analytics Through Baseball
A great many people are simply allergic to mathematics. Maybe even more so than to public speaking. They just hate the subject and the very thought of it gives them a big headache. In many cases though, I just have to blame their math teachers in their youth for not providing enough appreciation for the subject, as the same people have no trouble understanding baseball stats of their favorite teams and players.
What do you think that ERA (Earned Run Average) stands for? It is nothing but an index value made of a numerator and a denominator, multiplied by a factor. If you can paint the quality of a baseball pitcher with a bunch of statistics and indices like that, yes, you do have a basic aptitude to be analytical. Maybe not enough to be a professional analyst, but enough to be a consumer of analytics.
In the near future, that kind of basic aptitude may be all we need to navigate through this complex world weaved in numbers and figures originated from humans, machines and networks that connect them all.
All the headachy equational problems will be taken care of by the smart machines anyway, right? Maybe.
The way this old analyst sees it, the answer could be yes or no. Because there is no way for any machine to provide good answers to illogical questions (like Mr. Spock would point out). And the logical mind comes from mathematical training — with a little help from the DNA with which the subjects were born.
Why do I worry about such things now? Simple. I see too many decision-makers who say they must get more into analytics, and their behaviors tell otherwise. It is unfortunate for them, as the verdict is out already on the effectiveness of good analytics. In fact, the question is no longer about whether an organization must embrace more analytics-based decision-making processes, but about how deep and complex they must get into it. The winners and losers in the business world will not solely be determined by the business models, but by the effectiveness of execution, enhanced and measured by analytics. Gut feelings may have worked well for many in the beginning of the last century, but that won’t be enough when competitors are armed with data and analytical toolsets.
We are undoubtedly living in the complex world now. The differences between people who freely wield technology and toolsets and people who are afraid of the changes won’t be just income levels; it may even form new social classes. And yet, the way many are dealing with perceived and real challenges isn’t much different from the past era. No, you can’t just work hard and hope that everything will be alright. There are people who see what is coming before anyone else does. Not all may be able to “see” it completely per se, but at least some have better future prediction than others. And those who do properly employ predictive analytics will clearly have an edge over those who don’t.
Then, why is it so difficult to “sell” analytics? There are many reasons. The first one, I think, is the fear of unknown (or unfamiliar territory). People hate to spend money and resources on the things that they don’t understand. The majority of the population does not understand how the internal combustion engine works, so the car companies sell coolness and other perceived benefits of their products.
Unfortunately, there is absolutely nothing sexy — for the general population, not for the geeks — about algorithms that may increase sales and reduce costs. So, the analysts must try to emphasize the benefits of it all; yet too many fall into the trap of believing that everyone will appreciate the beauty of the solution they agonized over. Well, most people simply don’t care for the details. That is why engineers don’t sell cars, but salespeople do. Analysts must get to the point fast; possibly within a minute, as most don’t have the patience for anyone’s mathematical journey.
Another reason why selling the concept of analytics is difficult is collective resistance to change. For most people, change is scary; and even if it's not, it's terribly inconvenient. All organizations and people in them are accustomed to some existing ways of doing their businesses. Analytics inevitably invoke changes in existing behaviors. It may start with a simple request for some extra data collection (“What do you mean I have to enter more data into my Salesforce account?”), and ultimately move onto changes in decision-making processes (“Oh, now I have to look at those model scores while I’m on the phone with the customer?”).
Stephen H. Yu is a world-class database marketer. He has a proven track record in comprehensive strategic planning and tactical execution, effectively bridging the gap between the marketing and technology world with a balanced view obtained from more than 30 years of experience in best practices of database marketing. Currently, Yu is president and chief consultant at Willow Data Strategy. Previously, he was the head of analytics and insights at eClerx, and VP, Data Strategy & Analytics at Infogroup. Prior to that, Yu was the founding CTO of I-Behavior Inc., which pioneered the use of SKU-level behavioral data. “As a long-time data player with plenty of battle experiences, I would like to share my thoughts and knowledge that I obtained from being a bridge person between the marketing world and the technology world. In the end, data and analytics are just tools for decision-makers; let’s think about what we should be (or shouldn’t be) doing with them first. And the tools must be wielded properly to meet the goals, so let me share some useful tricks in database design, data refinement process and analytics.” Reach him at firstname.lastname@example.org.