A nice benefit of Maggie's website management are notifications when posts are shared.
Validation is always welcome. It's great to see someone pick up on your writing and think "I am glad I was able to add to the discussion." I believe this holds when a piece is shared on a site opposing what you've written. I'm not interested in an echo chamber.
Twenty months after writing this post on data, I received notification of its inclusion on another site. Upon reading, one might be inclined to believe I'm not a fan of data. Not true, I just don't put my full faith in everything as it is presented, or simply because it's presented, to me.
Since my post, 20 months have passed and nothing has changed. In fact the 2016 election was an example of organizations simply accepting data, becoming reliant on it, while few questioned its value. The data left me, and many others, inclined to believe Hillary would win. At the same time, it left me angry about how it was presented in a "See? We have more information and you don't know what's really going on" manner. The day of the election, however, the long lines I saw (in New York City) left me with the impression the data may not be telling the whole story. If Hillary voters in a safe city were turning out in droves, I came to the conclusion turnout would be high across the board, and high turnout usually coincides with a desire for change. The data itself may not be 'wrong' but whoever was using it was doing so improperly.
In the article linked to my post, there is a brief discussion of large banks using data to avoid lending in poor neighborhoods, thus limiting potential losses on their loan portfolio. This policy, known as 'redlining', is often a source of anger and has yielded policies designed to punish firms which do this. It's my view firms shouldn't be punished for doing this. Mainly because these firms are more likely to take outsized risks in good neighborhoods - and face potentially larger losses in those areas. On the other hand, presumed lost opportunities in poor neighborhoods will find a willing participant for profit. It takes time. It takes money. But more importantly, it takes an understanding that risk is the critical part of earning a profit. And it is the word RISK which makes the difference, and shifts the discussion from one of data to something else altogether.
Data, as currently over-used, is designed to reduce risk. If you reduce risk, you also reduce the likely margin of profit. You can increase your opportunity to be profitable. The margins on that profitability may be lower than if you'd rolled the dice in a few areas the data told you to avoid because they are poor risks. Large firms, looking to keep risk low and profit as high as possible, enjoy the benefits which data can provide to avoid potentially bad loans. These benefits carry with them the potential lost opportunity of larger profits from taking on higher risk.
Branch Rickey once said "Luck is the residue of design." Data is a key part of that design. But so is understanding risk and knowing how to manage and profit from it (though not necessarily reduce it).
