by Zach Marsh on Sep 27, 2019
We live in a data obsessed world. The rise of super computers, over half a century ago, gave us the opportunity to quickly retrieve, analyze, and create a solution around all sorts of computable information. We can thank IBM for that. What began as a niche in most industries, data compilation and analysis, has now evolved into the new arms race--who can gain the greatest edge by compiling the most discrete and pertinent information and data. But has this obsession created a problem, are we now at the point of information overload? And has information and data ceased being a tool we employ and evolved into the obligatory road to follow. Who is the puppet and who is the puppeteer?
I believe in and love quantitative analysis. I believe numbers and information can help us remove many of our predisposed biases and help us make clear and informed decisions devoid of cognitive and emotional responses. That is my qualifying remark. But I also hate it. I hate what it has done to many of the things I love. I hate how Silicon Valley uses information to create algorithms designed to continuously feed me movies, tv shows, news articles, advertisements, etc. based upon previous search items, previous shows I’ve viewed, or emails I’ve received for the sole purpose of helping me buy more things or keep me streaming or scrolling news feeds. At the end of the day, they have melted me down to an automaton of consumption. I’m fed up with it.
I also hate what it has done to sports, notably baseball. Baseball has always been the most quantitative and data oriented of the major sports. As a kid I used to play Strat-O-Matic baseball, a board game in the 1980’s combined players’ statistics with the randomness of a dice roll to create an authentic baseball experience. In fact, I even created my own stat-based baseball game using my large collection of baseball cards. So, baseball’s history has always been linked with data and statistics. But like the steroid era of the 90’s, today’s game is data analysis on steroids. Old standard metrics like batting average have given way to new data like launch angle and exit velocity (how much a hitter elevates his hits and how fast the ball leaves his bat).
All of this data and computable information has changed the way the game is played. Home runs are up, batting averages are way down, strikeouts and walks are both through the roof, pitching changes is half the game—the game has turned into a plodding test of endurance for the fan. So, maybe the front offices have done well to become more efficient at run scoring and run prevention, but they’ve lost the fan. The games can be a real bore at times.
So I have to ask the question, if attendance is down and fewer people are watching on TV, then why do general managers and owners continue down this same rabbit hole? Are the trapped in a Prisoner’s Dilemma game where they have to keep chasing the information because their opponents are, and they will fall behind? That seems a plausible and rational explanation, but one day someone somewhere will say, “Congratulations to 2035 World Champion St Louis Cardinals,” but there won’t be anyone to hear it because there will be no fans left.
This brings us to the crux of the problem—a problem with potentially profound consequences, does the data serve us or do we serve the data? And can we make decisions contrary to the data even when we know the data is leading us toward an untenable position? Here’s a quick relevant thought experiment: At work you have a decision to make, follow corporate procedure, even though you are 100% certain it will fail, or go out on a limb and chose a different path that may only have a 50% chance of success. Unfortunately, most will choose the path of certain failure because it is the path where the blame factor and career risk factor is low. The other path has low upside and big downside: if it turns out successful the boss won’t see that the standard procedure would’ve failed and so you’ll only get blamed for not following orders, not for solutioning a better course of action.
Data has painted us into a similar dilemma. Throughout our day we are provided an enormous amount of data on which we can choose to act. It could be as small as whether or not to cancel the soccer game, or as big as whether to buy a company. Deciphering that data can be made difficult because we struggle to determine which information is pertinent and which is not. If we choose the wrong data to focus on, or have incomplete information, going against the decision implied by that information would seem unwise or fraught with potentially negative consequences. How would we justify our decision to our clients or bosses?
For example, if the weather app on my phone shows rain for the next 5 hours, and I have a game in 2 hours, then surely it is wise to cancel the game, even if it is sunny right now. If I cancel and the rain doesn’t arrive, then I can hide behind the poor information I received. If I have the game, because it is currently sunny, and it rains I have a hoard of angry parents. But if I have the game and it remains sunny no parent comes up to me afterwards and compliments me on my wise decision making. The course of action implies blindly following the information.
Life as an investment manager has similar negative risk/reward trade-offs but forces tough decisions. A balanced-risk investment strategy may provide returns that don’t necessarily rhyme with the direction of the stock market but may provide a smoother longer term return sequence. The downside is that the returns may not follow the returns of the stock market, causing anxiety when the market is up, and the returns are not. Being a fiduciary advisor should mean investing in the best manner possible, not the one that simply creates the least friction.