Leadership in the Post-Moneyball Era
When Elon Musk embarked on his Twitter crusade, he launched a live stream of corporate decision-making that had been confined to corner offices, board rooms and golf courses. Pre-acquisition, Musk was the Technoking, breaking stereotypes about uncool nerds with bad management skills. He was worshipped for his ruthless drive, irreverent persona and zealotry in technology. However, the world cringed as we watched him stumble from rash decision, snap rescindment, to defensive retort. Musk’s antics at Twitter depict how plenty of business decisions are made: riddled with personality, hype and dogma.
We now have unprecedented access to technology that promises tidy solutions for all of life’s predicaments. Despite this, we cannot seem to escape the same old, human traps that plague our thoughts and behaviors. Furthermore, we abuse data to create an illusion of rationality. As Michael Lewis, who immortalized the term “Moneyball” for data-driven decisions, puts it: the numbers start out as rules for thinking, but they wind up replacing thought.
With explosive gains in algorithmic and computational prowess, we can no longer rely solely on technical skills when engaging with data and technology. Harnessing bleeding-edge technology requires a scrupulous examination of how we think and act. At the end of the day, humans decide which problems are worth solving, how we define our objectives, how we make tradeoffs towards those objectives, and finally, how we communicate about our choices.
Chindōgu
In the pursuit of the next big thing – the groundbreaking revolutions, transformative innovations and disruptive everything, we devote a disproportionate amount of effort shopping for problems to fit the solution. While chindōgu, a humorous phrase describing seemingly logical but ineffective solutions, can be wildly entertaining, it also implies wasted resources, missed opportunities, even substantial harm. Such examples abound. Juul, whose product was developed to help adults stop smoking, addicted a new generation to nicotine by marketing flavored e-cigarettes to teenagers. Cryptocurrency, conceived to bolster financial inclusivity, has been exploited by profiteers and fraudsters that prey on groups marginalized from mainstream financial services.
Technology is a great tool for addressing problems. However, it cannot tell us what problems are worth solving. Many organizations are not proficient in identifying and articulating critical problems. The process of defining the problem and justifying the need for a solution is key to ascertain demands that are worth addressing. Leaders who leap into specifying solutions without understanding the problem could end up missing genuine problems or wasting too many resources on low-priority or wrong ones.
What Good Is
One crucial aspect of defining the problem is to determine the desired outcomes. Modern corporations are run with a plethora of metrics, scorecards and charts. Unfortunately, we acquiesce to conventional benchmarks as targets without considering the context and implications of their application.
In the movie “Moneyball”, based on the non-fiction book by Lewis, we are intimated to the travails of Billy Beane, the General Manager of the Oakland A’s. Frustrated by a slashed budget, defecting star players and failure to replace them, he cornered Peter Brand, an assistant from a rival team, after observing cryptic signaling in a meeting. Brand sheepishly confided that convention has misled “people who run major league baseball teams to misjudge their players and mismanage their teams.” Instead of buying players, the goal should be buying wins.
Following this counsel, Beane gutted traditional scouting qualities like looks, personality and attitude, and replaced them with quantitative measurements correlated to winning such as “on-base percentage” i.e. how often a batter gets on base. Establishing what good means entails clarifying how we measure achievements. Beane ultimately led the A’s to an unbelievable 103 wins, made baseball history with a 20-win streak and revolutionized the sports industry.
What we define as good also informs the choices and tradeoffs we make on the journey towards our objectives. It is tempting to assume that once we set off on a path, we will meander towards some destination. The reality is that we would be confronted with messy circumstances requiring us to make hasty adjustments and unpalatable compromises. Under duress, we can be led astray by unrealistic expectations, the fear of missing out and false knowledge.
In 1979, Michael Porter formalized the concept of business strategy in his seminal work “How Competitive Forces Shape Strategy”. In recent years, however, the notion of strategy has fallen out of fashion in favor of technological agility and reinvention. Critics argue that the speed of technology change is too fast to establish definite strategies and goals. But strategy is not about pandering to the latest fads; it is about making choices and tradeoffs to do things differently than one’s competitors. Having the right goals informs our strategy: what we choose to do (or not). Technology can help us make better choices, but it is not the ultimate outcome. Being discerning about which ideas are good means that we are less susceptible to the spells of gizmos.
Communicating Our Choices
The language of math and science is powerful because they invoke an emotional response. As statistician David Spiegelhalter noted, politicians are hyperaware of this and love using “number theater” – numbers as a performance – to influence perception and manipulate behavior. Math and science give business vernacular a veneer of intellectual respectability with a dash of elitist polish. Sprinkle in some swanky technology jargon and the hype becomes irresistible.
False generalizations are especially rife in business discourse. Snappy soundbites that belie the complexities of underlying issues are appealing because they are neat and reassuring. In the retelling, the truth is often oversimplified or misconstrued. This distortion is aggravated by popular media figures such as Malcolm Gladwell, who perpetuated myths like the “10,000-hour rule”. Misinterpreted evidence can lead to disastrous social and economic outcomes, for instance, the human toll of the low-fat diet and the insidious effects of vaping. To cut through these perversions, leaders have to convey judiciously why and how choices are made, which includes articulating the limitations of science and technology.
Beyond Technology
Organizations have poured billions of dollars to amass data and infrastructure in hopes of increasing competitiveness. But technology alone is not enough. Humans shape how data is collected and analyzed, how technology is used, and how we talk about our choices. Progress calls for leaders who recognize that technology is just a tool to expand our limited knowledge and perspectives. Mastering technology requires more than technical competency; it needs leaders that can grapple with the fundamental aspects of our humanness – leaders who can account for the precariousness of our ignorance and consequently, make sound decisions and take meaningful actions.
Acknowledgements
This post is inspired Professor Ramesh Johari, who teaches data science as a qualitative subject, with zero compromise on technical rigor.