Introduction: Why We Crave Reliability

Introduction

Why We Crave Reliability

Whateveryonewants, morethananything, is reliability. Substitute whatever term you want—stability, consistency, dependability—in the end we all want it. It is the cornerstone of modern existence. Look at virtually any period of rapid economic growth in history and you’ll o3en "nd this formula at work: a strong motivation to advance coupled with con"dence in stability around us. Even the economic expansion during World War II exempli"ed this. Americans rallied their strength against the imminent threat of the Axis powers, but we had an advantage. Every country engaged in the war was motivated to win, but as Europe was gripped in the con5ict and destruction, American soil felt relatively safe. With the combined motivation to win and stability at home, the United States went through its most aggressive industrial expansion in history. !ereliabilityhere at home drove unparalleled increases in productivity, and enabled America to emerge as the new global superpower.

Looking around us now, the value of reliability couldn’t be more evident. When we start our cars, turn on our lights, drop our kids o$ at school, show up at work, receive our paycheck, or go to the grocery store, we expect everything in society to work. When some small thing happens that threatens to disrupt normal life, we panic. !e mention of a possible toilet paper shortage caused a run on toilet paper. People went to the store, loaded up on every roll they could "nd, waited in line for hours, and stocked up with a year’s worth of toilet paper.

Reliability makes our world go around. !e more we have, the more we advance. When there is less, we slow down, or even go

backward. Our dependency on reliability is shown in the e$ects on corporations, academic institutions, and government. When things are consistent, employees, students, and citizens feel good. Introduce a bit of uncertainty, and stock drops, students struggle, and citizens panic. !is, in turn, re5ects another critical aspect of our society: the gargantuan impact of decisions made in crucial moments. !e connection between key decisions and systematic reliability has brought modern society to a seminal moment in our history. Our reliability, in the developed world, has reached such epic levels that we can live very comfortably. One thing the coronavirus lockdown showed us is that the basic needs of Americans can now be met with only 25 percent of our population working. And while this is unsustainable, it is a testament to the ingenuity of people.

As we have increased productivity using machines and systems, the employment makeup of our population has gone through a major shi3. We went from over 50 percent of our population working in basic goods and services in the 1950s, down to only 20 percent today. !is allows the rest of our working population to go into other goods and services that enhance our lifestyle, improving everything from restaurants to clothing to vacations to cars. In other words, reliability has improved our quality of life in staggering proportions. However, the downside of that high degree of reliability is that we have now come to expect it. And this expectation has made it possible for a single bad decision, or a series of uncertain ones, to easily disrupt our entire way of life when unreliability causes panic.

History may judge our reactions (by individuals, societies, and government) to COVID-19 as some of the worst decisions in modern history. In a rapid response, governments shut down major portions of our economy, bankrupted thousands of companies, suspended schools, and put tens of millions of people out of work. !is intensi"ed an unprecedented level of fear, and decimated the reliability of our way of life. As people began to "gure

out that this was unsustainable, they pressed hard to return to work and the rest of life. Of course, the spread returned. Most economists agree that the struggle to balance a functioning society with prevention and treatment of COVID-19 will have lasting impacts, some speculating that it will take years, even decades, to recover.

But in failure comes the opportunity to learn. !e human race has had several big leaps in our development, including the Stone Age, the Bronze Age, the Industrial Age, the Machine Age, the Space Age, and the Information Age/knowledge economy. Right now, we are on the precipice of what could be our next and possibly greatest advancement, as we use data in ways we never have, to make consistently better decisions and perform at levels we never thought possible. !e early signs of this started nearly twenty years ago when Billy Beane revamped the Oakland Athletics recruiting strategy based on a revolutionary use of data, making better decisions about which recruits to put on the team. Captured in the movie Moneyball, this idea spawned an entire shi3 in thinking, with other sports clubs, businesses, investors, technology gurus, gamblers, and cultural thought leaders working to advance the use of data to make game-changing leaps forward. In my own world, talking to leaders in marketing, operations, technology, human resources, and politics, we have tossed around the idea of moneyballing our processes. !ere have even been adaptations, attaching “balling” to the end of any word to indicate a substantial shi3 in the use of data. In his 2018 book, Astroball, author Ben Reiter described how the Houston Astros took the original concept to new heights and won the world series in 2017.

While these advancements in sports, gambling, and iPhone apps are exciting to watch, they are even more impactful to the broader world because they give us a glimpse of what else is possible. With more powerful data analytics and advancements in our study of the "eld of data science, we can take performance in other areas to places we have previously never dreamed.

Nowhere is this potential as striking as in the world of reliability and complex systems. Improvements there would a$ect the most vital components of our society, from healthcare to education to energy to large business teams. How we make decisions and solve problems using data will drive the next evolution in human history. !e question is, how quickly will we make the change? It took "3een years a!er Billy Beane’s magic with Oakland for the rest of the major league teams to accept that good scouts and good data analytics will beat good scouts alone.

In the early days of data analytics in sports, for example, the scouts and coaches with decades of experience (a.k.a. the experts) tended to refute the application of the geeks (a.k.a. the data analysts). In return, the data analysts had a temptation to view the experts as dinosaurs, not willing or able to get with the times. !is polarization in industry, sports, or public policy pushed experts and geeks onto opposing sides of decisions for much of the past two decades. As a result, much energy has been spent by both the expert and the geek trying to prove themselves superior, rather than operating as two players with di$erent skills on the same team. Hence, we turn to one or the other in a vacuum, make the best decisions we can, and either get it right or su$er a miscalculation.

While it may not have been the norm, there have already been shining examples of integration of experts and data analytics in decision making. When Je$ Luhnow and Sig Mejdal brought performance data analytics to the St. Louis Cardinals and then used it to win the world series with the Houston Astros, they took a di$erent approach. “It’s the scouting information and the performance information.” In 2006, Sig “had developed the "rst iteration of a metric that sought to incorporate the reports of the club’s scouts with his own performance-based algorithms, to integrate quantitative and qualitative evaluations. He called it Stout—half stats, half scouts.” 1

1 Ben Reiter, Astroball: The New Way to Win it All (!ree Rivers Press, 2018), 28.

It took a decade and much consternation to move professional sports to more consistent use of data. !erefore, the process of shi3ing reliability and risk analysis in larger, more complex organizations and industries may be even more di#cult. A3er all, according to LinkedIn, the entire Houston Astros organization employs around seven hundred people. 2 Industries such as healthcare, oil and gas, water treatment, education, and mining encompass the world’s largest organizations. !is includes thousands of the most accomplished leaders, their most seasoned engineers and scientists, and tens of thousands of employees. A bigger challenge, yes, but also a bigger opportunity!

Since our decisions have become more complex, and a$ect more people than ever before, they are more crucial than at any time in history. Shi3ing our decision process toward quantitative methods will not only make the world more reliable but allow more people to focus on unlocking even more creative and forward-thinking ideas. !is could make the quanti"cation of crucial decisions the biggest advancement in the world of data science. !is book lays out the bold ideas to start.

2 “Houston Astros,” LinkedIn.com, https://www.linkedin.com/company/houston-astros/.