In 2002, the Oakland Athletics baseball team was facing a problem. They didn’t have enough money to go after big-name talent, but they needed to turn their performance around. How could they make a decision about who to put on the team that didn’t involve chasing the flashiest players out there? As luck would have it, general manager Billy Beane happened to meet Peter Brand, a Yale-educated economics graduate who had a crazy idea. Brand told Beane to ignore the flashy players, cast aside the advice of experienced talent scouts, and instead follow nothing but the raw data. They faced serious opposition to this decision, but they persevered and ended up with a record-breaking team.
If all that sounds familiar, it’s because this story became an Academy Award-nominated film starring Brad Pitt called Moneyball. Eventually, Brand’s new plan for chasing talent got out, and now other teams are following suit. In fact, nine out of the ten teams that made the playoffs in 2015 were either all-in on the model or at least following it loosely.
What does this story have to do with understanding business? Well, it’s a cut-and-dry example of data-driven decision making (DDDM), and it illustrates why it’s so important to take this decision-making model into account… not just for baseball, but for everything.
How People Make Decisions
Human beings make decisions every day. From the mundane (what will we eat for lunch) to the personally earth-shattering (should I leave this job for a new one) to those that impact all of society (who should I vote for), we are faced with millions of choices throughout our lives, and we have to determine how to best make each one. There are a few different approaches people can take:
Often, we make decisions based on our “gut instinct.” We pick the one that “feels” right, and we believe that our internal emotional response is an accurate gauge of what’s best.
Sometimes, we rely on what has happened in the past. We think back to former experiences (or research others’ experiences) and make our next decision based on what we think is the likely outcome.
In the business world, many of our decisions are made based on HiPPOs, which stands for the highest paid person’s opinion. In other words, we adopt a sort of trickle-down version of success that suggests the person at the top knows what’s best and that their decisions will ripple through the business in positive ways.
Based on data
Finally, we can make our decisions based on data-driven outcomes, following the numbers and statistics to determine how to move forward.
A data-driven approach overwhelmingly produces the most positive outcomes with a stable replicability. Intuition is fast and allows us to make a decision almost instantly. Relying on past experiences can make us feel safe. HiPPO-driven decisions tend to crop out of traditional models of authority. If we could find a way to mirror the speed, security, and institutional support for data-driven models, we’d have a much more efficient and effective way of making decisions.
Availability of DDDM
Until recently, relying on DDDM for many decisions simply wasn’t practical. Data is unwieldy. It takes a lot of time to collect. It can be difficult to analyze accurately. It can require a whole team of educated professionals just to make it make sense. It can take a long time to gather enough data to make a decision worthwhile.
People often avoid data-driven decisions because they’re not the fastest, and they require a lot of work on the front end.
Today’s world is interconnected, and due to technological advancements like cloud storage, improved user interface for online data collection, and the rapid spread of internet accessibility, data is more easily gathered than ever before.
These same technological advances make it possible to analyze and draw meaningful conclusions from this data in a matter of minutes rather than months, and new software allows this to be done affordably and without having to hire in-house analysts.
In other words, the best decision-making method has become increasingly accessible, affordable, and user-friendly.
Examples of DDDM in Business
There are plenty of example of the successful implementation of DDDM in businesses across a range of industries. While these businesses deal with different types of customers, different production demands, and different economic forces, all of them recognized that using a data-driven approach would improve their success:
Southwest is known for being a little scrappy and off-the-beaten-path in the airline industry. When every other company started dropping ticket prices and instead charging customers fees for everything from checking bags to reserving their seat, Southwest made different decisions. Their approach has paid off, giving them some of the most loyal customers in the industry. What makes them different? Their data-driven approach to customer service. Southwest has been consistently and meaningfully using data collection to determine which features to roll out, how to market them to consumers, and how to make their flights safer.
In November of 2005, Google launched Google Analytics, a powerful and popular tool that allows anyone with a website to track data in dozens of different ways. With Analytics, web creators can see which content drives traffic, where users go after leaving a page, what time people are viewing the site, where they live, and how much time they spend there. This collection of data allows web content creators to tailor their marketing strategy to specific sets of users or see where they need to do more outreach. Success stories from using Google are plentiful, and sites like PBS and Travelocity are among those who used the tool to do what they already did better than they were doing it before.
Toyota is famous for its “lean manufacturing” model, a hallmark of efficiency that was developed using data-driven decision making. Toyota’s model focuses on eliminating waste at every possible point, and that includes measuring the distance that a piece on an assembly line travels before making it to the next step in the process and even counting how many times an assembly line employee bends at the waist while assembling a product. Each of these measures becomes part of the overall picture to help decrease waste and make the process streamlined.
DDDM in Human Resources
At this point, most companies accept that DDDM should be the goal. It’s clear from the plethora of examples across every industry that businesses adopting DDDM perform better, solve problems, and increase their stability. However, DDDM hasn’t been as readily adopted in some areas, including human resources.
There are two big hurdles to making data-driven decisions in HR: getting the data in the first place and making sense of it once you have it.
When it comes to the decision-making common in the HR department (hiring, firing, training, team assignments, etc.), we still tend to rely upon instinct or HiPPO models instead of tapping into the vast resources available through DDDM. The reason is that human resources have traditionally been harder to mine for data. People are trickier to measure than machines, and the process of collecting data through surveys, interviews, and performance reviews is time-consuming and skill-based. Even once we have that data collected, it can be difficult to separate the statistical noise from the meaningful information and make a decision that is fair, justified, and useful.
Make Decisions About Your People
Just like technological advancements are transforming DDDM in other areas, HR now has tools at its disposal to adopt data-driven practices with ease. Buzzuzz is designed to bring a clear, easy-to-use interface into your business so that data collection is simple and intuitive. Then we take that data and give it to you in manageable, customizable formats so that you can use it to make decisions about your people that will meet your needs.
Buzzuzz is designed to be a holistic system that not only gives valuable information to those in HR management but also empowers people at all points in the process to be informed and engaged. Individual employees receive anonymized data from meaningful, data-driven peer reviews. Since employees can use the system to monitor and evaluate their own progress, Buzzuzz creates opportunities for data-driven decision making every step of the way, making your workplace more efficient, collaborative, and effective from the ground up.
There is no reason to rely on outdated decision-making practices when the tools you need to bring DDDM to your human resources department are literally at your fingertips. Human resources represent some of the biggest investments and most valuable assets that a company can have. Isn’t it time that you handle HR decisions using all of the data and tools available to you?