How to Become a Data-Driven Organization

We recently wrote an emerging technology guide for health insurance CEOs. When we examined those technologies – everything from artificial intelligence to predictive analytics to the Internet of Things (IoT) – most of these technologies rely on data. As a result, if you aren’t a data-centric organization, you’ll be less likely to reap the business benefits of these emerging technologies.

With that in mind, here are 4 steps to take to become a more data-driven company:

Define Your Issues

When the Houston Rockets hired Daryl Morey as their general manager, he lacked a strong scouting background. But the Rockets admired his ability to take data and turn it into insights. The Rockets initially wanted Morey to improve the team’s drafts. To do that, Morey turned to data. Specifically, he created his own model to value players. That model examined statistics like steals and rebounds per minute instead of popular metrics like points per game or assists per game. Using that model, in his first draft Morey found two starter caliber players picking at 26 and 31. The odds of drafting a good player at those positions were between 5% and 8%.

Data helps solve problems. Initially for Morey that meant determining which attributes are most likely to predict the success of NBA basketball players. For your business, you may have any number of issues that you can put data to work trying to solve. You could leverage data to determine which unhappy customers are likely to churn. You might want to identify when your sales team should contact a lead. Or, you could use it to identify your best customer service representatives. Whatever the case, create a list of issues you want to solve. Then, identify the data elements that may help you better understand the issue. Finally, determine the metrics you’ll use to measure the effectiveness of the solution so you can continually improve your data model and ultimately promulgate a continuous improvement process.

Culture Matters

When the Minnesota Gophers hired PJ Fleck to coach the Minnesota Gophers football team, many local sportswriters panned the hire. They found his overly enthusiastic attitude off-putting. They mocked his “Row the Boat” slogan. Yet, Fleck was able to create a winning culture. He finished with an 11-2 record and a bowl game victory, one of the Gophers’ best seasons in 50 years, in just his third season as the head coach. Why was Fleck so successful? He instilled a new culture within the Gophers football program, a culture that focused on team unity and player leadership.

For organizations interested in becoming data-driven companies, a data-centric culture needs to start at the top. If your executives aren’t using data to make decisions, your employees aren’t going to, either. For example, the Entrepreneurial Operating System outlined in the book Traction dictates that there are core metrics to be reviewed at every weekly meeting. Businesses set goals based on those metrics. If those goals aren’t met or there’s anything concerning in the core business metrics, it is discussed during a portion of the meeting dedicated to business issues. As a result, you’re making decisions and starting conversations based on metrics. Each organizational unit has metrics that they monitor. And they’re solving issues based on business data.

For Fleck, creating a player-led, unified team also has recruiting benefits, which help propel the team to winning records. A data-driven culture helps you better identify and solve problems. But it will also help you attract data scientists who want to work on new and inventive projects and problems.

The bottom line: if you want to become a data-driven organization, you need to create processes and meeting rhythms that leverage data to start conversations and make decisions.

Let Experts Manage the Data

The documentary Grizzly Man explored the untimely demise of Timothy Treadwell. Treadwell was a self-proclaimed bear enthusiast and environmentalist. Treadwell spent much of his time near bears while living in Katmai National Park in Alaska. His mission was to protect bears. But according to a research ecologist with the Alaska Science Center, he was “breaking every park rule that there was in terms of distance to the bears, harassing wildlife and interfering with natural processes.” In the summer of 2003, Treadwell was killed and eaten by a bear.

Nothing as dire as death will occur if you don’t let your data experts manage and distribute data. But you could certainly waste time and lose revenue. Instead of giving everyone in the business access to data to answer the questions that they have, leverage your data team to create structured data sets that are easy for your teams and customers to consume. You want your teams to have access to data, but you don’t want to overwhelm them with that data so that they’re paralyzed about what to do. Leverage your data teams to prepare the data that will make it easy for the rest of the organization to manipulate to their own ends.

Think Simple and Actionable to Start

Not many people are aware that Apple created a gaming console in the 90s. Called Pippin, it routinely ranks as one of the worst 25 tech devices of all time. It sold only 42,000 units in a year. The Nintendo 64 sold nearly 500,000 units in its first three days. Why was it so bad? Apple tried to do too much, which increased the price and didn’t meet consumer expectations for a gaming console. They included an Internet connection, even though reading text on a TV screen was difficult due to the quality of TV connections available at the time. It was also complicated to use compared to other consoles.

Simplicity wins with data projects as well. A lot of businesses create overly complex data projects that, at the end of the day, don’t offer real actionable insight. Most organizations likely have an abundance of data projects they’d like to create. The key to success is prioritization and part of the prioritization process should involve the viability of the solution in production as well as the development timeline. Engineering a proof of concept for solutions requiring your data team can help scope the complexity as well as deliver a better understanding of the project. Once the simple data project is complete, you can build more complex data volumes, data models, and other enhancements like artificial intelligence to expand the capabilities of the project. Starting small and building modularly delivers incremental wins that improve organizational morale.

Certifi’s health insurance premium billing and payment solutions help healthcare payers improve member satisfaction while reducing administrative costs.

 

Emerging Technology: A Health Insurance CIO's Guide

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