February 8, 2019

Your Quick-Start Guide to Data-Driven Decision Making

By 
Cynthia Kenworthy

In business, you constantly have to make decisions — from how much raw material to order to how to optimize retail traffic for changing weather. In days gone by, you might have consulted the person who had been around the longest for their best guess; for a more scientific approach, you might have also looked at sales records. Today, companies are finding that the best answers to these questions come from another source entirely: large amounts of data and computer-driven analysis that you rigorously leverage to make predictions. This is called data-driven decision making (DDDM).  

To quickly get you up to speed with data-driven decision making, we’ve pulled together this action-oriented guide that explains the technology and includes expert tips as well as easy, step-by-step how-tos.

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BUILDING A DATA-DRIVEN BUSINESS CULTURE

Organizations that have successfully built a culture founded on data-driven decision making say the most important thing to remember is that your company will not transform overnight. As Klipfolio’s Marketing Director Jonathan Milne says, you have to “be patient, take your time, and start small.”

To create the conditions for a data-driven culture, you must do the following: collect high-quality data within a useful timeframe; make sure the costs of your information architecture are reasonable; and be sure the data is easily accessible. Some experts urge companies to begin collecting data as early as possible, so they have benchmarks, while others caution against a scattershot approach.

Moreover, you need to know how to present, visualize, and talk about data. Create an internal data dictionary that outlines the content, format, and structure of your databases. Make sure that managers have at least basic statistics training.

Tom O'Neill

Tom O’Neill, CTO and Founder of business intelligence platform Periscope Data, has worked with more than 1,000 teams of data scientists and offers some advice based on the most successful teams:

  • Train everyone at your company to use and interpret data accurately. Recognize that no one is born data literate. All employees should know how to determine if they're using metrics, goals, and conclusions correctly.
  • Analysts must work tightly with their business counterparts and drive change together. Intentionally include different levels of experience and perspectives throughout the process to reduce the biases that homogenous groups exhibit. Using this mix of people will minimize blind spots and maximize potential solutions.
  • Encourage curiosity in your culture. Everyone should be empowered (and expected) to ask a lot of questions, and there need to be resources available to provide answers. You should regularly dissect KPIs into their components to provide new perspectives on what the non-curious might see as a simple sum or projection.
  • Use data to make testable, tactical predictions. Then, take actions based on those predictions and feed the results back into your process to improve future decisions. The data-driven decision engine looks a lot like the scientific method. Historical analysis should be focused on learning why something happened, not just reporting what happened.

Your management style must also change as your culture evolves. Data scientists Jeff Bladt and Bob Filbin say that in organizations making the transition to being data driven, there are always people who attempt to subvert the change and those who embrace it. People who adopt the data-driven approach do so to improve either their perceived or their actual performance, Bladt and Filbin say. People who have low organizational prestige but are high performers will welcome the use of data, believing that it will make their contributions more evident. With encouragement, these individuals can turn into data champions.

Conversely, highly regarded team members who are actually poor performers remain distrustful of data, which they fear will reveal their shortcomings. Many of these people are unlikely to change their views, and, over the long run, they will probably need to leave the company for it to become truly data driven.

Data-driven decision making needs to be embraced at the upper levels if it’s going to take root in the organization. Given that the successful implementation of DDDM requires particular qualities, such as a collaborative style and an openness to solutions that may come from anywhere in the ranks, the process itself demands practitioners who model from the top down and across departments.

Encourage consistent metric tracking (such as KPIs) throughout your organization as a way to get people to focus on the relevance of data to business objectives. Building a dashboard that gives you a fast snapshot of those KPIs can aid this effort.

In addition, you may be wondering where in your organization responsibility for data-driven business intelligence should reside. (This responsibility is also referred to as data governance.) Cross-departmental data “competency centers” are much more likely to be responsible for data in best-in-class companies (52 percent) than they are in average companies (40 percent) and stragglers (28 percent), according to a survey regarding corporate data governance. When asked, “Who manages and governs data for decision making?” 60 percent of respondents said it was the finance department, while 41 percent said it was the IT department, and, again, 41 percent said it was a competency center.

Strong data governance assists DDDM. You can support that governance by fostering agile IT infrastructure, cross-departmental data management, a collaborative decision culture, and the use of KPIs throughout your organization.

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