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What is Data Driven Decision Making? A quick intro

Apr 27, 2022 Big Data

Data driven decision making (DDD) refers to the practice of basing decisions on the analysis of data, rather than purely on intuition. For example, a marketer could select advertisements based purely on her long experience in the field and her eye for what will work. Or, she could base her selection on the analysis of data regarding how consumers react to different ads. She could also use a combination of these approaches. DDD is not an all-or-nothing practice, and different firms engage in DDD to greater or lesser degrees.

The benefits of data-driven decision-making have been demonstrated conclusively.
Economist Erik Brynjolfsson and his colleagues from MIT and Penn’s Wharton School conducted a study of how DDD affects firm performance (Brynjolfsson, Hitt, & Kim,2011). They developed a measure of DDD that rates firms as to how strongly they use Data Science, Engineering, and Data-Driven Decision Making data to make decisions across the company. They show that statistically, the more datadriven a firm is, the more productive it is—even controlling for a wide range of possible confounding factors. And the differences are not small. One standard deviation higher on the DDD scale is associated with a 4%–6% increase in productivity. DDD also is correlated with higher return on assets, return on equity, asset utilization, and market value, and the relationship seems to be causal.

In 2012, Walmart’s competitor Target was in the news for a data-driven decision-making
case of its own. Like most retailers, Target cares about consumers’ shopping habits, what drives them, and what can influence them. Consumers tend to have inertia in their habits and getting them to change is very difficult. Decision makers at Target knew, however, that the arrival of a new baby in a family
is one point where people do change their shopping habits significantly. In the Target analyst’s words, “As soon as we get them buying diapers from us, they’re going to start buying everything else too.” Most retailers know this and so they compete with each other trying to sell baby-related products to new parents. Since most birth records are public, retailers obtain information on births and send out special offers to the new parents.


However, Target wanted to get a jump on their competition. They were interested in whether they could predict that people are expecting a baby. If they could, they would gain an advantage by making offers before their competitors. Using techniques of data science, Target analyzed historical data on customers who later were revealed to have been pregnant, and were able to extract information that could predict which consumers were pregnant. For example, pregnant mothers often change their diets, their wardrobes, their vitamin regimens, and so on. These indicators could be extracted from historical data, assembled into predictive models, and then deployed in marketing campaigns.

Another case was in 2004 when Hurricane Frances was on its way, barreling across the Caribbean, threatening a direct hit on Florida’s Atlantic coast. Residents made for higher ground, but far away, in Bentonville, Ark., executives at Wal-Mart Stores decided that the situation offered a great opportunity for one of their newest data-driven weapons … predictive technology. A week ahead of the storm’s landfall, Linda M. Dillman, Wal-Mart’s chief information officer, pressed her staff to come up with forecasts based on what had happened when Hurricane Charley struck several weeks earlier. Backed by the trillions of bytes’ worth of shopper history that is stored in Wal-Mart’s data warehouse, she felt that the company
could ‘start predicting what’s going to happen, instead of waiting for it to happen,’ as she put it. (Hays, 2004)

Data Science for Business
by Foster Provost and Tom Fawcett