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The Seven Pitfalls of Data-Driven Decision-Making (And How to Avoid Them)

The promised powers of incorporating data into decision-making read like an advertisement: Make decisions better, faster, and more accurately! Minimize uncertainty and maximize returns! Gain agility and accountability! Facilitate innovation and disruption in all the right ways!

And in reality all the hype is as advertised—when data is done right. But when data is mishandled, ignored, or even tortured, it no longer delivers the same superlative benefits.

A man in black and white pointing to a graph showing data behind him

Through BYU Analytics, a consultancy run by MBA students, BYU Marriott is preparing grads who know how to best make data-driven decisions. Check out these cautions from professors and alumni to ensure that your data is living up to its promises.

Pitfall No. 1: Not Using Any Data

Guts are good at telling us when we’re hungry or when we’ve made a mistake, but when it comes to making changes that affect profits, performance, and people, it’s better to rely on something a little more tangible.

Jeff Dotson, BYU Marriott associate professor of marketing and faculty advisor to BYU Analytics, is perplexed by the many companies that still do not use data in their decision-making processes. “There are a lot of managers who still operate in a mind-set that existed twenty years ago: making decisions based upon gut reactions and not necessarily harnessing the value of data to make those decisions,” he says. “I don’t know that business has fully embraced the benefits of data-driven decision-making.”

Pointing to the vast amount of data available in the digital age, Jeff Dotson finds the slow adoption of good data practices surprising. “There was a big shift [in available data] once social media achieved scale,” he says. “People are not using the internet to provide information to marketing researchers; they are using it to buy, to research, and to share information.”

In other words, the internet is not just a medium to get people to answer survey questions but also a place where actual consumer behavior can be observed, tracked, and better understood. That quantifiable information allows managers to look beyond their guts to make evidence-based decisions.

“Humans are really incredible, capable computers,” says Daniel Snow, associate director of BYU Marriott’s MBA program and associate professor of global supply chain management. “But we sometimes think we know more than we know.”

Snow notes that this hazard is particularly acute in the aspects of business in which human judgment or assessment is constantly required. “In human resources, for example, people are trying to predict who would be good at something, but people are notoriously bad at predicting. All kinds of biases creep in,” he says. “So in some ways data helps us look past our biases.”

Of course, data can’t solve everything, but it can provide a push in the right direction. “Data-driven decision-making does not guarantee the correct decision, nor does it guarantee that decisions aren’t still biased in some way,” echoes Jeff Larson, BYU Marriott associate professor of marketing. “But well-reasoned decisions based on good inferences from the right data should lead to more accurate decisions most of the time.”

In short, if you aren’t leveraging data, you should be.

Pitfall No. 2: Assuming More Is Better

“Some people feel you can gather everything and then parse it out later,” says Joanie Westwood, a data-science consultant and 2016 MBA alum. “But this can be challenging and overwhelming.”

A black and white man thinking while holding papers

And, frankly, unhelpful. Issues can often be illuminated by smaller amounts of good data, says Marc Dotson, BYU Marriott assistant professor of marketing and Jeff Dotson’s brother. “The amount of data does not determine the potency of the insight you can gain from it. People often think that data alone has value, and that is an expensive misconception,” he says. “You shouldn’t just collect all information. You should decide what would be relevant in the decision-making process.”

A little planning before collecting data can go a long way, particularly when it comes to communicating the results. “Sometimes people show data for the sake of showing data. They assume the more data, the better,” says Jorge Ramirez, a 2015 MBA alum and product manager for in Provo. “But a better approach is to tell a story with the data. It’s not how much data you have but how well your data is connected.”

Pitfall No. 3: Not Asking the Right Questions

Collecting good data—and digging into it—starts with asking smart questions. “If you aren’t asking the right questions, looking in the right places, or doing things the right way, the data can be useless or, even worse, misleading,” says Shayla Barber, a 2016 MBA grad who is now an event marketing manager for Adobe in Lehi, Utah. “Define your questions and goals for your data very clearly before you start to collect and analyze. Making sure you have a clear objective can help you stay focused and help you avoid the ‘garbage in, garbage out’ downfall.”

To avoid this, Marc Dotson recommends starting with a specific problem and gathering data that informs that problem.

Ramirez suggests managers follow the pain points to find and define problems. “The more pain you experience with a certain problem, the more valuable the solution for it. To get the most out of your data, you need to find the painful problems,” he says.

According to Ramirez, that means developing a habit of curiosity and being willing to learn from the data of areas that are not your own.

Pitfall No. 4 Failing to Connect the Dots

Though too much data can muddle good decision-making, it’s vital that companies share their data across core competencies. Looking beyond a data set’s initial label can yield surprising discoveries.

“Sometimes people think about data in silos: financial data, HR data, sales data, product data, operations data, etc. The truth is that the most interesting questions are answered by combining different data sets,” Ramirez points out. “Unfortunately, often companies don’t combine data, or if they do combine it, they don’t make it available to other teams.”

Ramirez offers an example from a time when he was working in a business strategy and operations role. He was tasked with improving a particular metric that the company had identified as high priority. Using a time-series analysis and leveraging top-down statistical methods, he found a distinct negative trend for the small-business segment of the company’s customer base.

When Ramirez and his coworkers dissected the data, they discovered that the trend started shortly after a large internal organizational change had taken place. They were able to reverse the negative trend by connecting two seemingly unrelated dots: small-business clientele and internal company processes.

“A great analysis usually prompts interesting questions. If you’re inspiring insightful discussion with the data, you’ve succeeded,” Ramirez says.

Pitfall No. 5: Focusing on the Wrong Metrics

In the age of big data, the most readily available data sets aren’t always the most informative. “The most-common pitfall I see is focusing on ‘vanity metrics’ over substantive key performance indicators,” says Larson, who coauthored the textbook Internet Marketing Essentials.

Key performance indicators—also known as KPIs—are the critical measurements of successful performance, such as the percentage of visitors to a website who actually make a purchase or the average value of each order. Vanity metrics, on the other hand, track the number of Facebook page likes, retweets, or page views.

“KPIs should line up with the steps of a customer journey, and the customer journey needs to be followed all the way to the end purchase—not just through the discovery stage, in which people visit the website or like the Facebook page,” Larson says.

He recommends examining what behaviors are crucial to achieving the desired outcome and then analyzing the data that most accurately gauges whether or not this behavior is happening.

Pitfall No. 6: Telling the Data What to Do

When it comes to managing data, 2016 MBA alum Collin Burton, now a data scientist at Pluralsight, remembers one experience in particular that he had as an intern. Burton’s manager at the time had a hypothesis about an underperforming factory and wanted to find data to support his assumption. Burton was given the task.

“When I brought data that showed the opposite of his hypothesis, he rejected it because it didn’t support the story he was trying to tell,” he recalls.

This kind of attitude toward data is a slippery slope, Westwood warns. “Companies should be very careful not to use data-driven decision-making to try to validate decisions they’ve already made but rather use it to come to conclusions based on what the data is telling them,” she says.

Pitfall No. 7: Letting Data Dominate

For all the promise of data, it is ultimately only as useful as the humans delving into it. “Lots of data does not substitute for smarts or for using it correctly,” Snow says. “You can’t take humans out of this completely. Having lots of data and a few people is not a substitute for knowledge.”

Westwood says that a manager’s experience and knowledge should support and guide the data-gathering process to ensure that the right data is gathered in a consistent and usable format.

That’s the correct balance, Snow says. “If the decision could be made only on the data, we wouldn’t use managers,” he explains. “We would just plug the data into an algorithm to make the decision for us.”

Marc Dotson affirms that running a business isn’t a process that can be automated. “Data is not making the decision for you; it’s just informing you so you can make the right decision,” he says.

And in the end, that’s all business leaders want: to make the right decisions for their shareholders, customers, and employees.


Written by Lisa Ann Thomson

About the Author
Lisa Ann Thomson is a data-driven freelancer based in Salt Lake City. As a writer for more than twenty years, she has long understood that her final products are no better than the data she amasses, analyzes, and synthesizes into stories and articles. Her work has appeared in a wide range of local and regional business, travel, and lifestyle magazines and websites. Closer to home she has written extensively for BYU Magazine and the Ensign.

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