In today’s business landscape, data science has become a critical asset. The proliferation of data, driven by advanced computing and the Internet of Things, allows companies to meticulously track every facet of their operations. However, many leaders feel overwhelmed by this constant stream of data and hesitate to engage with what they perceive as a purely technical domain.
However, successful leadership in the age of data requires more than just delegation to data scientists. It demands data literacy.
A data and analytics leader needs a “working knowledge” of data science, enabling them to distinguish valuable insights from noise and identify areas where analytics can deliver tangible value. This guide explores the key principles that enable leaders to navigate the world of data effectively and build a data-literate organization.
The Importance of Data Literacy for Leaders
Data literacy empowers leaders to move beyond simply accepting data at face value and enables them to make informed decisions based on solid evidence. Developing data literacy also protects against making choices based on flawed assumptions. According to Florian Zettelmeyer, a professor of marketing and faculty director of the program on data analytics at the Kellogg School, a primary reason for analytical failures is presenting non-experimental data as experimental findings.
Start with a Clear Business Problem
A common pitfall is collecting data without a specific purpose. Leaders must view data generation as a strategic imperative, integrating analytics into the core business plan. Data collection should always be driven by a well-defined question or problem.
Whether a software company aims to optimize its advertising campaign or a fast-food chain seeks to improve its global operations, data collection must align with the specific business challenge. As Zettelmeyer points out, relying solely on data incidentally generated during business operations is unlikely to yield breakthroughs. While collecting data such as consumers’ browsing behavior is essential, customer interactions should be designed to capture the metrics needed for informed analysis.
Leaders cannot simply delegate analytics to data scientists. They must actively participate in identifying the problems that need solving and determining how analytics will be integrated into operations. Ultimately, executives are responsible for making decisions, so they must play a central role in determining what to measure and what the numbers signify for the company’s overall strategy.
Understand the Data-Generation Process
A crucial step in becoming a data-literate leader is understanding the origins of the data. Leaders should be skeptical of the idea that data science inherently produces objective truth.
“There is a view out there that because analytics is based on data science, it somehow represents disembodied truth,” Zettelmeyer says. “Regrettably that is just wrong.”
To distinguish good analytics from bad, leaders must thoroughly understand the data-generation process. Zettelmeyer observes that many managers exhibit a bias towards deferring to experts when presented with results derived from complex data analytics. It’s vital for leaders to develop a “sixth sense” for the insights that can be legitimately drawn from data. To make informed decisions, they should step back and ensure a solid foundation of understanding.
For example, when comparing groups, it’s essential to know how those groups were formed. A marketing department evaluating ad effectiveness might compare consumers exposed to the ad with those who weren’t. Randomly selected groups are “probabilistically equivalent,” forming the basis for sound analytics. However, if exposure was based on prior product interest, the analysis becomes flawed, obscuring the true impact of the ad.
Consider a hospital replacing ultrasound machines. Data from wireless sensors reveals that the new device takes longer to use than the old one. This analysis overlooks a crucial factor: the experience levels of the technicians. Novice technicians, naturally slower, disproportionately used the new device, skewing the data. Zettelmeyer identifies this as “confounding technician experience with the speed of the device.” The analytics failed because it ignored fundamental questions: Why do technicians choose one machine over the other? Are the usage scenarios comparable? And if not, was the analysis adjusted to account for these differences?
Understanding the data-generation process can also reveal reverse causality. A company might find that promotional emails drive sales, with more emails leading to more purchases. However, the data might not reveal that the company targets loyal customers (those who bought recently, frequently, and spent more) with more emails. In this case, the causality is reversed: more purchases lead to more emails, rendering the data useless for determining the true impact of email marketing.
Leverage Domain Knowledge
Beyond ensuring purposeful data generation, leaders should apply their business acumen to interpret results. They should critically assess whether the findings align with their understanding of the business, asking, “Knowing what I know about my business, is there a plausible explanation for that result?” Analytics should not be conducted in isolation. Data scientists may lack the deep domain expertise that leaders possess, and analytics is no substitute for understanding the intricacies of the business.
An auto dealership might attribute a sales increase in February to a promotion. However, a data-literate leader would consider external factors. “But,” Zettelmeyer says, “let’s say what they were trying to sell is a Subaru station wagon with four-wheel drive, and they completely ignored the fact that there was a giant blizzard in February, which caused more people to buy station wagons with four-wheel drive.” In such cases, simply having the data is insufficient. Leaders must apply their domain knowledge to interpret the findings accurately.
From Thinking to Knowing: Building a Data-Literate Culture
Decision-making in business is undergoing a transformation akin to the shift towards evidence-based medicine in healthcare. As big data and analytics revolutionize business, leaders with a working knowledge of data science will gain a distinct competitive advantage. Beyond being gatekeepers of their own analytics, leaders should foster data literacy across their organizations. A data-literate company is one that learns quickly and generates value across all departments.
“If we want big data and analytics to succeed, everyone needs to feel that they have a right to question established wisdom,” Zettelmeyer says. “There has to be a culture where you can’t get away with ‘thinking’ as opposed to ‘knowing.'”
Developing such a culture poses a significant challenge for leaders. Organizations often resist admitting the need for change, and many managers lack the confidence to lead with analytics. This must change.
The shift towards data literacy demands a fundamental shift in mindset. As Zettelmeyer concludes, “Can you imagine a CFO going to the CEO and saying, ‘I don’t really know how to read a balance sheet, but I have someone on my team who is really good at it.’ We would laugh that person out of the room. And yet I know a whole bunch of people in other disciplines, for example, marketing, who, without blinking an eye, would go to the CEO and say, ‘This analytics stuff is complicated. I don’t have a full grasp on it. But I have assembled a crackerjack analytics team that is going to push us to the next level.’ I think this is an answer that is no longer acceptable.”
The future belongs to leaders who embrace data literacy and empower their organizations to do the same.
