Demystifying The Data-Driven Mindset To Power Better Decision-Making
Decisions are the lifeblood of business. From the c-suite to frontline employees, decisions are constantly being made at all levels within organizations. Some are consequential and irreversible, but many are routine and amendable. No progress can be achieved, or knowledge gained, without making decisions. Management guru Peter Drucker said, “Whenever you see a successful business, someone once made a courageous decision.” While I agree, success is often the result of a series of thoughtful, bold decisions rather than just one.
Until the rise of computers in the 1970s and 1980s, decision-making was primarily shaped by people’s experience and intuition. The birth of information technology added a third element—data—which added precision and objectivity to offset the bias and subjectivity inherent in decision-making up until that point.
However, rather than using data to enhance the quality, speed and efficacy of their decisions, many executives still resist it. A 2020 Accenture/Qlik report found two-thirds of leaders relied primarily on “gut feeling over data-driven insight.” When leaders make decisions without paying attention to the data, their firms pay a price. In a 2021 Alation report, 97% of data leaders stated their organizations had suffered consequences from ignoring data, such as “missing out on new revenue opportunities, poorly forecasting performance, or making bad investments.”
An example of this occurred recently when the audio equipment manufacturer Sonos rushed to market a redesigned product app with insufficient user testing. Instead of modernizing the user interface, the new version removed core functionality that upset loyal customers. Sonos announced its app redesign slip-up will cost the company $20-30 million to repair damaged customer and partner relationships.
While not leveraging data correctly in the decision-making process can lead to problems, data can also make it more difficult. In a recent 2024 Oracle survey, 74 percent of respondents said the number of decisions they make every day has increased ten times over the last three years. Eighty-six percent said the volume of data is making decisions in their professional and personal lives much more complicated. Seventy-two percent admitted the sheer volume of data and their lack of trust in it stopped them from making any decision (a phenomenon known as “analysis paralysis”).
Even though 97 percent of people said they wanted help from data, 70 percent of business leaders said they would prefer a robot to make their decisions. Before abdicating our decision-making responsibilities to machines, we must reevaluate why the current approach to data-driven decision-making (DDDM) has mostly failed.
How a misaligned focus is undermining data-driven decision-making
When I started researching the current state of DDDM, I discovered most of the focus was heavily centered on the steps that preceded a decision. In multiple DDDM articles and diagrams, ‘making a decision’ wasn’t listed as a step in the outlined DDDM processes. If it was explicitly mentioned, there was little said about the step as it was viewed by the authors as obvious and straightforward.
In practice, the decision step in the DDDM process is anything but easy and automatic. For too long the technical aspects of getting the right data in front of decision-makers has been viewed as the primary challenge. As a result, the essential human component of making decisions based on data is overlooked. Everyone assumes people will know how to use data when making decisions and will do so rationally. As behavioral economist Dan Ariely noted, “People are irrational—predictably so.” Lack of attention to this simple but crucial human step will continue to derail organizations’ attempts at becoming more data-driven.
Some will argue that data literacy training will address this problem. However, data literacy today is plagued by the same technical bias that has hampered DDDM. Most programs emphasize developing technical data skills—tools and techniques—over fostering a data-driven mindset grounded in culture and thinking. It’s like training cyclists for strength and power when what they need is endurance and stamina. You can’t expect them to excel if the coaching doesn’t align with their specific needs.
Others may see AI as the remedy to this situation—handing over decision-making entirely to machines. While AI could handle low-level, tactical decisions more efficiently, I’m not sure humans are ready to take our hands off the wheel for more strategic ones. Like my thoughts on data storytelling, I don’t envision a world where decisions are fully automated but rather one where AI enhances a human-led decision-making process.
Human judgment, which balances broader context, situational nuances, ethical considerations, empathy, and compassion, remains crucial. For AI-powered decision support, humans must scrutinize not only the data but also the logic and criteria used by AI tools to generate their recommendations. I’m unsure which is scarier: the overreliance on technology for decision-making, which leads to less critical thinking and accountability, or making decisions based on potentially flawed and opaque ‘black boxes.’
To bridge the decision gap in the DDDM process and re-calibrate the focus on this uniquely human responsibility, I’ve developed a comprehensive framework called the Human-Data OS. It is designed to enhance how people approach data-driven decision-making, ensuring data and technology function as tools rather than crutches or impediments. By demystifying what it means to be ‘data-driven,’ I hope it can inspire changes that unlock the full potential of DDDM within organizations, large and small, guiding them toward more informed, balanced, and human-centric decision-making.
Human-Data OS: A framework for a data-driven mindset
Before introducing the framework, I recognize some people may be uncomfortable with the term “data-driven” when applied to people and decision-making. They may prefer “data-informed” because it represents data being an integral component but not necessarily the sole or controlling determinant of decisions.
I prefer data-driven because it implies leaning into the numbers and applying more evidence-based rigor to decisions that are generally susceptible to subjective judgment. It also suggests that you have a destination in mind—the data is taking you somewhere, such as improving your customer service or expanding your market share.
Regardless of your preference, this framework will highlight 12 traits that contribute to a comprehensive data-centered approach to decision-making. Just like an operating system governs how a computer functions and interacts with other components, a data-driven mindset will govern how an individual processes, interprets, and acts on data. Within the Human-Data OS framework, I’ve grouped the attributes into three core areas: Data, Outward, and Self/internal.
Data: Assessing the foundation of your decisions
This first category represents how you approach and prioritize data as a key input in your decision-making process.
- Thinks strategically about data. Evaluates data from a broader and more grounded perspective such as the overall strategy and business goals, not just based on a personal agenda or ambition.
- Thinks critically about data. Analyzes data with a discerning eye, raising questions about its accuracy, relevance and sourcing. Avoids taking the numbers at face value without considering context or potential bias.
- Maintains a constant focus on data. Treats data as a golden thread woven through every decision-making stage—before, during, and after. Consistently relies on data as a guiding force, rather than selectively when it’s convenient or easy. Embraces a test-and-learn approach.
- Understands data’s limitations. Recognizes that available data may be imperfect or incomplete but is still useful. Acknowledges when desired data isn’t available or sufficiently trustworthy. Is ready to rely on their best judgment as needed.
Outward: Influencing others with data
This second category focuses on how you emphasize the importance of data and how you communicate its role in your interactions with others.
- Champions data with others. Advocates the importance of data in decision-making across the organization. Leads by example, rewards the discovery of insights and promotes data’s ethical use.
- Fosters psychological safety. Fosters a safe environment where team members feel safe to share ‘bad news,’ question assumptions and admit uncertainties without fear of judgment. Promotes open discussion of the numbers.
- Holds all accountable to numbers. Ensures everyone is held responsible for their decisions and actions, especially themselves. Encourages learning from both successes and failures.
- Shares knowledge and insights. Actively disseminates findings and learnings with others to elevate the collective understanding and improve decision-making. Doesn’t hoard information and insights for personal gain only.
Self/Inward: Harnessing your mind
This final category encompasses the internal qualities and self-awareness you need to exhibit with a data-driven mindset.
- Avoids speculation or quick judgment. Patiently waits until more facts and context are available before forming opinions or making judgments. Avoids jumping prematurely to conclusions without sufficient evidence (scientific method).
- Shows curiosity (asks questions). Continuously seeks to explore and understand something by asking insightful, probing questions. Strives to push beyond superficial observations to uncover potential root causes.
- Keeps an open mind, not a closed one. Listens to the data from a growth mindset perspective. Willingly embraces a new position if the data reveals their previous views or assumptions are wrong. Views it as a positive rather than a letdown.
- Values personal introspection. Regularly reflects on own biases and assumptions that might influence data interpretation. Seeks to minimize cognitive biases to remain as objective as possible when making decisions.
When discussing the importance of establishing a data-driven culture, it can be hard to envision how it functions at the organizational level. This framework clarifies the essential attributes of a data-driven mindset at the individual level, making the development of a data-driven culture more concrete and attainable, rather than nebulous and daunting.
The Human-Data OS framework provides a roadmap that your team can use to target areas that need development or further refinement. It will give your team the clarity and direction they need to cultivate the behaviors and practices that are essential for a thriving data-driven culture. As you make progress toward developing a shared data-driven mindset, it’s crucial to remain vigilant and continuously support one another throughout the journey.
Several years ago, a luxury retail brand was introducing a new clearance section to its website for the first time. As the digital team was preparing to run tests on the new page designs, the digital vice president suggested they could skip the testing phase. Due to his extensive experience in off-price retail, he felt confident he knew which page design would work best for discounted merchandise.
His analytics director chastised him for being willing to make an exception to their “test everything” mantra. He had engrained his team with this data-driven practice, and she held him accountable during his moment of weakness. After the VP humbly admitted his mistake, they ran the tests and he discovered his intuition was wrong. The page design he didn’t like was one of their top-performing test results of the year.
Rather than weakening his team’s data culture with an impulsive decision, this data-driven executive reinforced it by adhering to their established testing process. Even if the VP’s intuition proved to be correct, he would have been able to quantify the impact based on the test results while also modeling data-driven behavior to his team.
Data is not the only factor in decision-making. However, it must be actively sought out and integrated into the process for important decisions. The success of DDDM doesn’t just hinge on technology or technical skills—it depends on fostering the right mindset within individuals and then collectively across your organization. Only then can companies truly maximize the return on their data investments and translate DDDM’s potential into tangible, sustainable results.