Six Key Elements for Building a Data-Driven Culture

Leaders of healthy and high-performing organizations recognize that data is a strategic asset that holds promise for substantial competitive advantage.

In a 2021 survey, 83% of CEOs said they wanted their organizations to be more data driven and evidence based. Many leaders see the promise of using data-based insights and predictions to inform decision making, improve business operations, optimize customer and employee experiences, and ultimately drive business results. Yet many are struggling to get there. In 2023, only 26.5% of organizational leaders reported success in establishing a data-driven organization.

Technology, analytics capabilities, data governance, and the data itself are all undoubtedly important parts of the equation. However, building a data-driven organization isn’t about hiring a bunch of tech and stats experts. The most critical part of becoming a data-driven organization is the cultural factor and the necessary shift in mindsets and behaviors to fully embrace data as a strategic asset. In fact, nearly 92% executives cited cultural issues as the greatest obstacle to becoming data driven.

So how do leaders build a data-driven culture? Multiple elements need to be in place to help shift mindsets and behaviors to enable prudent and strategic use of data. Here are some of the considerations and key elements needed to build a data-driven culture:

1. 1. A strategic vision for data and measurement

As in all ways relevant to organizational culture, senior leaders play a pivotal role in building data-driven organizations. Leaders who authentically believe in, align on, and speak to the power of data will have the greatest success in building a data-driven organization. Employees need to hear from executive leaders about why being data-driven matters and how it supports the organization’s strategy and objectives. Check out this article for more insights on understanding the impact of culture.

1. 2. Modeling data-driven practices

Leaders also play an important role in modeling what data-driven behaviors “look like” —not only setting team expectations for how to anchor decisions in data, but also practicing evidence-based decision making themselves. Examples of behaviors to model include exploring data with curiosity, viewing data as a means for asking better questions, and testing hypotheses with data. To learn more about using data for decision making, read this article.

1. 3. Clear data roles and responsibilities

Successful data-driven cultures recognize that data isn’t about having a team of siloed number crunchers sitting in front of a bank of computers. Organizations that successfully embrace the power of data recognize that all employes have a role—whether they’re collectors and sharers of data, analyzers and visualizers, interpreters and sense-makers, end users of data insights, or some combination of these roles. The entire workforce plays a role in using data to drive an organization’s objectives. Adopting this mindset that “everyone has a role to play” fosters a greater appreciation for the lifecycle of data and the various capabilities needed to become a data-driven organization. Want to rethink capabilities at your organization? Read this article to learn how to get started.

1. 4. A data literate workforce

For employees to fulfill the data expectations of their role, they need data literacy to “speak the language” of data. This includes understanding and communicating basic data concepts, such as the terms of descriptive statistics, correlation vs. causation, probabilities, levels of confidence, or sources of error and uncertainty. It also involves thinking critically about data, including how it’s collected, represented, visualized, interpreted, and used. Read this article for insights into how data can be visualized. Organizations can build a data literate workforce through a combination of hiring data literate employees and upskilling existing employees through data science development programs and experiences. For an example of upskilling employees through the work, check out this article.

1. 5. Guiding principles for data

Leaders play an important role in setting the tone for data-driven practices. One way they can shape mindsets and behaviors related to data and data usage is by establishing a set of guiding principles. Consider these examples:

• • 5. • Recognize that data doesn’t provide a definitive answer. It opens the door for additional (and, we hope, more informed) inquiry. Treat the data as a starting place for further exploration, not the end of the discussion.
       • Keep humans at the center. In every decision around data, be intentional in considering the human element. How will the data impact employees, consumers, or other stakeholders? Who will use the data and for what purpose? How do consumers of the data need to receive the data (and in what form) so that it’s most useful to their goal? What are some potential unintended consequences of collecting or attending to certain data? Read this article for more insights on how to make measurement meaningful for stakeholders.
       • Recognize that data is more than numbers. Observations and qualitative information are data too and should be considered along with quantitative data as part of a richer and more holistic interpretation when seeking insights. To learn more about the power of qualitative data, read this article.
    6. A commitment to data ethics

There are many concerns, fears, and even angst around data—including concerns with privacy, data use, and the perception of a “Big Brother” phenomenon. Yet only 44.1% of organizations report that they have solid data and AI ethics policies and practices in place.1 Leaders of a data-driven organization must address data ethics issues directly and proactively, especially when it comes to the use of employee behavior and performance data. Discomfort and fear can be mitigated with commitment, transparency, and intentionality.

One tactic for intentionally addressing data ethics is to craft a data “bill of rights” that defines an employee’s rights when it comes to data collection and use. Regardless of what is included in a data “bill of rights,” the central goal is to strive for transparency and clarity, foster trust, and help promote a data-driven culture. Example items for a data “bill of rights” include:

• • • • You have a right to know what data is being collected about you and why.
      • You have a right to know who is looking at the data.
      • You have a right to know how the data is being used, including the decisions it’s informing.
      • You have a right to access your data.
      • You have a right to correct the data collected about you.
      • You have a right to know findings from surveys you’re asked to participate in, and what decisions or action plans are stemming from those findings.

Of course, if such a “bill of rights” is used, the organization has a responsibility to bring it to life through practices aligned with these rights. 

Conclusion 

Becoming data driven is a long-term journey that can take years. Progress may be inconsistent and can only be developed over time through the building blocks of a strategic vision, leaders as role models, role definition, data literacy, guiding principles for data use, and a commitment to data ethics. 

Developing a data-driven organization requires a heavy focus on the people and the culture to support data-driven mindsets and behaviors. Remember: The entire workforce plays a role in using data to drive an organization’s objectives. As with any strategic objective or change, building a data-driven culture means supporting employees to own their role in activating the strategy. 

Author Bios 

Anna Grome, M.S., is an Industrial-Organizational Psychologist and Principal Consultant at TiER1. With plenty of street cred helping organizations solve tough challenges, Anna has helped design and evaluate solutions that help improve the health and performance of individuals, teams, and organizations – ranging from to leadership development to culture definition and enhancement to measurement strategies. 

Walter Warwick, PhD, is a Principal Scientist and the Director of Applied Performance Research at TiER1. Walter has extensive experience developing computational models of cognition and human performance. He has long worked to improve the methods used by computational modelers to create, validate, and communicate the workings of their models. This experience has given Walter a unique perspective on what it means to practice human-centered data science.