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Have you ever heard the phrase “I don’t do data?” Does this make you wonder why people aren’t comfortable digging into data? This raises deeper questions around the organization’s data culture. Have staff been adequately supported in using data? Have they had access to the right data, presented in a way that is easy to interpret and analyze? Are clear processes in place to ensure data is regularly used to support decision-making? Data is at the heart of implementation. It connects teams and processes across the system. Data is needed for effective decision-making and continuous improvement. This blog will focus on organizational practices needed to build a data culture that can support and sustain implementation.
Data Culture
So, what is data culture and why does it matter? First let’s examine a definition created through AI:
Data culture refers to the shared values, norms, practices, and behaviors within an organization or system that prioritizes the intentional use of data to inform decisions, improve practice, and achieve outcomes (Microsoft Copilot, n.d.).
A little formal, but a pretty good definition. It emphasizes the need to establish shared values, norms, and behaviors within the organization that ensure data is used in decision making and to facilitate ongoing improvements aimed at achieving intended outcomes. Establishing a data culture shifts data use from something that we have to do to something that meaningfully supports what we do. See the difference? Data use becomes a part of everyday practice. To do this, organizations need to intentionally define what a data culture looks like within their system and develop structures and supports that reinforce data use as a valued and routine practice. Let’s explore some key areas within implementation that organizations can focus on to build a strong data culture.
Building a Data Culture
Looking across some resources focused on building a data culture, some common themes emerged, including the importance of leadership, building staff competency, and developing supportive systems. Reflecting these themes, do you see how they relate to the Implementation Drivers which require strong leadership bringing together competencies with organizational structures to develop an infrastructure for successful implementation? Let’s explore how each of these supports building a data culture.
Leadership
Indeed, for many leaders, the challenge is … fostering an environment within an organization where individuals instinctively turn to data any time they must make a decision (Kesari, 2025).
Just as strong leadership is foundational in active implementation, building a data culture requires strong, effective leadership. Leaders play a critical role in building the culture; they are key in creating an environment that values and expects data use. Effective leaders lead by example, modeling a commitment to data-informed decision-making, engaging in activities such as asking data questions, using analysis to dig deeper into the data, and by leveraging various types of data in decision-making (KPMG, 2025). As leaders engage in these data activities, they actively include staff to exemplify how data use as a process meaningfully connects work and reinforces the collective culture within the organization (Rajappa, 2025). By routinely engaging in data activities—not only when required for reporting—leaders help shift the organizational culture from data compliance to one in which data use is valued as a core practice that guides how decisions are made.
Data use needs to become a part of the agency’s norms and values so that it can be cultivated into everyday practice. Building a strong data culture requires establishing a shared definition and common language around data use. Core practices need to be identified and defined to ensure these practices can be used consistently across the agency, with mechanisms developed to monitor these practices over time. Sound familiar? When we approach data use as a Usable Innovation, where practices are defined, operationalized, and measured, we can build effective structures to support and sustain these in everyday practice. Let’s examine some needed structures.
Competency
Building readiness is a key implementation activity that helps foster buy-in and sustained commitment in work. The same attention to readiness is essential when developing a data culture. This requires attending to staff competencies in accessing, analyzing, and using data (Somers, 2024). A component within this is identifying what data is needed by which individuals to effectively use the data within their work. It is likely that within the agency, staff will have different needs which will require varied skillsets and knowledge to effectively use data. Identifying what specific skills and knowledge staff need around data can support developing effective training systems responsive to staff needs.
Training is a good first step that focuses on building knowledge; however, it’s not sufficient on its own to truly build competency. Staff need support to effectively integrate data use into their daily work. A key strategy to provide this support is coaching. Coaching creates structured, supportive, and reflective opportunities for staff to actively apply data in real-time decision-making, building the confidence needed to connect learning to practice, which is needed to make data use a routine way of work.
Organizational
Within an organization, an effective data culture relies on structures that clarify ownership, set standards for quality, and create policies for data use (Knight, 2026). Central to this is developing a robust decision-support data system (DSDS). It is important to recognize that people have different levels of comfort and experience in using data. Building a culture around data use may cause discomfort or disruptions in current ways of working. Acknowledging these as potential barriers, organizations need to intentionally focus on cultivating trust in the data and the system that supports it, both of which are needed to establish a strong data culture (Capgemini & Informatica, 2026). Data must be seen as accurate, timely, and reliable, requiring a system that is responsive to individual data needs. Systems need to focus on accurate and reliable data collection, that can support reflective analysis and use. Processes needed to be developed to monitor data within the system to identify and provide targeted support when needed.
Building an effective data culture means moving beyond siloed data use (Freske & Swenson, 2023; Gupta, 2024), requiring processes that ensure data used becomes shared. Organizations typically generate vast amounts of data and have data that come into the system through various entry points, with designated owners for pockets of data. When these data are collected and used in isolation, data becomes siloed. An effective data culture requires data to be connected across the organization to support learning and engage in effective decision-making. Individuals or departments should be supported in taking ownership of their data, and in reporting and sharing data. Organizational structures needed to be developed to bridge data use across the system, bringing data together for deeper analysis to generate shared knowledge and support aligned decision-making throughout the system. Shared data can support the identification of organizational strengths, surface systemic barriers, and support the development of processes for collective problem-solving.
Strengthening the Data System through Continuous improvement
Data within an organization should be viewed as a strategic asset that strengthens decision-making and enhances performance (Gupta, 2024). How well this happens is dependent on the data culture. Building an effective data culture is not a one-time event, but a process that requires deliberate planning and implementation, and monitoring for continuous improvement. Organizations must routinely examine how data is used and shared across roles and teams, routinely reinforcing these as valued practices to become part of the organizational culture.
Building a data culture doesn’t just happen; it takes careful planning and ongoing monitoring of practice and outcomes. Improvement cycles need to be developed into the process to evaluate progress and inform ongoing planning. Embedding regular Plan-Do-Study-Act (PDSA) cycles provides organizations with a structured process to plan, learn, reflect, and make intentional adaptations that attend to staff needs, improve decision-making, and strengthen their data culture over time.
Wrapping up
Looking back to what Microsoft Copilot (n.d.) generated, it includes this summary statement:
In short, a data culture exists when data use is embedded in everyday work and supported by leadership, systems, and skills.
In applying effective implementation in building a data culture, it speaks to the connected nature of the work within the system. Organizations need to define what data use looks like and intentionally develop the structures and supports needed to integrate it into all aspects of the work. As leaders begin the process to build a data culture, efforts may not always be perfect. The focus is on implementing, learning, and improving over time to create a supportive environment where data use is experienced as a valued and essential part of ongoing work.
This is the third blog in our data series. Check out the other two blogs in the series:
References:
Capgemini & Informatica. (2026). Trust first: Cultivating a data governance culture for AI-driven enterprise success [White paper]. https://www.capgemini.com/wp-content/uploads/2026/01/Informatica_POV_Trust-first.pdf
Freske, S., & Swenson, M. (2023, October 11). How to reconnect data across the organization for more effective decision-making. Quirk’s Media. https://www.quirks.com/articles/how-to-reconnect-data-across-the-organization-for-more-effective-decision-making
Gupta, P. (2024, December 2). Data as a strategic asset: Culture transformation to break data silos. LinkedIn. https://www.linkedin.com/pulse/data-strategic-asset-culture-transformation-break-silos-pranav-gupta-ivbwe/
Knight, M. (2026, March 20). The 2026 guide to creating and maintaining your data governance policy. DATAVERSITY. https://www.dataversity.net/articles/creating-a-data-governance-policy/
KPMG. (2025). Build a data‑driven culture in 8 steps. https://kpmg.com/us/en/articles/2025/build-data-driven-culture-8-steps.html
Microsoft. (n.d.). Microsoft Copilot [Artificial intelligence software]. https://www.microsoft.com/copilot
Rajappa, S. (2025, October 14). Building a data‑driven culture: Lessons from the CDO’s desk. Forbes Technology Council. https://www.forbes.com/councils/forbestechcouncil/2025/10/14/building-a-data-driven-culture-lessons-from-the-cdos-desk/
Somers, M. (2024, August 14). Data literacy: The key to cracking the data culture code. MIT Sloan School of Management. https://mitsloan.mit.edu/ideas-made-to-matter/data-literacy-key-to-cracking-data-culture-code