What does it actually take to build a data culture that lasts?
At our most recent Data Culture Summit, data leaders from across industries came together to discuss exactly this question – and the answers were more nuanced than many expected. The conversations touched on organizational design, leadership commitment, enablement approaches, long-term change management, and the infrastructure that makes skills actually stick.
One message was consistent throughout: data culture is not a training problem. It is a systemic change initiative. Organizations that treat it as a course catalog miss the leadership commitment, organizational design, and long-term patience it truly requires.
Of course, there is no single definitive list of success factors – every organization’s journey is different, and many more dimensions come into play. But from the roundtable discussions at the Summit, seven factors emerged that dominated the conversation time and again: the themes that practitioners are wrestling with most, and where they are finding the most meaningful progress.
1. Build awareness across all organizational levels
Awareness must reach two audiences simultaneously: employees who need to build skills, and top management who need to actively support the initiative.
Without employee awareness, you get low participation. Without management awareness, you get no resources.
Successful tactics include:
- AI / Data Days or open houses where all employees can explore data topics hands-on
- Executive briefings that connect data culture to business impact
- Regular communication campaigns that keep data literacy visible
One insight came up repeatedly: “Communication is always more.” You can never communicate enough about your data culture initiative. Visibility drives engagement, and engagement drives momentum.
2. Secure top management support early
Top management support is not optional. It is the single biggest differentiator between programs that scale and those that stall.
Three reasons why:
- Budget and resources: Convincing leadership requires success measures and lighthouse use cases that demonstrate ROI
- Time allocation: Employees need explicit permission to invest time in learning, especially in hierarchical cultures where priorities flow from the top
- Strategic alignment: Data culture efforts must connect to company goals and strategic roadmaps to survive beyond the first budget cycle
Without top-level support, your program competes with every other priority. With it, data culture becomes part of how the organization achieves its goals.
3. Choose quality over quantity in learning design
Here’s where many programs go wrong. They create dozens of courses, workshops, and resources. The result? Employees feel overwhelmed and disengage.
The panel’s conclusion was unanimous: class over mass.
Better to have fewer, high-quality offerings that genuinely engage people than an overwhelming catalog that no one completes. Several tactical approaches emerged for making those offerings count:
Create scarcity: Limit availability of premium formats. When spots are limited, people value them more.
Use gamification: Make learning enjoyable, not just educational.
Foster peer learning: Buddy systems, lunch-and-learn sessions, and peer exchanges create social motivation that no e-learning module can replicate.
Stay close to real needs: Design offerings around what people actually need to do their jobs, not generic data concepts.
The key is getting people into practice, not just theory. Communities of practice, hybrid training with hands-on sessions, and live work on real projects all outperformed traditional classroom formats.
4. Build a scalable operating model with cross-functional partnerships
The consensus model for scaling data culture: hub and spoke.
You need a centralized team to maintain quality, consistency, and strategic direction. But you also need multipliers, ambassadors, or champions who spread the message across the organization. One team alone can’t reach an entire company. A network can.
Critical detail: define clear roles and responsibilities. Don’t just assemble people and hope for the best.
Equally important is the partnership with HR. In most organizations, employee development and skills sit within HR’s domain. If your data culture initiative is not aligned with HR, you are working against organizational structure rather than with it.
Successful programs build strong HR partnerships from the start. This alignment helps with:
- Integration into performance management and goal setting
- Access to learning infrastructure and platforms
- Connection to broader talent development strategies
5. Invest in patience and realistic expectation management
Data culture follows an upward slope, not a hockey stick. Early stages require patience as you build awareness and momentum. Exponential effects come later, but only if you persist through the gradual beginning.
Two critical applications of patience:
- Patience with the program: Don’t expect overnight transformation. Set realistic expectations with all stakeholders about what is achievable in each timeframe. Communicate common goals clearly from the start.
- Patience with people: Meet employees where they are in their data journey. Make them feel welcome at any starting point. Take away fears rather than creating pressure.
As one panelist noted from an HR perspective: approach people with empathy, not as experts telling them they are behind.
6. Align enablement with data organization maturity
Here’s a critical insight the panel surfaced: enablement without infrastructure fails.
You can teach people data skills, but if they don’t have:
- Tools they can actually use (e.g. data catalogs, analytics platforms)
- Data quality good enough to trust
- Accessible data sources
…then training creates frustration, not capability.
Successful programs coordinate literacy initiatives with data organization maturity. As people gain skills, they need an environment where they can apply them.
One often-overlooked dimension: help employees understand their role as data producers. Data quality is not just a technical team’s job. When people see themselves as part of the data ecosystem, the culture shift accelerates.
7. Prepare for the challenges that come with success
Even with these success factors in place, expect friction:
- Resource constraints: Every panelist reported never having enough resources. Success paradoxically makes this worse. The better your program, the more demand you create.
- Workers’ council collaboration: In larger companies, this requires trust-building, patience, and education. Don’t underestimate this stakeholder group.
- Starting without foundational elements: Some organizations need to build data strategy or governance in parallel with culture programs. This “chicken and egg” problem requires careful sequencing and a willingness to move forward with imperfect conditions.
What’s the one thing to remember?
Building a data culture is not an overnight success. It is an upward slope that requires patience, leadership commitment, and systemic change across the entire organization – not just in learning and development.
The organizations that get this right are not those with the best course catalog. They are the ones that align leadership, infrastructure, organizational design, and people – and then stay the course long enough to see the results.
The question is not whether to invest in data culture. It is whether you are willing to treat it as the strategic initiative it actually is.
Looking for peer insights on data culture initiatives? Join data leaders at the next BARC Data Culture Summit for roundtable discussions, workshops and practical frameworks. Explore BARC events.
