Some teams build their own data tools in isolation, focusing only on their immediate needs without considering the bigger picture. That leaves a maze of siloed solutions, limiting visibility and creating a culture where data is treated as personal property rather than an organizational asset.
“The culture that we see today that we’re trying to break is the culture around ‘It’s mine — I did it,’” said Mike Henry.
Henry, managing director at Domino Collective, was speaking to a room full of data enthusiasts at YYC DataCon, and from the nodding heads in the crowd, it was a familiar problem.
You roll out a new data model, only to find that hardly anyone is using it. Another department hires an external vendor to solve a problem you already tackled. Data teams spend months implementing new tools, yet workflows remain unchanged.
Data culture is about how organizations manage, distribute, and leverage data. And according to Henry, the biggest obstacles aren’t the systems themselves — it’s the people.
So how do you fix that? Here’s what he suggests.
Stop thinking of data as a tech problem
It’s tempting to believe that if you build the right system, people will use it. But technology alone doesn’t drive adoption.
“We focus on technical complexity, both on the business side and on the cloud side today,” said Henry. “But what we miss is who else is part of the system.”
This means shifting focus from just solving technical problems to understanding who is affected by them. Engineers might be thinking about databases and architecture, but business users care about usability and insights. When these perspectives aren’t aligned, data remains siloed.
A better approach? Treat stakeholders as part of the system, not just end users. Engage them early and often in the decision-making process.
Identify and break down silos
Data culture problems often start with how tools and processes evolved.
“People built tools that unlock data for themselves in their silo with blinders on,” Henry said.
This leaves you with data that’s difficult to integrate, inconsistent definitions, and resistance to change.
One way to fix this is by making data more transparent across teams. Henry suggests an “interactive planning” approach:
- Get stakeholders in a room to map out their processes and pain points.
- Make each team’s data priorities visible to others.
- Identify dependencies — who relies on whose data — and optimize accordingly.
- Develop a roadmap that delivers value incrementally instead of massive, high-risk overhauls.
The key is to show teams why collaboration benefits them, not just tell them to change.
“The person literally standing right beside you has no idea the problem you’re solving,” Henry said. “And more importantly, they don’t know how you solved it.”
Without visibility, teams miss opportunities to leverage existing work or collaborate on solutions.
Market your wins internally
A tried and true strategy to get people on board is to make success visible.
But it doesn’t just mean getting things done behind closed doors and presenting a polished result.
Henry suggests starting before the project even begins:
- Talk to potential users about what they could do with the new data system.
- Keep them updated throughout development, so they’re invested in the process.
- Share results widely — don’t assume people will just notice the impact.
This isn’t about bragging, but making sure teams understand how new approaches can make their lives easier.
Start small, then expand
Trying to change an entire organization at once is overwhelming. Henry recommends starting with one department and proving the value before expanding.
“If you haven’t done this before, my argument is start small and then expand it with interest.”
By showing incremental improvements, other teams will naturally become curious. And once they see value, they’ll be more likely to participate in larger organizational shifts.
Changing data culture isn’t about forcing new processes, it’s about making it easier for people to work smarter.
Overcoming resistance to change
Even with the best tools and strategies, people are often the biggest hurdle in shifting data culture. Resistance can come from different sources too; some employees are set in their ways, while others simply don’t know what’s possible with modern data practices.
“People that have been around for a while have inherent tribal knowledge,” Henry said. “They have a sense of ownership over what they’ve built because it’s valuable to the organization. That value is perceived through them.”
Rather than dismissing long-time employees as blockers, Henry suggests treating them like the experts they are. Recognizing their knowledge and showing how data initiatives support their work can turn resistance into advocacy.
Bridging this gap requires curiosity and collaboration. Encouraging experienced employees to share insights while helping newer team members understand the existing landscape builds mutual respect and ultimately, a stronger data culture.
Sustaining a strong data culture requires buy-in from both leadership and frontline teams, but Henry said getting the people on the ground involved early.
“Sometimes it’s easier to start at the top, but where all the work happens is lower,” Henry said. “I prefer to manage up, get consensus from the people actually doing the work and show leadership the value they’ve identified.”
By shifting focus from just technology to people, breaking down silos, and making wins visible, Henry suggests your organization can move toward a data-driven culture that actually sticks.
Digital Journal is the official media partner of YYC DataCon 2025.
This article was created with the assistance of AI. Learn more about our AI ethics policy here.
