You da real MVP for getting your data platform live, but to your surprise all that effort was just the pre-season. Now it's time for the actual season to begin. Take a moment to put your head up and look around: your business has just launched a new product, marketing has pivoted its strategy, you have a meeting set on your calendar that just says "Phase 2."
Here's the upside (and challenge) of success: good platforms get used. Used platforms generate feedback. And feedback? That means your co-workers are invested enough to want more. It's working! Sure, watching those requests and new use cases pile up might feel overwhelming, but this is exactly what the launch of a good data project looks like.
In this final post of our myth-busting series, we'll show you how to keep your data platform in sync with shifting business goals, expanding data sources, and growing user expectations. By applying product management principles and encouraging real business ownership, you can evolve your MVP into a powerful, ever-adapting engine that breathes and grows along with your organization. The truth is, your data platform isn't a project with an end date – it's a living product that's just beginning its journey.
Success requires a different mindset. Instead of treating your data platform as a one-off project, you need someone who thinks like a business owner – a product manager who aligns technology with real business value. Think product manager meets data platform CEO: someone who can whiteboard data lineage with engineers in the morning, then show finance how a new capability can shave days off their month-end close.
As emphasized in Myth #1, genuine business value emerges when users actively apply the platform to solve critical problems. But how do we validate this adoption? Start by mapping user workflows – not just tracking logins. For instance, usage logs at an insurance client revealed actuaries logged in daily… only to manually export data due to slow processing. At another organization, power users wasted weeks building redundant tools that already existed in the platform.
This is why product managers must:
Lemonade provides an excellent example of engaging its entire workforce with data. Every month, the company distributes usage reports highlighting which teams or individuals are most active with its data tools. This practice reinforces their commitment to data-driven decision-making.
By understanding and engaging with your users, you can transform your data platform from a project into a vital product that drives real business value. And there's another benefit to this: the more you understand your users, the more strategic your backlog becomes.
Effective backlog management is all about turning chaos into strategy. But the good news is that you're a data team. You already have the tools to spot patterns and identify root causes – you just need to organize all that information. Start by adding metadata to your backlog: tags like “Finance report,” “Performance issue,” or “Urgent request” give your team the structure needed to filter, sort, and quickly spot trends.
Bring your team together to hunt for common triggers: Where could one root-cause fix eliminate five “urgent” asks? Take a cue from Coca-Cola’s BI team, who color-coded report usage and discovered 40 percent of their “critical” finance reports hadn’t been opened in 90 days – data that gave them the confidence to question future asks and better prioritize.
This systematic approach slashes reactive fire drills, uncovers root causes faster, and frees up bandwidth for high-impact improvements, like launching new capabilities that genuinely move the needle for the business.
Extra Tip: Consider occasionally rotating the analysts and engineers onto different teams. The best backlog ideas come from those who really know the context and the art of the possible.
Your platform can’t thrive in isolation. If you can regularly check in with executives and end-users to anticipate business needs (everything from upcoming product launches to next quarter’s KPIs), you can guide your team with foresight instead of just being reactive. Keeping your team aware of these bigger-picture moves ensures the platform can evolve alongside the organization.
As mentioned before, the occasional lunch-and-learns or workshops also go a long way, showing off new capabilities and reinforcing how the platform supports real-world tasks. Remember, your MVP launch was just the starting line; staying closely aligned with ever-shifting business goals will keep your data platform bringing the ROl.
...staying closely aligned with ever-shifting business goals will keep your data platform bringing the ROl.
For more info on how to democratize data and empower their employees, check out this post how AirBnB did it with Data University.
Remember all that trust you worked so hard to establish? Without proper governance, it can erode bit by bit across different teams until suddenly it collapses altogether, dragging you right back into the chaos you fought so hard to escape.
This is a key area many organizations stumble, post implementation. They invest heavily in building beautiful data platforms and dashboards but then neglect the ongoing practice of governance that preserves their value. What they don't realize is that overlooking these efforts is precisely what allows their past demons to return.
Think of data governance not as another department or software purchase, but as an organizational mindset. At its best, governance is simply good habits embedded into how your teams already work.
Remember all that trust you worked so hard to establish? Without proper governance, it can erode bit by bit across different teams until suddenly it collapses altogether, dragging you right back into the chaos you fought so hard to escape.
This is a key area many organizations stumble, post implementation. They invest heavily in building beautiful data platforms and dashboards but then neglect the ongoing practice of governance that preserves their value. What they don't realize is that overlooking these efforts is precisely what allows their past demons to return.
They invest heavily in building beautiful data platforms and dashboards but then neglect the ongoing practice of governance that preserves their value. What they don't realize is that overlooking these efforts is precisely what allows their past demons to return.
Think of data governance not as another department or software purchase, but as an organizational mindset. At its best, governance is simply good habits embedded into how your teams already work.
Instead of attempting to govern everything at once, launch a pilot project focusing on a specific pain point, like a nagging data quality or compliance issue that’s already causing headaches. By demonstrating value quickly, you spark wider interest without overwhelming people or processes. Here are a few things to keep in mind while starting small:
Identify a current issue already causing headaches, perhaps inconsistent customer data across systems, or reporting discrepancies affecting decision-making. By solving a real problem, you demonstrate value immediately without disrupting existing workflows.
Clearly articulate both organizational benefits (reduced errors, faster reporting cycles) and personal advantages (less time spent reconciling numbers, fewer challenging questions from leadership). When people see governance solving their actual problems, adoption follows naturally.
The closer governance capabilities are to people's daily work, the more likely they'll be used. Look for opportunities to embed quality checks, documentation, and accountability directly into the systems people already use rather than creating separate processes. Technology should support human collaboration rather than replace it.
A colleague once shared a story that perfectly captures this challenge. Their former company had invested seven months meticulously building what looked like a textbook data governance program – complete with quality controls, documented processes, and a comprehensive framework. Leadership had cleared the runway for this initiative, even protecting it from competing priorities.
Then came the quarterly executive review.
"So what exactly has this improved for us?" the CFO asked, leaning forward. "Are we making decisions faster? Have we reduced regulatory risks? Is anyone actually using these standards?"
The painful silence that followed told the whole story. Despite technical excellence, the IT team had built the entire initiative with minimal business input. There was no connection to pressing challenges the organization actually faced, no measurable outcomes anyone cared about, and consequently, no meaningful adoption across departments. The initiative was quietly put on hold the following week.
This scenario underscores a fundamental truth we see repeatedly: Data governance cannot be driven solely by IT.
This scenario underscores a fundamental truth we see repeatedly: Data governance cannot be driven solely by IT. While technical teams excel at providing "The How," business units must own "The Why" of governance to ensure it solves real problems rather than creating elegant solutions nobody needs.
As your initial governance efforts begin showing results, perhaps faster report production or fewer data quality issues, and a good pattern has been established, it becomes easier to distribute governance responsibilities throughout the organization. Here's how to make that transition:
Rather than creating new governance positions, identify who already has the knowledge and stake in particular data domains. The marketing operations manager might own customer segmentation definitions, while the finance director would be responsible for revenue recognition standards.
Create lightweight frameworks that guide how decisions are made about data without building bureaucracy. Finding a simple template for documenting key data elements or straightforward approval workflows can provide some guardrails without becoming overcomplicated.
When governance prevents a major reporting error or enables a faster strategic pivot, make sure those stories spread throughout the organization. Nothing builds buy-in faster than concrete examples of value.
The organizations that sustain long-term value from their data investments are those that successfully transition from project-based thinking to ongoing practice. They weave governance into the fabric of how work gets done rather than treating it as a separate initiative.
By starting small, leveraging existing roles, and focusing relentlessly on business value, your governance program remains relevant long after any single project concludes. The business – not just IT – becomes the champion for quality data, transforming it from a technical asset into sustained competitive advantage.
Remember: In the end, successful data governance isn't about controlling data – it's about empowering people to use it confidently to drive better outcomes.
After unpacking four pervasive myths about data projects throughout this series, one truth stands crystal clear: Data transformation is fundamentally a human endeavor.
Whether we're debunking the notion that technology alone drives success, exposing why "if you build it, they will come" falls flat, explaining why data excellence can't be a side hustle, or revealing why governance matters, the common thread is always people.
The most successful organizations we've worked with don't necessarily have the biggest budgets or fanciest tech stacks. What they do have is seamless collaboration between business and technical teams, executives who champion data initiatives through actions (not just words), and a culture where everyone understands their role in the data ecosystem.
Start by investing in the human connections. Build cross-functional teams that speak both business and technical languages. Create feedback loops that keep improvements flowing. And remember that governance works best when it solves real problems people already feel.
The tools will inevitably change. What endures is the organizational muscle you build for turning data into decisions that matter.
Thanks for joining this journey. The conversation continues – reach out anytime to share your own data transformation stories.
