This site uses cookies to enhance your browsing experience and deliver personalized content. By continuing to use this site, you consent to our use of cookies.
COOKIE POLICY
Despite years of investment in data platforms and analytics tools, many organizations still face a familiar challenge: their data strategy looks great on paper, but never delivers the value that was expected. Dashboards sit untouched, and self-service portals fail to gain traction. The data team checked every technical box, yet business users continue defaulting to outdated or siloed methods.
Why? Because the strategy wasn’t built with the users’ real needs in mind.
Think of it like buying a car without ever seeing it, let alone taking it for a test drive. You’re promised it’ll meet your needs, but once it arrives, it misses the mark. Maybe it lacks certain features. Maybe it just doesn’t feel right. Or maybe—because there are so many boxes it doesn’t check—you simply don’t want to drive it at all. Still, there it sits in your driveway.
That’s the reality for many data and analytics leaders. The dashboards, self-service portals, and AI-powered platforms that their teams built look impressive in a strategy deck. But when it’s time to use them, adoption stalls. That’s because people don’t embrace what they don’t understand, trust, or see as meeting their actual needs.
Here’s the truth: the problem isn’t the technology—it’s the process. And the solution is simple: let people test drive the product before building it.
That’s the power of blueprinting, mock outputs, and early validation. By integrating these elements into your strategy process, you ensure alignment with real user needs from the start. The result is a business-first, hands-on approach that dramatically increases adoption, accelerates ROI, and minimizes costly misalignment.
In this article, we will cover:
It’s no secret that many data initiatives fail because they struggle to deliver ROI. According to Gartner, more than 80% of data strategies fail to scale across the enterprise. That’s because the solution is not about building something technically sound. Instead, it’s about building something people will use.
The reasons for low adoption are familiar:
We’ve all been there. The result, despite the significant effort and investment, missed the expectation and ultimately doesn’t deliver business value and impact.
Just like a great car design starts with understanding the driver’s needs and desires rather than the engine, data strategies need to begin with how people actually work, not just the technology behind them.
That’s where blueprinting comes in. It flips the traditional approach by starting with the business and putting users in the driver’s seat from day one. Through structured, collaborative sessions, stakeholders define key decisions, must-have KPIs, and the real workflows that the data needs to support.
The blueprinting process reveals hidden assumptions, aligns business and IT teams, and results in a clear visual model that everyone understands because they helped design it. Instead of reacting to a finished product, stakeholders are shaping a solution tailored to how they actually work. It’s the difference between customizing a car’s features to your needs versus buying a model from the lot sight unseen (and hoping it works).
This is where the metaphor becomes reality. Mock outputs are the equivalent of a test drive: they let stakeholders get a feel for what’s being built before it’s delivered.
Rather than waiting for a fully developed BI solution, mock outputs provide mid- and high-fidelity prototypes of dashboards, reports, or self-service interfaces. These mockups show layout, flow, and functionality, using tools like Figma to simulate interaction.
This process provides direct benefits to stakeholders from the beginning, enabling teams to:
Just like test driving a car reveals preferences about handling and ergonomics, mock outputs uncover expectations and priorities that rarely come up in requirements meetings.
Just as crucially, they allow fast iteration before any costly technical development begins.
Think of it as a “try before you build” approach versus the classic “build and hope” approach.
Self-service analytics only deliver value if people use it, and people only use what is familiar, relevant, and designed for their needs.
Mock outputs and blueprinting create buy-in from the start. Instead of dropping a new tool into people’s laps and hoping they adjust, you’re involving them in shaping the experience.
This builds trust, increases data literacy, and removes fear of the unknown. And – just like deciding what features to customize on a car during a test drive – it allows teams to define exactly what they need before they invest time, energy, and budget into building it. This results in platforms that reflect how the business actually operates, rather than how the data team thinks it should.
On a broader level, this process also facilitates building a culture of data use. Users start to visualize how data helps them do their jobs better. They provide sharper feedback, ask smarter questions, and become more engaged in the success of the initiative.
Ultimately, when adoption is built into the design phase, rollout becomes less about training and more about momentum.
A large organization we work with faced a familiar challenge. After multiple failed attempts to modernize a decades-old ERP platform, the initiative was stuck. Meetings weren’t producing alignment, goals were unclear, and early results lacked clarity and adoption. At the end of the day, stakeholders were frustrated, skeptical, and lacked trust in the process.
That’s when we took a different approach: we started with the customer rather than the code.
Service Blueprinting helped the business visualize how customer needs, internal processes, and supporting systems intersected. We built mockups and used them to gather feedback from stakeholders at all levels of the organization, from executives to sales associates. And the results were significant:
By taking a tested, validated plan and aligning it across the business, progress was tangible. One business leader even said: “In a matter of weeks, they achieved what we’ve been working toward for the past five years.”
Read the full client impact story here.
The success of this approach lies in how each step builds on the last to drive both business alignment and technical readiness. The successful Business-Driven Data Framework is built around key steps designed to align, validate, and scale:
Each step is like tuning the engine, customizing the interior, and selecting the features you need so the end product actually drives value from the moment you “take it off the lot.”
This test-driven approach to data strategy delivers measurable business results:
Ultimately, the results speak for themselves when users are part of the process, when mockups make it real, and when input and feedback shape the outcome.
The old way of building data products is broken, and we all know it. You wouldn’t buy a car without driving it first, so why would the approach to data transformation be any different?
Blueprinting and mock outputs let your business take a test drive and put the right stakeholders in the driver’s seat. These strategic tools bring clarity, eliminate waste, and ensure the final product fits your needs, well before anyone hits “deploy.” It ensures your investments lead to real usage, real insight, and real business impact.
If your team is struggling with stalled initiatives, unused dashboards, or self-service that never took off, it’s time for a smarter approach.
Take your next data investment for a test drive first. Contact us to discuss how blueprinting can strategically lead you down roads you haven’t been able to explore before.
Reid, SVP of Data and Analytics at UDig, is a long-time data professional with experience at multiple Fortune 500 companies. Most recently, he was the Chief Data and Analytics Officer at Markel. Prior to that he held multiple roles at Capital One including VP of Data Engineering.
