Data: The Most Misunderstood Phase in Digital Innovation

Once a meaningful human problem has been identified, most digital teams rush headlong into the data—only to get lost in the weeds. According to digital product strategist and author Agathe Daae-Qvale, this is one of the most common pitfalls in innovation.

A team engages in a focused company meeting, discussing the strategic uses of data.

“Just because data exists doesn’t mean it’s useful,” she writes in Digitized Product Management. “The right data must be located, validated, and translated into a language the entire team can understand and act upon.”

In Step 2 of her 9-Step Use Case Evaluation framework, Daae-Qvale outlines how to critically assess the relevance and usability of data before building out a solution. It’s not about big dashboards or impressive charts. It’s about asking the right questions:

·       Does this data actually speak to the problem we’re trying to solve?

·       Is it recent, reliable, and human-readable?

·       Do our stakeholders trust it?

When Data Misses the Mark: A Real-World Reminder

Consider the early rivalry between Slack and Microsoft Teams. Slack led the market in user engagement data—it knew how often users logged in, how long they stayed, and what features they used most. Their team leaned heavily into optimizing for those metrics.

Microsoft Teams, on the other hand, focused on a different dataset entirely: what large enterprise customers needed to streamline communication within existing Microsoft ecosystems. Their data was less about feature usage and more about integration, admin control, and cost-saving outcomes.

Slack had more usage data. Teams had more business-relevant data. And that difference in strategic alignment helped Teams gain massive traction in the corporate world—despite initially lagging in engagement features.

“Relevance must come before availability,” Daae-Qvale explains. “When we treat data as the second step—not the starting point—we unlock solutions that are both intelligent and truly useful.”

Avoid the Trap of ‘Data Vanity’

She also warns against the temptation of “data vanity”—falling in love with metrics that look impressive but have no real impact. This includes page views, sign-up spikes, or AI predictions that don’t tie back to a core use case.

As more teams experiment with AI and advanced analytics, there's a growing tendency to build solutions around whatever tools or models happen to be available. That’s why Step 2—focusing on relevant, purposeful data—is more essential than ever.

Want to Build Smarter Data-Driven Solutions?

Agathe Daae-Qvale’s 9-Step Use Case Evaluation framework isn’t just for digital product teams—it’s for any organization using data to solve complex problems. Whether you're working in healthcare, manufacturing, logistics, energy, finance, or public services, the steps remain the same:

·       Start with the human problem

·       Match it with the right data

·       Validate before you scale

Discover the full framework in Digitized Product Management—and learn how to turn raw information into results that matter.