The biggest wins with data rarely come from the flashiest AI pilots or the newest tools. They come from places where no one is looking. We have to get better at surfacing what’s hidden in plain sight.

That’s why I created the FIND framework. It’s about helping teams spot the signals that something deeper is going on — in places where data is underused, misaligned, or just plain ignored. FIND helps cut through the noise and focus on real, high-leverage opportunities. Each letter represents a dysfunction that reveals untapped potential.

Here’s what FIND looks like in action:

F — Fragmented decisions

Fragmented decisions happen when different teams make critical decisions based on different data — or worse, data pulled out of thin air.

Examples:

• Sales plans for big demand, while Marketing cuts ads because they looked at different numbers.
• Two regional offices solving the same problem in silos and differently.
• Teams emailing spreadsheets back and forth to reconcile numbers manually.

Why it matters:

Fragmentation doesn’t just slow things down; it creates confusion and wastes precious time by duplicating work. You can’t make confident decisions when everyone’s speaking in their own dialect and defining terms. Fixing this means creating shared data definitions, aligning metrics, and most importantly, designing systems that actually talk to each other.

Remediation:

• Catalog KPI definitions: Publish KPI definitions for enterprise-wide use. Bring teams together to speak the same language.
• Position data teams as a liaison between different departments. 
• Introduce programs like Master Data Management and Data Governance.

I — Invisible patterns

These are insights that are technically in your data, but no one’s catching them — usually because of silos, blind spots, or underpowered tooling. You’re already collecting and possibly even looking at the data, but you’re missing the bigger picture or the deeper relationship it holds.

Examples:

• A certain customer segment is quietly churning, but dashboards don’t flag it.
• A pattern of minor product issues shows up in support tickets, but doesn’t trigger any alerts.
• Procurement teams are overpaying, but it’s buried in unstructured expense data.

Why it matters:

This is the classic case of “we had the data, but didn’t realize what it was telling us.” When you ask better questions or apply the right lens—like clustering, trend detection, or even just a smarter dashboard—you can unlock game-changing insights. 

Remediation:

• Unify data across systems, then explore it using pattern-finding tools like clustering or anomaly detection. Leverage off-the-shelf products if possible.
• We have to train our data teams to not just get proficient at processing data, but also get proficient in analyzing data trends. Make space for regular discovery in sprints.
• Finally, pair analysts with domain experts — insights deepen when data meets real-world context.

N — Neglected signals

Sometimes companies sit on gold mines of data and do not do anything with it. It’s like having a security camera running 24/7 and never reviewing the footage — even after something goes wrong.

Examples:

• Manufacturing teams with machines pumping out sensor data that no one is analyzing.
• Archived customer service calls that could train AI, but just sit in storage.
• Security logs that only get checked after something goes wrong.

Why it matters:

Neglected signals are often your early warning system. They can highlight quality issues, inefficiencies, or even surface ideas for new services. The problem usually isn’t a lack of data — it’s that no one thinks it’s “important.” That mindset shift is critical.

Remediation:

• Don’t let valuable data gather dust. Start with a quick audit to spot overlooked sources, test small pilots to show impact, and assign clear ownership. 
• Even one “aha” moment can shift neglected data from forgotten to essential.

D — Duplicated effort

This one’s painfully common: different people doing the same work, over and over, because systems aren’t connected or trusted.

Examples:

• Analysts rebuilding reports that already exist.
• Teams redefining KPIs because they don’t believe the official numbers.
• Business units separately buying the same third-party data sets.

Why it matters:

This isn’t just inefficient, it’s a symptom of something bigger: lack of trust and poor data governance. When people don’t believe the data, they build their own version. Solving this means creating better documentation, clearer ownership, and easy ways to find and reuse what already exists.

Remediation:

• Fix the trust gap: If no one trusts the source, they’ll rebuild it. Make sources clear, well-documented, and easy to find.
• Promote Reuse: Give teams a reason to rely on centralized data by showing how it saves time, boosts accuracy, and supports better decisions. Make it the obvious choice — not the enforced one. If it feels premium, teams will want to tap into it.

How to use FIND with your team

Want to put this into practice? Here’s a simple way to start:

1. Run a FIND session: Gather stakeholders from across the organization — business, data, tech — and walk through each letter.
2. Capture real examples: Let people share where they’re seeing friction or missed opportunities.
3. Prioritize what you find: Score each example based on business impact and effort to fix.
4. Tie everything back to outcomes: Make sure every idea connects to something measurable — cost savings, speed, experience, growth.

The point isn’t just to find problems — it’s to align around what’s worth solving, and why. That shared language is what turns scattered efforts into a true strategy.

With FIND, you can stop chasing the next shiny tool — and start unlocking the value that’s already there.

About the Author:
Apurva Wadodkar is a seasoned leader in Data Science, with over 20 years of experience turning data into powerful business tools. Her expertise lies in pairing well-governed, high-quality data with cutting-edge AI/ML products to create solutions that are as reliable as they are innovative. Whether it’s driving transformative growth, streamlining operations, or unlocking hidden efficiencies, Apurva knows how to make data work smarter and harder for businesses.

Wadodkar is a dedicated mentor with a mission to shape the next generation of tech leaders. As an advisory board member for the Michigan Council of Women in Technology, Wadodkar combines her passion for diversity with her commitment to nurturing talent and creating opportunities that inspire and empower future innovators in technology.