Build Data Like a Product: Why Most Dashboards Fail (and How to Fix It)

Over 85% of data and GenAI projects fail (source: McKinsey, 2025).

Not because of bad code. Not because Snowflake is slow. But because most teams forget one thing:

They don’t treat their data work like a product.

Instead, they get stuck:

• Building new ETLs.

• Deploying another platform.

• Tracking every metric under the sun.

No strategy. No clear user. No value.

Before your team builds another dashboard, pause. Here’s the playbook we use to turn data into product—the kind that actually gets used, trusted, and drives ROI.

Why Most Dashboards Are Useless

Most dashboards are:

• Unused

• Outdated

• Conflicting

• Misaligned with business outcomes

Because nobody asked: Who is this for? What decision will it drive?

The product mindset flips everything.

Instead of asking: “What data do we have?”

You ask: “What business outcome are we trying to achieve?”

The 5-Part Framework to Build a Real Data Product

Before writing one line of SQL, run through this checklist:

1. Identify decision-makers

Who will actually use this?

• Sales leaders?

• CS reps?

• Finance?

2. Map decision points

What exact choice does this dashboard enable?

• Increase spend?

• Pause budget?

• Trigger a retention play?

3. Define success metrics

How will you know it worked?

• Lift in conversion?

• Time saved?

• Churn down?

4. Set quality thresholds

What’s the minimum level of accuracy, freshness, completeness?

• Real-time?

• Weekly?

• 95% coverage?

5. Establish ownership

Who owns the logic, the data, the UX, and the outcome?

If it breaks.who fixes it?

A Real Example: Churn-Risk Early Warning System

Product goal: Reduce subscriber churn with automated early alerts

Primary users:

• Marketing → Retention marketers running save offer playbooks

• Customer Success → Account managers doing proactive outreach

• Finance → Rolling churn into revenue forecasts

Data signals (scored):

1. Engagement-gap index (60%): Drop in watch-hours for >5 days vs. 90-day baseline

2. Payment flags (25%): Card declines, retry failures

3. Dormancy (15%): No logins on any device for 7+ days

Success criteria:

• ↓ 15% relative churn in 6 months

• 30% lift in win-back conversions

• 95% of alerts contacted within 24h

Now the data team has:

• Clear purpose

• Measurable results

• Specific stakeholders

• Prioritized metrics

TL;DR Build Data Like Product

Whether it’s dashboards, AI copilots, or data pipelines:

1. Start with business problems

   • Talk to execs

   • Ask what decisions they’re stuck on

2. Think like a product manager

   • Who’s the user?

   • What outcome?

   • What’s the MVP?

3. Score complexity vs. value

   • Use a prioritization matrix

   • Kill vanity projects

4. Work in feedback loops

   • Ship fast

   • Collect feedback

   • Iterate

5. Build in modular chunks

   • Think Lego blocks

   • Start small, prove value, scale

Final Thought

More tools, more dashboards, more data… none of it matters without clarity, ownership, and outcome.

Build your data like it’s a product.

Because it is.

Need help transforming your data into a real product in production?

Book a 30‑minute Data / AI Audit. 

We’ll map your stack, infrastructure, evaluate the tools and build a mini‑matrix live and have low-hanging data & AI initiatives aligning core goals before you waist money on another useless data platform thinking will make you data driven.

You need a strategy and a roadmap-

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