How to Build a Data Product That Doesn’t Collect Dust

It happens more often than anyone wants to admit. A company invests months building a dashboard, prediction model, or reporting suite – only to find that nobody uses it. Or worse, people check it once and never come back.

The problem isn’t the data; it’s the gap between what was built and what the team needed. This article covers how to close that gap and create a data product people rely on – not one that dies quietly in a forgotten tab.

Before we delve into the details, let’s quickly define data products to ensure we’re all on the same page.

Data Product Explained

Are data tools, data apps, and data products all the same? No.

Data App

A data app is an application that allows users to interact with data through a user-friendly interface.

Data Tool

The tool usually refers to an online software platform that can be more technical than a user-friendly app.

Data Product

A data product is designed to solve specific user problems. It is self-contained and combines data, metadata, semantics, and templates to aid business use cases.

Building an In-Demand Data Product

Building an in-demand data product that users actively engage with involves a structured, user-centric approach that takes six steps.

Step 1: Start With the Problem, Not the Data

The biggest mistake starts with “What can we analyze?” instead of “What decisions are we struggling with?”

Before building anything, ask:

• Who is the end user?
• What decisions do they need to make regularly?
• What would a good vs. bad outcome look like?
• What happens if this dashboard/model didn’t exist?

If those questions are fuzzy, hit pause. Strong data science companies don’t rush into tools. They clarify decisions first.

Step 2: MVP the Data Product Like a Real Product

Follow these steps to treat your data solution like a Minimum Viable Product (MVP)— not a final release.

• Build a prototype or wireframe first
• Demo the logic with mock data
• Run a fake report before building the whole pipeline

Following this process, you get an early validation loop that catches usability issues, field mismatches, and unclear definitions before you lock in logic.

Example:

One retail chain built a “store performance” dashboard. Before launch, they showed store managers mockups – and discovered nobody understood the score weighting. That saved weeks of rework.

Step 3: Visualization Is Not Decoration

A good chart doesn’t just look nice. It tells you what action to take. As we are mostly visual-first out of all our senses, data visualization services are about providing clarity, and to get it here is what you need to do:

• Use sparklines or icons to flag outliers, not just bar charts
• Show change vs. expectation, not just values
• Tailor views by role – what a CFO wants is different from what an ops lead needs

It pays to remember that dashboards need a narrative. No more filters.

Step 4: Plan for Adoption Like a Product Manager

People don’t magically “start using” a data tool. They need hand-holding and encouragement. Additionally, they need to form the habit of using it, so you need to have the following:

• Internal onboarding: short videos or walkthroughs on how to use it
• Feedback loops: ask users what works and what’s missing
• Advocates: assign a few champions who promote and gather feedback internally

The best data tools behave like software products. They evolve. They respond to user needs. They stay visible.

Step 5: Design for Decay (Because It Will Happen)

No data product stays relevant forever in much the same way no technology does either.

With data products, they need to be relevant while teams change, KPIs evolve, and source systems shift. In other words,  what worked for you in Q1 might be irrelevant in Q3 or Q4, let alone the following year.

Therefore, for longevity, build your data product with the following:

• Modular pipeline design for easier changes
• Scheduled reviews of definitions and business logic
• Use analytics to see which data visualization charts or reports are ignored

Real-world data platforms like those developed by S-PRO include built-in alerting and version control for logic changes. It’s not just about delivery – it’s about sustainability.

Step 6: Don’t Wing the Technical Implementation

Technical implementation is where most teams overestimate their tooling or underestimate the complexity. To prevent this happening, you need:

• Data engineers to design extract/load pipelines (5–15 hours per data source)
• Backend support for query optimization, logging, and transformation logic
• Cloud infra setup (e.g., Redshift, BigQuery, Grafan, or Metabase for dashboards)
• QA support for metric validation, especially for financial or regulatory data

A “small” dashboard project can require 80–120 hours of backend work, not counting frontend UX. Planning for this means fewer rewrites later.

A Data Product People Use Looks Like This

How do you know if your data product is working? Check you have the following:

• Teams check it before meetings – not after
• KPIs in the product are used in actual performance reviews
• Users ask for new slices or features – not explanations
• People reference it by name: “Let’s check the growth tracker”

If nobody’s asking for more, or if they export everything to Excel… you have work to do.

Final Thought

A data product is only helpful if someone uses it to decide.

That means less obsession with the perfect model – and more care for the real-world context in which it lives.

Build for clarity. Design for humans. And never assume that data alone will drive action.

The best teams? They know successful data products are equal parts analytics, UX, and change management. And if you’re not sure where to start, service providers like S-PRO work with organizations to design usable, impactful analytics tools.