Data Products: Packaging Insight, Not Raw Data

Offering raw data is like handing someone a pile of ingredients when they asked for a meal. It is inefficient, risky, and usually ends in frustration. In addition, very few clients will have the capacity or knowledge to build their own products based on your data. If you are serious about monetising data, you need to think in products, not pipelines.

This article is an excerpt from Alternata's Data Monetisation Playbook.

 📘 DEFINITION

Data Product: A reusable output derived from raw data that delivers value — e.g., dashboards, APIs, risk scores, or behavioural segments. Data products are monetisable, scalable, and governed.

Build, Don’t Dump

The biggest mistake in data monetisation? Dumping CSVs or tables into a cloud folder and calling it a product. Data buyers – internal or external – are looking for outcomes, not data engineering projects.

Great data products are:

• Dashboards tailored to industry-specific KPIs.
• APIs serving real-time credit risk scores or customer segments.
• Benchmarks showing market performance.
• Indices, lookalike models, or purchase propensities

They’re packaged, accessible, understandable, and valuable. The more you do upfront, the more scalable and monetisable your solution becomes.

Design for Use Cases

Start by asking: "What question is my customer trying to answer?" or "What decision are they trying to make faster, better, or with more confidence?"

Then reverse-engineer the data product. Whether it's a churn prediction score, optimal store location tool, or campaign targeting segment - your product should remove friction from a real-world decision.

Do not build what’s easy to extract. Build what is hard to ignore.

🧭 PRO TIP

Bundle don’t broadcast. Instead of selling raw tables, bundle insights into segment packs, industry benchmarks, or decision-ready models.

Think Like a SaaS Company

Data products aren’t fire-and-forget. They’re living assets that need:

• Version control: Track updates and changes over time
• User onboarding: Help users understand what is in the product and how to use it.
• Feedback loops: Continuously gather input to improve relevance and usability.
• Support channels: Treat data consumers like real customers.

In short – operate your data business like a SaaS startup: iterative, customer-focused, and product-led.

Use this framework to assess the priority of data monetisation initiatives in your organisation. Answer each question and score the results.

Revenue Potential: How much commercial value could this use case unlock?

Data Accessibility: Is the required data available and clean?

Privacy Risk: What legal or ethical implications need to be addressed?

Partner Demand: Is there strong interest from one or more buyers/partners?

Speed to Market: Can this be piloted and monetised within 90–180 days?

Want to explore how your company could generate revenue from data in 90 days? Schedule a discovery call with Alternata.