Data Set vs Data Product: Understanding the Key Differences

Data is one of the most valuable assets an organization has, yet it is often treated as an IT byproduct rather than a strategic asset. Many companies collect a vast amount of data, store it in isolated systems, and expect that insights will somehow emerge from these raw numbers.

Simply storing data isn’t enough. To truly unlock its value, organizations need to treat data as a product: something that is deliberately designed and made usable for specific business needs.

At first glance, this may sound like additional overhead. Another layer of governance? That’s going to slow our initiatives down! But in reality, the opposite is true. Consider the amount of time spent questioning data sets when they are dumped in a centralized system. How many times have you asked yourself questions like: Is it really the data set we need? Oh wait, this column actually means something else in our data model. The data in this table seems incomplete. I can’t understand what it represents. The schema changed again, now our system is broken in production…

Modern frameworks have proposed alternative ways of managing data sets. But it all stems from a different way of thinking about your data. In this post, we’ll break down the key concepts behind data as a product, the difference between data sets and data products, and how this shift improves usability, accessibility, and governance.

Understanding the Basics: Data, Data Sets, and Products

Before diving into data as a product, let’s clarify some fundamental terms:

Data: The raw facts and figures collected by an organization—numbers, text, timestamps, sensor readings, etc.
Data Set: A structured collection of related data points, typically stored in formats like tables, CSV files, or databases. Examples include a table of customer transactions, machine sensor logs, or financial records.
Product: Something deliberately designed and built to provide value to a user, whether physical (a phone) or digital (a mobile app).
Data Product: A combination of data set(s), domain model, and user experience, designed for a specific use case. Examples include a fraud detection model based on transaction data, a self-service analytics dashboard for sales teams, or an API that provides real-time customer insights.

Data Set vs. Data Product: What’s the Difference?

Let’s start with an example and consider the sales data in an e-commerce business.

A data set might contain raw customer transactions with thousands of line items. Without context, a user might struggle to extract insights. The transactions don’t serve any purpose by themselves: they are simply a collection of information.
A data product could be a "Monthly Sales Performance Dashboard", which combines one or more data sets (the raw transactions), the domain model (aggregation into revenue trends, regional breakdowns, …) and the user experience (the graphic UI of the dashboard, its access policies, the ability to drill down in a report, …).

A data set is a fundamental component of a data product. But a data product is much more than that. It must include:

✅ Defined ownership – Who is responsible for maintaining the data?

✅ Clear documentation – What does the data mean? How should it be used?

✅ Usability – Is the data structured in a way that users can access and understand?

✅ Ongoing updates & maintenance – Is the data kept fresh and accurate?

Business Domains Define Data Products

Organizations don’t need just data. They need data that serves a purpose. That’s why defining data products should always start from the business domain:

🔹 Finance teams may need a revenue forecasting data product based on historical transactions.

🔹 Marketing teams may need a customer segmentation data product that enriches demographic data with purchase behavior.

🔹 Operations teams may need a real-time logistics dashboard that tracks shipment statuses and delays.

By starting from business domain needs, companies can design data products that are immediately useful, rather than dumping raw data into a central repository and expecting teams to figure it out on their own.

What Does It Mean to Treat Data as a Product?

A data product must be designed, maintained, improved, and eventually retired when no longer needed. This approach ensures that data remains valuable and does not become obsolete. The whole lifecycle should revolve around the following key principles that augment the world of “data sets” by introducing product thinking as a new dimension.

Usability & Customer-Centricity

🔹 Who needs this data? How will they use it?

A well-designed data product is intuitive and built for the end user. This means:

✅ Well-documented definitions so users understand the data.

✅ Consistent structure and formatting to avoid confusion.

✅ Version-controlled updates to prevent disruptions in workflows.

Unlike traditional data management, which prioritizes storage and availability, a product mindset ensures that data is packaged for real-world usage, just like a consumer-facing app or tool.

Accessibility

🔹 How easily can users find and interact with the data?

Many organizations struggle with data locked away in silos, making it difficult for teams to access and use. A data product should be discoverable and well-integrated, for example it should have:

✅ Self-service data catalogs to remove unnecessary friction between teams and questions such as “Could you tell me which data sets are available?”

✅ Role-based permissions to manage security and compliance.

✅ Standardized formats to allow smooth integration with other tools and platforms.

By ensuring discoverability and controlled access, data becomes a trusted, reusable resource instead of a hidden asset that requires manual extraction.

Pragmatic Governance with Data as a Product

One of the biggest challenges with traditional data management is poor governance—unclear ownership, inconsistencies, and compliance risks. Treating data as a product solves many governance issues by design.

1. Clear Ownership & Accountability

Every data product has a designated owner (often a business or data team) responsible for its accuracy and updates.
Unlike traditional IT-driven data models, ownership is distributed across business domains. This ensures, for example, that data issues are addressed by people who have a deep understanding of the business processes that generate the data itself.

2. Built-in Data Quality

Errors and inconsistencies are addressed before reaching users, thanks to holistic observability.
Data contracts ensure that data sources follow a standard schema and remain compatible across different systems.

3. Stronger Security

Access controls are built into data products, preventing unauthorized access while facilitating access for authorized users.
Automated audit logs track data usage, making compliance with GDPR, CCPA, and other regulations easier.

📌 According to a 2024 Gartner survey (Evolution of Data Management), the top investment trends for the next three years are AI-ready Data Initiatives and Data quality & Governance. Treating data as a product supports both: AI models require structured, well-maintained data, and governance naturally improves when ownership and usability are prioritized.

Data as a Product is a Transformation, Not Just a Definition

Shifting to data as a product is not just a matter of changing terminology—it requires a fundamental transformation in how an organization creates, manages, and uses data. This shift impacts business processes, technology, governance, and culture.

The transition can be challenging, especially in organizations with legacy systems and siloed data ownership. More importantly, the biggest hurdle is a culture that treats data as an IT responsibility rather than a business enabler.

If you're looking to adopt a data-as-a-product approach and need guidance on where to start, get in touch. We can help you design a strategy that fits your business and unlocks the full value of your data. 🚀

Here’s a story on the transformative journey of Data as a Product at one of our past clients.

Data Set or Data Product, That is the Question

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