What is a data product? A complete guide to data product management in 2025

Read time:

15 min read

Author:

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Anthony Cosgrove
Co-Founder

Date published:

2.17.2025

What is a data product?

What exactly is a data product? And more importantly, why does it matter?

If you ask 10 different data leaders, you'll get 10 different answers.

After years of working with organizations building and scaling their data products, I've landed on a definition that I believe captures what truly matters. While no definition is ever going to be perfect, this seems to spark the right conversations and drive good outcomes:

A data product is one or more data assets that deliver a value proposition to a defined target market and is optimally placed, packaged, and permissioned.

Breaking that down, a data product:

Contains one or more data assets
Delivers a value proposition to a defined target market
Is optimally placed, packaged, and permissioned

Let's take a closer look at each component of this definition and explore why it matters — starting with data assets.

Data assets: The foundation of a data product

At their core, data assets are the raw materials of working with data: tables, notebooks, SQL queries, and files. In a data product context, these building blocks are carefully assembled, combined, and refined to solve specific problems. Any given data asset could be used across multiple data products, each driving towards a different goal.

It’s important that data assets are purposefully designed and maintained to serve specific use cases. They're not just raw data dumps or hastily assembled visualizations — they're carefully crafted resources that solve specific problems.

Key takeaways:

Data assets are the fundamental building blocks like tables, notebooks, and queries
A single data asset can be used across multiple data products

Delivering value to a defined market

Every successful data product starts with a clear understanding of who it serves and what problem it solves for them. This might sound obvious, but it's where many data initiatives falter — they begin with the data that's available rather than the value that's needed. As Nicolas Averseng put it on the Data Product Mindset, too many data leaders are concerned with being “data-driven”, rather than “value-driven”.

A value proposition in the context of data products answers the question: "What specific problem does this solve, or what opportunity does it unlock?" Some examples:

Reducing time spent manually gathering data for monthly reports
Enabling real-time decision making instead of working with day-old data
Providing insights that were previously impossible to surface
Automating processes that required human judgment

But having a compelling value proposition isn't enough — you need to deeply understand your target market. Data products often fail not because they lack value, but because they don't match how their intended users actually work. A data scientist comfortable with Python notebooks has very different needs from a business analyst who lives in Excel, who in turn has different needs from an executive who wants insights delivered to their phone.

The key is that the same underlying data can drive multiple value propositions for different target markets. The art of data product management lies in identifying which combinations of value proposition and target market are worth pursuing, and then tailoring your product accordingly.

Key takeaways:

Start with the value needed, not the data available
The same data can drive different value propositions for different users
Understanding your target market goes beyond just their role or department
Success depends on matching your solution to how users actually work

The three Ps: Placement, packaging, and permissioning

Having valuable data assets and a clear target market isn't enough. The difference between a successful data product and an underutilized data asset often comes down to how it's delivered. This is where placement, packaging, and permissioning come into play.

These three elements aren't about cosmetics or bureaucracy — they're about maximizing the value of your data assets by making them discoverable, usable, and secure. You might have the most valuable data in the world, but if users can't find it, understand it, or access it appropriately, it might as well not exist.

Placement

Placement addresses where your product can be discovered and is made available. Good placement requires a deep understanding of your ideal customer profile (ICP).

The same data product might need different placement strategies for different users:

Business analysts might need the data surfaced in their existing BI tools
Data engineers might need access through their data catalog or marketplace
Product managers might need insights delivered via Slack
External partners might need a secure API endpoint

Data product placement checklist

Placement isn't just about technical integration — it's also about discovery. When deciding where to place your data product, ask yourself these key questions:

Where does your ICP look for solutions to their problems?
Where does your ICP spend their time?
How much of an explanation does your data product require?
Is it okay for anyone to see your data product?
What constraints need to be applied to the access and usage of your data product?
How easy is it for your ICP to use your data product?
What format are the data assets within your data product?

Packaging

Packaging describes how your product is described and branded. The goal of packaging is to make your data product as self-service as possible. You can't scale if every new user needs a one-on-one orientation session. Your packaging should enable users to discover, understand, and start using your data product with minimal hand-holding.

Anticipating user needs with packaging

Good packaging anticipates user needs and questions:

What is this data and where did it come from?
How frequently is it updated?
What can (and can't) it be used for?
How do I get started?
What do these fields mean?
Who do I contact if I have questions?

Permissioning

Permissioning is how access and usage of your product is managed. Every successful data product needs a clear strategy for who can access what, under what conditions, and how that access is granted and maintained.

Aligning your permissioning strategy

The key is to align your permissioning strategy with both your security requirements and your users’ needs. This could mean:

Setting up role-based access where marketing teams can only see customer data relevant to their region
Creating approval workflows that let users request access to specific datasets
Implementing usage quotas to prevent any single user from overwhelming your systems
Establishing different access tiers based on data sensitivity or business value
Building audit trails to track who's accessing what and when

Getting the 3 Ps to work together in harmony

When these three elements work together effectively, they create a flywheel effect. Good placement makes your data product easy to find. Good packaging makes it easy to understand and use. Good permissioning makes it easy to access appropriately. This leads to higher adoption, more feedback, and continuous improvement of your data product.

Data products from theory to practice

Defining a data product as "one or more data assets that deliver a value proposition to a defined target market and is optimally placed, packaged, and permissioned" gives us a framework for thinking about what makes data initiatives successful. But more importantly, it gives us a practical checklist for creating value:

Do we understand our data assets — what they are, what they can do, and what they can’t?
Are we clear on the value proposition, and have we validated it with our target market?
Have we thought deeply about placement, ensuring our product is discoverable and accessible where our users actually work?
Does our packaging enable understanding, self-service, and adoption?
Have we struck the right balance with permissioning to protect our assets while enabling value creation?

Creating successful data products isn't about perfect data or cutting-edge technology - it's about understanding your users, their needs, and how to deliver value to them effectively.