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

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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

Let’s look at our data product definition again.

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.

A data product:

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

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.

Consider this example: Your organization has valuable customer segmentation data. For the marketing team, the value proposition might be “understand customer behavior to create targeted campaigns.” But how you deliver that value varies dramatically based on who specifically needs it:

• A marketing analyst might need direct access to explore the segments in familiar BI tools
• A campaign manager might need automated segment updates fed into their marketing platform
• A CMO might need high-level insights delivered in their weekly dashboard

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.

This is why defining your target market goes beyond just naming a department or role. You need to understand:

• Their technical sophistication and preferred tools
• The decisions they need to make
• Their time constraints and work patterns
• How they measure success
• Where your data product fits into their existing workflows

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

Let’s now move onto the three Ps of data product management: Placement, packaging, and permissioning.

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.

The three Ps are interconnected:

• Placement must allow sufficient packaging and permissioning to enable discovery and access.
• Packaging must adhere to the constraints of where it’s placed and include permissioning.
• Permissioning must work within the limitations of where it’s placed and also be well-packaged.

Let’s go into a bit more detail about the three Ps.

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).

Consider a data scientist who spends their day in Jupyter notebooks. They’re unlikely to regularly check a BI dashboard portal, no matter how valuable the underlying data might be. For them, the ideal placement might be a Python package they can import directly into their workflow, or an API they can query programmatically.

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?
  • Understanding their discovery process helps you position your data product where they’ll actually find it.
• Where does your ICP spend their time?
  • Your data product should be accessible within their existing workflows and tools rather than requiring them to adopt new ones.
• How much of an explanation does your data product require?
  • This will influence whether self-service discovery and access is feasible or if a more hands-on sales approach is needed.
• Is it okay for anyone to see your data product?
  • Some products might be appropriate for public marketplaces, while others need to remain private or restricted.
• What constraints need to be applied to the access and usage of your data product?
  • This will help determine which placement channels are suitable from a security and compliance perspective.
• How easy is it for your ICP to use your data product?
  • The complexity of your product might influence whether it can be placed in self-service channels or requires more supported environments.
• What format are the data assets within your data product?
  • Different formats might require different placement strategies to ensure they’re accessible and usable.

Channels to place your data product

There are a range of channels that your data products may need to present in. Once you understand where your ICP with the questions above, consider the following channels:

• Adverts to reach new potential customers
• Website listings and product pages
• Manual sales and fulfillment processes
• Private data marketplaces for internal or select external users
• First-party tools and platforms you own and control
• Public data marketplaces to reach a broader audience
• Third-party tools where your users already work
• Partners and resellers who can extend your reach

The most elegant technical solution will fail if users don’t know it exists or can’t access it in their normal workflow. That’s why understanding your ICP’s daily habits, preferred tools, and existing processes is crucial for successful placement. Where you place your product will impact adoption, growth, and scale.

Key takeaways:

• Placement success requires deep understanding of your ICP’s habits and needs
• Consider multiple channels for discovery and access
• The best placement strategy will vary by user type
• Technical excellence means nothing if users can’t find or access your product

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