7 min read
What Is a Data Product? Part 1
In February 2026, Qlik launched Data Products for Qlik Analytics (announcement). Although the functionality already existed as part of Qlik Talend Data Integration, making these capabilities available to every Qlik Cloud environment creates a great opportunity to rethink how we approach data governance in Qlik.
The cornerstone of this new functionality is the 'data product'. You may have heard the term before without anyone bothering to explain exactly what it means. If you've nodded along while someone said it (and then secretly googled it afterward), this blog is for you.
In this first part of a four-part series, I explain what a data product is, why the concept matters, and which benefits and challenges you should know about. By the end of this article, you'll even be able to explain to your most data-curious non-data colleague what a data product actually is.
Data as a product – what does that actually mean?
Imagine your organization is a factory. Raw materials (your source data) come in, go through a production process (data pipelines, transformations), and eventually come out the other side as products that customers want to buy and use.
Most organizations by now produce various forms of data: dashboards, reports, prediction models. But here's the problem: in many organizations, the 'factory' is a mess. There are files everywhere with names only the original developer can decipher. There are multiple versions of 'the truth', depending on which team you ask. And whoever needs data doesn't know where to find it, whether it's correct, or who to call if something's wrong.
A data product solves this by treating a composed, well-defined set of data as something you deliberately design, maintain, and deliver. Just like a software product or a physical product. It's not just data; it's data with documentation, clear ownership, defined quality standards, and a way for consumers to find and use it, without needing a data engineer on the phone.
The building blocks of a good data product
Data products didn't spring up out of nowhere. The concept is closely tied to a broader data architecture philosophy: the Data Mesh. The Data Mesh moves away from the traditional centralized data lake or warehouse model, where one team is responsible for all data, and instead distributes ownership to the domain teams that know the data best.
Imagine your Sales team manages a 'Customers & Opportunities' data product, Finance manages a 'Financial Results' data product, and HR manages an 'Employees & Headcount' data product. Each team manages its own data product but makes it available to the rest of the organization through shared infrastructure and standards.
A data product is more than a dataset. For something to genuinely qualify as a data product, it needs to meet a number of essential characteristics:
1. Discoverability
If users can't find it, it doesn't exist. A data product needs to be discoverable: through a catalog, a marketplace, or at the very least a well-maintained documentation page. People need to know the product exists, what's in it, and whether it fits their needs.
2. Trustworthiness
Data is only useful if people trust it. That means data quality needs to be measurable and visible. Users shouldn't have to wonder whether the numbers are right; they should be able to see whether the data quality is up to standard, whether the data is fresh, and where the data comes from.
3. Clear ownership
Every data product needs an owner. Not a team, not a department, but an actual flesh-and-blood person responsible for keeping the product up to date, answering questions, and making decisions about its content and direction. Without ownership, quality will inevitably decline sooner or later.
4. Usability
A data product is only valuable if people can actually get to work with it. That means offering data in formats and through interfaces that match the consumer's needs, whether that's an analyst building a dashboard, a developer calling an API, or a data scientist training a model.
5. Interoperability
Good data products work well together with others. They use standardized formats, clear data definitions (also known as a data contract), and consistent naming, so consumers can combine data products without needing an army of data engineers to align everything.
Is a data product right for you?
Data products aren't a miracle cure, and adopting the paradigm takes real effort. So why do it anyway?
In many organizations, the same data gets transformed, cleaned, and loaded over and over by different teams, each building their own version. Data products create one shared, well-maintained version everyone can reuse.
If users can find and trust a data product without having to go through the data team for every request, they can get to work much faster. Clear ownership, documented lineage, and measurable quality standards don't just make users happy, they also make your data landscape much easier to manage. Audits, legal requirements, and privacy obligations all become simpler when you know exactly what data exists, where it comes from, and who's responsible for it. On top of that, data products, with their built-in quality guarantees and rich metadata, are exactly the kind of curated, trustworthy data that AI applications need to work reliably.
Should you jump on the train right away and go all in? Before you do, it's good to know that the Data Mesh philosophy requires domain teams to take ownership of data they previously handed off to a central data team. That's a culture change, and it doesn't happen overnight. Teams need to be upskilled, motivated, and supported. Distributed ownership is fantastic, but it also means you need strong central standards to prevent the distributed model from turning into chaos. Reaching agreement on data definitions, quality thresholds, and naming conventions across teams takes time and is politically sensitive. To get consumers to actually use a data product, they need to believe it's maintained and reliable, and that takes time. Buy-in from stakeholders at every level is crucial to making Data Products a success in your organization.
The good news is you don't have to overhaul your entire organization in one go. You can start small, with one domain, one data product, and learn from the experience before scaling up.
So, where do you start?
If your organization is dealing with one or more of the following situations, Data Products are worth exploring:
- Multiple teams building similar data assets without knowing about each other's work
- A lack of trust in the data, causing people to keep their own spreadsheets 'just in case'
- A data team that's the bottleneck for every new analytics initiative
- Difficulty answering the question 'where does this number come from?'
- Growing AI or advanced analytics ambitions that require high-quality, well-documented data
The recent release of data product functionality for Qlik Analytics makes this the perfect moment to build your first Qlik data product. No idea how? Good news: in the next article in this series, I'll show you exactly how to create a data product in Qlik, what the various components look like, and which decisions you'll need to make along the way.
Contact
Want to discover what data products in Qlik can mean for your organization? E-mergo helps organizations design and implement data products, set up data governance, and get the most out of Qlik. Feel free to get in touch with us to explore the possibilities together.
Written by Lennaert van den Brink
Cluster Manager/Senior BI Consultant