Organizations today are sitting on more data than ever before, yet access to this data remains a persistent challenge. Teams often spend more time searching for data—or recreating what’s already available—because the path to existing assets is unclear. This isn’t a problem of infrastructure but a problem of experience.
Data productization offers a new way forward. It reframes data as something consumable and user-centered. In other words, a product that is discoverable, trustworthy, and ready to use. But making this shift isn’t just about new tools. It’s about creating a system that supports people. The starting point? Designing the consumer experience.
Let’s begin with a distinction. To understand what a data storefront should be, it helps to start with what it’s not. A consumer experience is not a searchable archive of datasets. It’s not a dumping ground for raw exports or a folder structure organized by source systems. Archives are useful for storage and compliance, but they’re not designed to help people use data in meaningful ways.
A true consumer experience is more like a storefront. It’s intentional. It’s curated. And it’s built to guide someone from “I have a question” to “I found what I need.” In this model, data products are treated like any other product: they have a clear name, a description of what they do, documented terms of use, and support channels if something breaks.
The key is not just availability but also its usability.
Building a great consumer experience is not just a UI project. It involves strategic thinking across design, governance, operations, and engagement. Below are five principles we use at CTI that help turn this concept into something real and sustainable.
Designing for “the business” or “data consumers” is too vague. Instead, define clear user personas, such as an operations analyst who requires up-to-date transaction data every morning or a marketing manager analyzing trends in customer behavior. Each persona has different goals, tools, and technical comfort levels.
By mapping these roles upfront, you can tailor both the content (i.e., which data products are published) and the experience (i.e., how they’re accessed, previewed, or explained) to actual needs. This reduces confusion and increases adoption from day one.
Every data product should come with thoughtful packaging. This includes a product name, a short description, owner information, usage terms, last updated date, and data quality indicators. Include documentation, known limitations, and links to related products.
Think of this as the “product card” in the data storefront. Done well, it reduces the need for back-and-forth emails and gives users enough context to move forward with confidence. When teams know what a dataset is and how to use it, they’re far more likely to use it correctly.
The user journey stalls when people have to leave the platform just to request access. Embedding request and approval workflows directly into the platform keeps the process moving and maintains context.
Some products might allow for instant access. Others might require data owner approval or conditional access based on role or region. Regardless, the experience should be consistent, trackable, and clearly governed. Users should never be left wondering where their request stands or who to contact for follow-up.
Different users need different access paths. A BI user might want a dashboard. A data engineer might prefer a SQL endpoint. A data scientist might need an API. Instead of forcing one access method, design your experience to offer multiple delivery formats through a shared entry point.
What matters most is that the experience feels unified. Whether someone is browsing a web interface or calling an endpoint in code, they should be able to rely on the same metadata, documentation, and access policies. Fragmentation erodes trust; cohesion builds it.
Most data platforms today rely on users to know what to look for. This passive model assumes too much. In contrast, a well-designed consumer experience offers active discovery – think featured products, curated collections by theme or department, and recommendations based on user behavior or organizational priorities. This proactive approach not only helps users find what they need but also helps them discover what they didn’t know existed. It encourages reuse, standardization, and data literacy. That’s where real value compounds.
When the consumer experience works, everything downstream improves. Teams spend less time hunting for data and more time using it. The data team deals with fewer access requests. Governance becomes easier to enforce. And most importantly, decision-making becomes faster and more confident. Investing in this layer isn’t just about presentation — it’s about making data usable at scale.” The quality of the experience often determines whether the investment in infrastructure, pipelines, and platforms will actually pay off.
Bianca Firtin is a Lead Data & Analytics Consultant at CTIData.
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