Think about your favorite app store for a moment. You open it, type a few words, and instantly discover exactly what you need. Apps are neatly sorted, each offering clear and valuable functions. But what happens when you or your team need data at work? If finding the right dataset feels more like searching through an unorganized storage room than browsing an app store, you’re not alone.

What if data in your organization were packaged as neatly and intentionally as apps on your phone? This idea is at the heart of data productization, the practice of transforming raw data into structured, user-friendly assets. Just like apps, these data products serve clearly defined purposes, making them instantly valuable both inside your organization and beyond.

Inside Your Organization: Self-Service and Agility

Internally, productized data is about creating instant access, trust, and agility. Think again of an app store: no one calls a developer every time they need a calculator or calendar; they just download an app. Similarly, when your data is productized, your teams don’t have to request datasets, wait for permissions, or struggle to interpret confusing raw files. Instead, they simply access pre-curated, documented, and reliable data products, ready for immediate use.

Imagine your marketing team quickly exploring customer insights without navigating confusing databases or needing constant IT help. Or your finance team seamlessly integrating pre-built forecasting datasets directly into their reports without weeks of manual preparation. When data becomes an internal product, your organization shifts from slow-moving, reactive decisions to proactive, real-time insights. It means fewer meetings spent debating data quality and more time acting on reliable, shared intelligence.

This change isn’t just technical, it’s a cultural shift. When everyone can access trusted data effortlessly, collaboration naturally thrives. Data silos start to disappear, and strategic alignment across teams becomes easier and faster. Your teams move forward together, informed and confident.

Outside Your Organization: Monetizing Your Data Assets

Externally, productizing data opens entirely new ways to create value and revenue. Much like app developers share their innovations through the app store and reach users worldwide, your organization can also deliver valuable insights externally through structured, monetizable data products.

One practical way to do this is through a Data-as-a-Service (DaaS) model. Think of DaaS as offering a subscription-based “storefront” for data, where external partners or clients can easily search, select, and access your datasets through user-friendly portals, paying only for what they consume. You don’t just share data; you deliver actionable insights without forcing clients to manage complicated infrastructures themselves.

Another powerful avenue is API-driven data sharing. APIs allow external partners to pull your data products directly into their existing workflows, platforms, or analytics tools. It’s akin to providing a direct plug-in or add-on, helping clients quickly leverage your data without needing complicated integration. Monetizing APIs becomes straightforward: pricing can reflect usage levels, the value of enriched datasets, or premium access tiers.

Data marketplaces further expand this external opportunity. Platforms like Snowflake Data Marketplace, AWS Data Exchange, Google Cloud Analytics Hub, or a custom platform built for your needs act as digital malls, where organizations securely package and offer datasets to a vast community of potential buyers and collaborators. Data is presented in clear, structured packages, and buyers know exactly what they’re getting, just as they would when choosing an app based on its features, ratings, or relevance.

You don’t need to give away sensitive information to monetize your data, either. Through synthetic data, companies can safely share insights without risking privacy or compliance issues by using artificially generated yet statistically accurate datasets. It’s like showcasing a product’s capabilities without exposing trade secrets, broadening your reach without compromising security.

These opportunities and strategies for capitalizing on them are explored in more detail in Section 6 of our white paper, Enabling Data Products at Scale, which outlines how to shift from traditional data management to a product mindset that drives both internal efficiency and external monetization.

Overcoming Barriers to Success

Productizing your data comes with its own set of challenges. It requires thoughtful governance, robust security, and consistent data quality. Like apps requiring regular updates and reviews to maintain quality and usability, your data products must also be actively managed:

• Governance and Security: Clearly define who owns, accesses, and maintains each data product, and ensure robust security to manage privacy and compliance.
• Quality Assurance: Continuously monitor and validate data quality to ensure trust remains high.
• Cultural Change: Foster an organizational mindset shift from treating data as a passive resource to actively viewing it as a valuable and reusable asset.

Your First Step: Starting Small to Think Big

So, where should your journey start? Much like creating an app, start small. Select a dataset within your organization, one that’s valuable, relevant, but currently underutilized. Next, clearly define its purpose, who will use it, and how they’ll access it.

Ask yourself a simple, guiding question:

“If this dataset appeared in our organization’s own app store, would it attract users?”

If the answer is “yes,” you’re already on your way. Productizing your data starts with this simple step: seeing your data as something your internal and external customers not only need, but would willingly seek out, use, and even pay for.

Now, take action: select one dataset, clearly define its purpose, and transform it into your organization’s first genuine data product. You might just unlock value you never imagined possible.

Bianca Firtin is a Lead Data & Analytics Consultant at CTIData.