Introduction: Why You Should Care about Treating Data as a Product

“Without big data, you are blind and deaf in the middle of the freeway.” -- Geoffrey Moore, Hadoop Summit keynote, 2012

Back in 2012, organizations raced to collect data. Now we hoard data, yet still struggle to make sense of it.

Most enterprises share similar obstacles: siloed systems, an unrelenting influx of data, redundant data, poor quality, integration hurdles, slow insights, high operational costs, and growing security and compliance risks.

The typical response is to add staff or acquire a new platform. However, if your struggles still persist, it means you haven’t gotten to the root of the problem.

The real issue is that the traditional data management architecture most organizations use was built for yesterday’s technology and business needs. Centralized data warehouses, tightly controlled pipelines, and siloed systems are falling apart under the strain of modern demands, an explosion of complex data, and the need for speed and scale.  

What’s the solution?

The only way to solve the problem is to rethink how you handle data.

The data-as-a-product approach is a modern data management paradigm designed to deliver timely, reliable insights by packaging data as well-defined “products” with clear ownership, quality standards, and a focus on the consumer.

Data products are easily discovered and accessed, interoperable, and reusable, which breaks down silos and turns raw data into a strategic asset that delivers measurable business value.

Imagine regaining trust in your data and using it to get timely insights, innovate, offer personalization, and accelerate advanced analytics, machine learning, and AI.  

Leading organizations are already seeing results. Harvard Business Review reports, “Companies that treat data like a product can reduce the time it takes to implement it in new use cases by as much as 90%.”

Gartner even named “highly consumable data products” as a top data and analytics trend for 2025.

In this article, we’ll give you a brief overview of DaaP thinking and how it fits into a larger data mesh architecture. For a deeper drive, practical guidance, and critical advice about what you should and shouldn’t do, download our whitepaper, Data-as-a-Product Paradigm: How Leading Companies Unlock Their Data and its companion whitepaper, Data Mesh Architecture: How Leading Companies Win with Data.

What is the DaaP paradigm?

The DaaP paradigm applies product management thinking to how we manage and deliver data. Instead of treating data as the unintentional byproduct of digital activities, DaaP thinking treats data as a high-value product intentionally designed to serve specific use cases and meet user needs.

The shift to a DaaP approach isn’t about buying another tool. It’s about changing your data mindset and unlocking a powerful new way to deliver value from the data you already have.

Is DaaP thinking the same as a data product?

No. DaaP thinking is a philosophy where you treat data like a product that you plan, design, build, and iterate with your customer in mind.

A data product is the tangible output of a DaaP approach: a consumable and reusable standalone product or asset with defined owners, a full lifecycle, and measurable business impact.

A data product isn’t just a raw table or dashboard. It’s a managed, documented, versioned, and discoverable dataset engineered for reuse. You build a data product with such clear specifications and quality that someone can repurpose it in ways you never imagined.

A data product might pull data from multiple sources to answer a business question or address a use case. It can also perform calculations and generate new data if it doesn’t already exist elsewhere in the organization.

Data products vary in complexity. Here are some examples:

• Dashboards and reports
• 360-degree customer profiles
• Transaction records and activity logs
• Machine learning models
• AI assistants
• APIs and microservices
• Sales prediction tools
• Health-monitoring wearables

How the DaaP Paradigm Fits into Data Mesh

The data-as-a-product paradigm serves as a key foundation and pillar of data mesh, which is a sociotechnical, architectural, and organizational model that decentralizes data ownership and delivery. By establishing a new way of thinking about data, organizations make way for the other three pillars of data mesh:

• Domain-driven ownership
• Self-service access
• Federated computational governance

The result of this architecture is that domain teams build purpose-driven data products according to a shared organizational governance structure, which users access through a self-service data platform. There, they can view data products and dig down into descriptions and specifications of each product — much like online shopping. Role-based permissions then control the extent of visibility and provide security.

All of this comes together to create and strengthen an ecosystem where everyone — including business leaders, IT teams, and functional domain teams — views data as a valuable, intentionally designed product.

Preview What’s Possible with DaaP Thinking & Data Mesh

Take a moment to imagine how this can impact your organization.

• Instead of waiting weeks for IT to build new reports, you get answers instantly via self-service access to accurate, pre-packaged data that’s ready to use.
• What if your sales teams no longer needed to chase spreadsheets and your data analysts no longer wasted valuable time reconciling conflicting data sources for just one request? Instead, they find instant answers to questions through self-service access to pre-packed data.
• How would it change your company to have up-to-date, accurate data on customers, inventory, and operations available at any time via a data product marketplace?
• What kind of personalization could you deliver with a clear, 360-degree view of your customers, and how would that affect engagement?
• How quickly could you make predictions or find answers to business questions through ready-to-use, curated data for advanced analytics, machine learning, and AI initiatives?
• What kind of innovations and possibilities could you unlock with a wealth of clean, consistent data at your fingertips?
• How much more easily could cross-functional teams collaborate if everyone’s data products were accessible and managed by shared governance and consistent product management practices?

The Difference Between a DaaP Approach and Traditional Data Management Approaches

Fundamentally, DaaP changes how data is owned, managed, and delivered.

In traditional data management, data is an afterthought, a byproduct of digital interactions, transactions, and processes. With a data-as-a-product approach, data is intentionally designed and packaged for specific use cases and end users.

In traditional data management, centralized, IT-owned monolithic systems store data. With a data-as-a-product approach, domain-owned data products decentralize ownership and align with business context through a self-service data marketplace.

In traditional data management, organizations have a separate analytics platform and disconnected pipelines. With a data-as-a-product approach, products are created to be integrated and interoperable across domains.

In traditional data management, data assets are created reactively; IT or data teams receive requests in a queue and cannot fulfill them quickly. With a data-as-a-product approach, data products are created proactively; business users also have self-serve access to other data products.

In traditional data management, companies experience limited visibility and questionable trust in their data. With a data-as-a-product approach, users trust and rely on data products because they are transparent, discoverable, and documented.

In traditional data management, static reports are quickly outdated, and building new dashboards is time-consuming. With a data-as-a-product approach, the organization’s living, evolving assets can be accessed in real time.

In traditional data management, limited reuse of data causes efforts to be duplicated across teams. With a data-as-a-product approach, reusable assets scale with business needs, and there’s no need for teams to start from scratch.

In traditional data management, there’s no clear ownership of data assets. With a data-as-a-product approach, assigned and accountable product owners define a roadmap, lifecycle, and feedback loops.

How to Implement DaaP

Every company has different needs, but here is a high-level roadmap that explains how to implement DaaP:

• ‍Choose an engaged business partner with a clear understanding of their data and a specific, actionable goal or KPI.‍
• Identify a pilot use case where you can solve a high-value business problem with clear KPIs.
• ‍Create a cross-functional domain team consisting of the business stakeholder, data engineers, and product owners.
• ‍Define what data product to start with, who it will serve, its purpose, quality metrics, and set expectations for data freshness and availability.
• ‍Establish a self-service platform by providing the infrastructure fordiscovery, access, and future automation.
• ‍Apply product thinking so that you treat data like you’d treat software, with version control, documentation, iterative development, and feedback loops.
• ‍Implement federated governance, setting organizational standards for security, privacy, and interoperability with domains in charge of managing their own data.
• ‍Automate and monitor the data products, adding observability, automated quality checks, and performance dashboards.
• ‍Scale and iterate, expanding to additional domains, reuse suggestions, and continuously refine data products to align with changes in the business and user feedback.

Marlabs eases clients into the world of DaaP by running a small, manual pilot with no automation. We manually apply “FAIR” principles (the key characteristics of a data product: findable, accessible, interoperable, reusable) to set a solid foundation. Our experts work with your team to ensure we stick closely to the principles of data mesh, which mitigates risk and creates efficiencies. The focus of this pilot is to demonstrate tangible results and prove the value of DaaP before your organization moves forward.

DaaP in Action: Biotech Case Study

Marlabs helped a leading biotechnology company modernize its sales and marketing data ecosystem using a DaaP approach. Their legacy sales data mart was rigid, manual, and unable to support decentralized ownership or timely decision-making. Marlabs implemented a Snowflake-powered platform with governance, observability, and clear domain ownership. This transformation eliminated thousands of hours of manual work, empowered business units to manage their own data products, improved accessibility and trust in the data, and laid the foundation for a scalable and federated data culture.

Summary

For years, we’ve heard about the democratization of data. Now, through DaaP, it has come to life. Data-as-a-product thinking is not a trend. It’s a modern necessity and practical path to turning your data into a true competitive advantage. By treating data as a product and following the principles of data mesh, leading organizations have successfully replaced brittle, centralized architectures with scalable, domain-oriented data products that deliver trusted, reusable insights on demand.

Whether you start with a small pilot or a single, high-value use case, DaaP builds the foundation for faster innovation, stronger governance, and AI-ready data. Marlabs offers you guidance and a proven framework to turn overwhelming data challenges into a foundation for future success. The journey starts with knowledge, which is why we’ve created our whitepapers, Data-as-a-Product Paradigm: How Leading Companies Unlock Their Data and Data Mesh Architecture: How Leading Companies Win with Data. Download the whitepapers for:

• Guidance on whether data mesh is right for you (it isn’t always)
• Advice about implementation do’s and don’ts
• Access to our framework for reaching data mesh maturity
• More examples of how industry leaders have adopted DaaP

Contact Marlabs to learn more.