Data Products vs Data as a Product: What’s the Difference?

According to one study, 89% of business leaders agree that too much data limits success. Process complexity is a significant contributor to this. 

Traditionally, the process begins with a data consumer defining a need—for example, a marketing team wanting to understand why customer churn is rising. To answer that question, they may enlist a data analyst or data scientist to access multiple sources such as CRM data, product usage logs, and customer support tickets, then stitch these together into a relevant dataset. 

For this single business need, the cross-functional team often works across numerous technologies and systems just to capture a snapshot of the data and deliver it to the marketing team. The problem is that the snapshot is instantly outdated, meaning the entire costly, manual process must be repeated every time the churn question is asked again or a new angle needs to be explored.

To address this challenge, a new approach to finding, accessing, and using data is necessary. It’s rooted in the data as a product (DAAP) philosophy, which has evolved into what we now refer to as “data products” (DP).  Understanding both terms is key to modern data transformation.

Key takeaways: What to know about data as a product

Here’s a quick overview of what you should know about data as a product:

• The traditional data delivery model is reactive, inefficient, and unscalable.

• “Data as a product” is a design principle that emerged from data mesh, emphasizing ownership, usability, and value.

• “Data products” are the operationalization of this idea: packaged datasets, dashboards, or APIs designed for a specific purpose.

• The product mindset fosters cross-functional collaboration between data scientists, engineers, and product managers to deliver reusable data assets.

• Alation’s Data Products Builder and other solutions enable both strategic and technical implementation of data products by unifying governance, discoverability, and collaboration.

Let’s explore the historical evolution of data products, the practical realities, and the best practices that connect theory to execution.

What is data as a product?

DAAP began as a design philosophy—introduced by computer scientist Zhamak Dehghani—that emphasized data ownership, usability, and accountability across domains. Over time, this theory catalyzed the creation of data products: tangible assets like datasets, APIs, or dashboards engineered with clear owners, defined users, and measurable value.

The analogy often used is that of a furniture factory. A factory sources materials from many different suppliers, employs workers with diverse skill sets, relies on specialized machinery, and produces a wide range of items—from chairs and tables to upholstered sofas, china cabinets, and bed frames. 

In the same way, data as a product describes the overall process of creating and delivering data products. The products themselves, however, serve a singular purpose. A data product is like a chair: built for a clear function, such as enabling someone to sit. Similarly, data products are designed for specific use cases, including:

• Business reporting: Secure and easily accessible dashboards, reports, and summaries, with access automatically controlled via a data marketplace

• AI and machine learning model training: Curated, cleaned, and prepared data to help developers train AI and large language models (LLMs) 

• Recommendation applications: Custom applications that offer tailored suggestions to users based on their behavior

The shift from DAAP theory to practical data products mirrors the software industry’s move from monolithic apps to modular services, where each service is designed for a specific need. In the same way, data products are intentionally built with a clear user purpose, ensuring they can be reused, trusted, and easily integrated into larger workflows. We’re moving from centralized data control to distributed, product-led design. 

Why data products matter in a data mesh architecture

To fully appreciate why data as a product is central to modern data strategy, it’s important to understand its critical role in data mesh architecture.

Data mesh is a decentralized approach to data management that empowers domain teams to own and serve data as a product. It prioritizes domain-oriented ownership, self-serve infrastructure, federated governance, and most crucially, data as a product.

In traditional data architectures, centralized teams serve data to the business, which can sometimes lead to bottlenecks. The data mesh flips this model. Individual business units manage their own data domains and are accountable for the quality, usability, and availability of their data products. This approach enables self-service access to governed data products, backed by federated data governance and enforceable access controls.

This accountability is possible only when a business treats data like a product. How can your organization do that?

Launching a data product approach: From theory to execution

Adopting a data-as-a-product mindset requires more than renaming datasets. It demands a cultural shift, a disciplined methodology, and a platform to support lifecycle management. Moving from theory to action, it’s best to launch data products using a structured, iterative approach:

Step 1: Identify business use cases

Prioritize initiatives where poor data access hinders user experience or slows decision-making.  

Start with a strategic problem, not the data. For example, you focus first on improving cross-platform search metrics to avoid siloed datasets and inconsistent definitions. Data teams, including scientists and engineers, can then conduct discovery sprints to define what users actually need before designing a solution.

Step 2: Design the data product

Once you validate a use case, teams specify the datasets, sources, definitions, and outputs needed. This process includes:

• Defining schema and KPIs

• Outlining user access pathways

• Aligning on success metrics

Designing with the consumer in mind ensures the end product will be both technically robust and business relevant.

You can also use AI tools, like Alation’s Data Products Builder Agent, shown above. You’ll get sound recommendations for valuable data products, complete with the data and assets you’ll need.

Step 3: Engineer and govern the product

Data engineers and analysts build pipelines, integrate metadata, and ensure the product meets quality, privacy, and compliance standards. Tools like Alation’s data intelligence platform simplify this by:

• Mapping data lineage

• Enforcing data policies

• Centralizing documentation and user feedback

High-quality visualization and interface design ensure that even non-technical data consumers can extract insights in real time.

Step 4: Deliver through a marketplace

Data products gain traction when they’re discoverable and accessible. As illustrated below, users can browse products by function, owner, or domain in a data marketplace—just like browsing an app store. Governance guardrails are baked into access controls, ensuring appropriate usage without compromising agility.

Step 5: Monitor, learn, and improve

Like software, data products require maintenance. Monitor adoption, gather user feedback, and iterate on improvements. Embed data-as-a-product thinking into delivery workflows to ensure each shipped product is valuable, trusted, and sustainable. You can also hire a data product manager to keep everything on track for high-quality delivery and maintenance.

Developing a robust data-as-a-product discipline is a significant undertaking, but you can easily accomplish it by starting small and scaling intelligently over time. Following a data product operating model helps organizations structure and launch a pilot data product initiative by focusing on communication, collaboration, quality, and accountability. It’s important to get business stakeholders involved early to set expectations, define key metrics, and examine the value of the data product. As the data product team gains momentum, you can scale and continuously improve the effort.

Creating a data product operating model

Let’s extend the furniture analogy to consider how an organization would sustainably create and deliver data products methodically. A furniture maker turns its manufacturing capabilities into a sustainable business, just as an organization would deploy a data product operating model:

The primary characteristics of a data product are that it is: 

1. Accessible, potentially from a data product marketplace

2. Reusable to serve multiple users and use cases

3. Owned by a person or team responsible for its quality, relevance, and maintenance

Bringing all of those elements together requires a data product operating model to make the creation and delivery of data products a formalized, repeatable, and value-driving process. A best-practices approach focuses on three key elements of a data product operating model:

1. Align people, process, and technology

Support automation and lifecycle tracking through a centralized data catalog that makes it easy to build data products and iterate continuously.

Cross-functional collaboration is key. Data product teams should include:

• Product managers to define scope and value

• Engineers to build pipelines and ensure scalability

• Governance leads to maintain trust, compliance, and documentation

• Business stakeholders to define the metrics that matter

A shared process ensures consistency, and a common platform, like Alation, ensures interoperability.

2. Embrace a producer-consumer framework

Just like product teams build software for users, data product teams must build for consumers. That means:

• Clear service-level definitions: Every data product should come with agreed-upon standards for freshness, availability, and accuracy. These SLAs ensure that consumers know what to expect and can trust the data for decision-making.

• Documented inputs and outputs: Producers need to specify exactly which data sources feed the product and what the end outputs look like (e.g., tables, APIs, dashboards). This transparency helps consumers understand dependencies and use the product correctly.

• Defined ownership and accountability: Each data product should have a named owner (or team) responsible for its maintenance, governance, and lifecycle management. This approach prevents “orphaned” products and ensures accountability when issues arise.

• Feedback loops for continuous improvement: Like software, data products should evolve. Building structured channels for consumer feedback—whether ratings, comments, or direct communication—helps producers refine products to stay aligned with business needs.

3. Start small, scale smart

Each new data product initiative should align with measurable KPIs and specific business objectives.

Don’t try to transform everything at once. Validate value first by starting with one domain, one high-value use case, and one product team. Once you can prove impact, you can expand and gain momentum quickly thereafter. Within months, adoption can be scaled across departments—from editorial to engineering—with each new data product leveraging the foundations built by the previous one.

Data as a product pitfalls: What to avoid

While the data-as-a-product approach unlocks real value, missteps are common. Here are the biggest traps that organizations fall into:

• Treating it as a one-off initiative: Some teams build one-off dashboards or datasets and call them data products. But true data products require lifecycle management, governance, and continuous improvement.

• Ignoring the end user: A data product that doesn’t meet a clear business need will not be used. User-centric design must be baked into development. A lack of intuitive interfaces or real-time data access can alienate stakeholders and reduce adoption.

• Scaling too early: Trying to implement DAAP principles across the enterprise all at once leads to chaos. Start with a few strategic wins, then scale. Premature scaling often leads to duplicated data sources, fragmented governance, and organizational silos.

• No clear ownership: Without defined data product owners, accountability slips, and quality suffers. Data products must be owned like any other product.

• Misaligned tooling: Over-engineered platforms or fragmented toolchains often derail implementation. Unified solutions like Alation help enforce consistency and accelerate adoption.

While it’s important to be aware of these pitfalls, it’s also important to understand the keys to success when implementing a DP approach. 

What are the hallmarks of a successful data product approach?

What does a solid approach look like? The most effective data product strategies share these traits:

• Defined ownership: Ensure that each data product has a named steward.

• Business alignment: Your data products should each solve a meaningful business challenge.

• Discoverability: Data should be easy to find, understand, and access via your data products.

• Built-in governance: Policies, privacy, and lineage should be transparent.

• Collaboration: Ideally, stakeholders contribute at each stage of the data product creation process to optimize workflows and align them with specific needs. 

• Scalable platform support: Tools like Alation support metadata management, access control, and AI-powered discovery.

Successful data products include real-time KPI dashboards, AI-ready datasets, and domain-specific APIs integrated across the data stack. These attributes transform a theoretical DAAP model into a living, breathing operating framework.

Start treating data as a product

The path from DAAP to real data products is clear—what matters now is execution.

We’ve moved beyond the age of ad hoc data pipelines. Today’s business environment demands scalable, trusted, and purpose-driven data.

By embracing data-as-a-product ideology, organizations lay the foundation for modern data culture. But it’s not just about philosophy—it’s about execution. With the right people, operating models, and tools like Alation, businesses can unlock the full potential of their data assets. To be among them, download our whitepaper covering the 10 most important attributes for product development, business value, and sustainable data product management.