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

Published on February 19, 2025

Data’s value is unarguably immense. But many organizations struggle to effectively utilize their vast data resources because traditional approaches to data access and usage can’t keep up with exploding data volumes and variety of sources. This post explores two key concepts—data products and data as a product—to uncover how organizations can unlock data’s true potential.

Key takeaways

Relying on ad-hoc data requests creates bottlenecks and doesn’t alleviate the challenges of more and more data volumes.
Data products are purpose-built, accessible, governed, and reusable data-driven solutions designed to address specific business needs.
Data as a product creates a strategic, factory-like approach to designing, developing, delivering, and improving data products at scale.
Data products enable workers to access data up to 90% faster and 30% cheaper than traditional methods.
Adopting a data-as-a-product approach marks a strategic shift in how organizations use data for informed decision-making and innovation.

The traditional approach to data is broken

According to IDC research, 83% of CEOs want their organization to be more data driven. Unfortunately, 89% of their subordinates say too much data limits success. This gap illustrates the challenges in how data is commonly accessed and used.

Traditionally, when data is needed to complete a task, a data consumer defines their need and may even target the appropriate data sources. They likely work with a data scientist or data analyst to access the data and build a dataset that meets their needs. For this single data need, these cross-functional teams may work across multiple technologies, access data from multiple sources, capture a snapshot of the data, and deliver it to the data consumer.

That data snapshot is instantly outdated. When a refresh is required, the team is forced to run the process again. When a new data need arises, the process restarts in a cycle of repeated manual work across a complex data architecture by expensive talent.

Simply providing access to more data isn’t the solution. It’s clear that data holds immense value, but finding that value in response to a particular business question requires a new approach to how data is found, accessed, and put to work.

What is a data product?

The concept of offering workers data products—where workers can request, choose, and use data easily—has become increasingly common. Just as you would search for, research, and buy any product, a data product is created for a specific purpose and has a method of consumption.

If you think of data as lumber, a data product is a wooden chair that’s sold in furniture stores. It’s easy to visit a store, browse the chair aisle, and purchase a chair. The assembled chair has added value because it’s more comfortable to sit on than a pile of lumber. And the furniture maker has the skills to craft a well-made, aesthetically-pleasing chair.

IDC defines data products similarly as accessible, having business value, and having accountability.

Accessibility makes data products available, discoverable, and reusable. Think of this as the furniture store in the analogy above.
Business value places importance and relevance on data. A nice chair is worth more than raw lumber and a pile of nails.
Accountability assigns responsibility for developing, maintaining, monetizing, and nurturing a data product. The chair manufacturer is responsible for the quality of the chair.

Data products are purpose-built solutions to questions, needs, and business tasks. These needs can be as diverse as informational dashboards, financial reports, predictive forecasts, and AI model training datasets.

Examples of data products

Data products enable more workers to take advantage of more data in less time, with less training, and with higher accuracy. Researchers at McKinsey say companies using data products can put data into the hands of workers for specific use cases 90% faster while cutting the cost to do so by 30%. With benefits like that, data products are obviously becoming more popular.

Common examples of data products include:

Business reporting: Many workers need business insights regularly, yet business data requires governance to manage access, privacy, security, and other considerations. Reporting data products can be offered in the form of dashboards, charts, and summaries, with access automatically controlled via a data marketplace.
AI and machine learning model training: AI requires massive amounts of data to train models. Large datasets containing curated, cleaned, and prepared data for training AI and large language models (LLMs) can be available to developers as a data product for fast, governed access to appropriate datasets.
Recommendation applications: Similarly to how Amazon provides instant product recommendations based on shopper behaviors, predictive upsell and cross-sell suggestions are used by sales representatives, customer support agents, and others. Business teams with access to tailored recommendation engines can build custom applications such as suggesting which marketing assets to recommend to prospective customers or offering field service teams suggestions on which replacement parts to stock when visiting a particular customer.

Best practices for defining and building data products follow common product development lifecycle models, where teams specify, design, develop, release, update, and maintain products. Using data products makes the potential applications virtually limitless.

What is data as a product?

Where the above analogy compares a data product to a chair, the concept of data as a product is a higher-level approach that equips an organization to create data products at scale, analogous to a furniture factory.

A chair fits a limited application: enabling someone to sit. A furniture factory can fill an entire furniture store. It sources many materials from many different suppliers, has different sets of skills across its workforce, uses various machinery, and produces items as diverse as chairs, tables, upholstered sofas, china cabinets, and bed frames.

Fundamentally, the difference is that data as a product is the process through which data products are created and delivered.

Facilitating a data-as-a-product approach requires a data product operating model to align people, processes, and technologies, introduce a producer/consumer model for building and offering secure and trustworthy data products, and enable the approach to scale across data architectures and business needs. Treating data as a product enables more use and reuse of data, allows workers of all skill levels to leverage data, and serves data to workers in a usable form so organizations can realize data’s full value.

Launching a data-as-a-product approach

A data-as-a-product approach is similar to any product development process. It requires data product managers to define products that meet business needs, data analysts, data scientists, data architects, and data engineers to build the data products, and software developers and user experience designers to ensure the data products are usable.

A typical data-as-a-product cycle follows these steps:

Identify a business need, understand the use case, and conceptualize the solution.
Design a solution comprising required datasets and data sources, and define the functionality and interface of the eventual data product solution.
Engineer the technical infrastructure and capabilities of the data product using the required data in the right format and appropriate data quality levels.
Enable delivery by defining responsibilities and ownership of the data product, creating instructions and documentation, and ensuring data governance with security, privacy, and access controls.
Enable access through a data marketplace, create necessary training programs, and add collaboration tools to capture user feedback and improve subsequent product enhancements.
Monitor data product usage to understand performance and if the data product meets the needs of the data consumers.
Continuously improve the data product with updates, enhancements, and new features.

Developing a robust data-as-a-product discipline is a significant undertaking, but can be easily accomplished 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, the effort can be scaled and continuously improved.

Creating a data product operating model

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

	Furniture 🪑	Data Products 📊
*Raw materials*	Lumber	Data
*Finished product*	Chair	Data product
*Process*	Factory assemblyline	Data-as-a-product approach
*Sustainability*	Go-to-market methodology	Data product operating model

The primary characteristics of a data product are that it is accessible (potentially from a data product marketplace), reusable to serve multiple users and use cases, and owned by a person or team responsible for the data product’s 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:

Align people, processes, and technology to validate use cases as valuable, design and develop a data product that fits the need, ensure the data product is effectively governed, and technical and data expertise to engineer, architect, and ensure the ongoing quality of the data product.
Implement a producer/consumer model so it is easy for users to discover and use data products within accepted frameworks for value, ownership, trust, design, security, interoperability, AI readiness, and more.
Start small and scale smartly with a team of data product owners and engineers who can test and refine processes, establish accountability and key metrics, and launch a marketing campaign to build awareness, all with the intention of creating a model for how the organization collaborates to build and deliver data products.

Data products are crucial to the data mesh architecture

Terms like data fabric and data mesh appear quite frequently when examining data products and data as a product. For simplicity, a data fabric aims to ease data access, storage, and management through centralization, while a data mesh uses a decentralized approach to place responsibilities on those closest to the data.

The data mesh architecture has four principles:

Domain-oriented decentralized data ownership and architecture
Data as a product
Federated computational governance
Self-serve data infrastructure as a platform

With data as a product being one of those principles, organizations deploying a data mesh approach will, obviously, also deploy a data-as-a-product approach.

Start treating data as a product

The distinction between data products and data as a product is the same as that of a product and a process.

Data products are tangible outputs designed to address specific business needs like AI model training or financial reports. They offer real value to organizations by streamlining data access and usability, improving data-driven efforts, and reducing the cost of data use.
Data as a product, conversely, represents a strategic organizational shift in mindset and infrastructure. It establishes a systematic approach to data access, delivery, management, and reusability for the efficiency and scalable use of data products.

Both concepts depart from traditional, reactive data usage to a more proactive, user-centric model for data value extraction.

Embracing data as a product is crucial for unlocking the full value of organizational data. Data products provide immediate solutions while a data-as-a-product approach creates a sustainable, adaptable data culture. Focusing on data as a valuable asset to productize empowers workers, drives innovation, and creates a truly data-driven organization capable of exceeding CEOs’ data aspirations.

Curious to learn how a data catalog can help you deliver data products at scale? Book a demo with us today.