Data Product

A data product is a curated data asset that is discoverable, reusable, governed, and designed to generate business value, making it easy for data consumers (workers, applications, AI models, etc.) to find, trust, and leverage appropriate data.

What is a data product?

Data products are just that: products made of data designed to fill specific business needs. Just as you easily search for, find, and consume a product from any retailer, data products make it easy for those people or applications that need data to find and consume it.

Data products solve a discrete problem, answer a question, fill a business need, or enable task completion. Think of a business dashboard that shows up-to-the-minute sales data. That’s a data product. Users can bypass searching for, gathering, formatting, analyzing, and turning data into an actionable graph. Instead, the data product has already been defined, built, and made available.

In addition to dashboards, examples of data products can include reports, forecasts, training data for AI models, algorithms, and data APIs.

Key attributes of data products

Successful data products share a set of core attributes that ensure they deliver business value while remaining discoverable, governed, and reusable. These attributes serve as guiding principles for designing and managing data products effectively. Below are the key attributes that define a well-structured data product:

Value First: Data products should be designed with a clear business purpose, whether it is to optimize decision-making, improve operational efficiency, or enable AI and analytics. The value of a data product should be measurable and aligned with organizational goals.
Discoverable: A data product is only useful if it can be easily found. Leveraging metadata, search functionality, and data catalogs ensures that data consumers can locate and understand the data products available to them.
Clear Ownership: Each data product should have a defined owner or steward responsible for its maintenance, quality, and lifecycle. Clear ownership prevents duplication and ensures accountability.
Well-Explained: Data products must be documented with clear metadata, business definitions, and usage instructions. This ensures that consumers can interpret and leverage them effectively without ambiguity.
Globally Unique: To avoid conflicts and confusion, data products should have a unique identifier that differentiates them from other assets in the organization’s data ecosystem.
Trustworthy: Consumers need confidence in the quality, accuracy, and reliability of a data product. Governance policies, validation mechanisms, and data lineage tracking help establish trust.
Accessible: Data products must be readily available to authorized users while enforcing security controls to protect sensitive information. Role-based access and permissions help balance accessibility with governance.
Modular and Reusable: Designing data products with modularity in mind allows them to be repurposed for multiple use cases, reducing duplication and increasing efficiency across the organization.
Composable and Interoperable: Data products should be designed to integrate with other data assets and systems, ensuring they can be combined, extended, or reused in various workflows and applications.
Secure: Security and compliance should be embedded into the data product lifecycle, ensuring that data is protected against unauthorized access and that regulatory requirements are met.

By adhering to these attributes, organizations can create scalable, high-impact data products that drive business value, enhance data governance, and enable AI and analytics at scale.

Benefits of data products

Data products offer many benefits beyond just easing access to needed data. Additional benefits include:

Data products accelerate the delivery of required data to data consumers, speeding up implementation times by as much as 90%.
Data products lower the total cost of ownership for the technology, development, and maintenance of data assets by up to 30%.
Processes for developing data products consider access, security, application, and other concerns during development to minimize risks and improve data governance.

Data products align data efforts with business objectives by providing a structured, reusable approach to data usage. Rather than having data consumers ask a data scientist to answer a pinpoint question, stakeholders can work collaboratively with a data product manager to define the business goal and build a reusable data product that saves time and effort for everyone involved.

Additionally, data products reduce duplication of effort and promote better data governance through a coordinated process that can identify efficiencies and shared product opportunities during data product development.

How data products work

Data products must be designed, built, and offered for use. Overseeing that process is typically a data product manager who understands the business need and available data and translates it into a finished data product.

Data product managers work to address the business need while also considering the data product’s reusability to maximize value, governance to enforce quality, access, and security policies, promotion to ensure others can find and use the product via a data products marketplace, and scalability to support future needs like AI training and the data product’s lifecycle of maintenance, updates, and retirement.

What is a data product operating model?

Supporting data products and data product managers is a data product operating model, which can either be centralized to consolidate data expertise and delivery or decentralized to bring data expertise together with business knowledge at the point of need.

A data product operating model aligns people, processes, and technology to create a familiar producer/consumer model that offers data products in a data product marketplace and enables organizations to start small and scale effectively. It focuses on collaboration between data product managers who understand the need and data engineers who can build the data products.

Agile development methods have become more prevalent in data product operating models to improve efficiency, accelerate delivery, and streamline processes. And, as business applications and AI innovations need more and more data, data products increasingly rely on metadata to provide context that improves AI outcomes.

Data products vs. data as a product

Data products are reports, AI training datasets, recommendation algorithms, and other data assets offered and consumed through data products marketplaces. Data as a product is a higher-level concept organizations follow to create data products at scale.

Consider data products as purchased products, such as a chair. Data as a product is the repeatable methodology that guides the production of chairs in a furniture factory.

Common pitfalls to avoid when implementing data products

As with any change in approach, implementing a data product operating model and shifting to a data-as-a-product mindset can cause some issues along the way. Common challenges when deploying data products include:

Building data products ad hoc without a data-as-a-product framework. A structured methodology enables efficiency and scalability when building data products.
Not considering the change management involved with deploying data products. Getting leaders involved, aligning with corporate goals, and generating excitement for data products is crucial to success.
Attempting to deploy data products without a discovery tool like a data marketplace. If data consumers can’t find data products quickly and confidently, they won’t use the data products.
Repurposing legacy data assets instead of building modern data products. Understanding legacy data assets can take more effort than building new data products from scratch.
Deploying data products without considering their value. Investments in a data-as-a-product approach require a return on investment, which is driven by usage and outcomes that are defined by metrics and success stories.

Data products are a new concept for many organizations, requiring a focused effort and change management. However, starting with a data product operating model will help unlock the power of data products.

Alation eases data product efforts

Business users need fast, trusted access to data to drive smarter decisions, AI applications, and business processes. Alation’s Data Products Marketplace enables organizations to publish, discover, and use high-quality, reusable data products—ensuring teams, systems, and AI agents have the reliable data they need.

Key capabilities include:

A Centralized Marketplace for Trusted Data Products – The Marketplace transforms data management into a systematic, repeatable framework. Business users can shop for ready-to-use, governed data products—eliminating the complexity of searching through raw datasets.
Streamlined Data Product Discovery & Governance – Data producers can track usage, improve discoverability, and ensure compliance through built-in governance features, fostering trust and reducing risk.
Scalable Data-as-a-Product Approach – Alation helps organizations scale the data product operating model, accelerating delivery, minimizing duplication, and ensuring that data products support AI, analytics, and automation initiatives.

With Alation’s Data Products Marketplace, businesses no longer have to choose between speed and trust. By providing a governed, easy-to-navigate platform for data products, Alation enables organizations to unlock the full value of their data, accelerate decision-making, and drive AI success at scale.

Next steps: learn more about data products

Discover how data products can help organizations gain agility, move faster, and become more data-driven with the following resources:

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