What Is a Data Product Operating Model? A Step-by-Step Guide

The world of data management is rapidly changing, pushing organizations to rethink how they extract value from their data. A key driver of this transformation (along with AI) is the rise of data products. According to the Gartner Chief Data and Analytics Officer (CDAO) Agenda Survey 2024, 50% of organizations have already deployed data products, and another 29% are actively considering them. 

Per the Gartner Chief Data and Analytics Officer (CDAO) Agenda Survey 2024.

The promise is clear: data products unlock efficiencies, insights, and business wins. But this shift demands more than just producing data assets—it requires creating data products that drive measurable, repeatable value.

What is (and is not) a data product?

Over the past 18 to 24 months, organizations have sought a clear definition of a data product. In the past, that definition often read something like, “A data product packages data assets to solve specific business needs in a discoverable, understandable, and trustworthy way.” Common examples include datasets, dashboards, reports, and APIs.

While this definition has value, it raises debate about dashboards and reports as data products. In the visualization below, dashboards and reports consume data products to deliver insights. However, labeling them as data products misrepresents their purpose. They are tools for analysis powered by data products, not the foundational data products themselves.

The bottom layer represents the data sources used to create the data products in the middle layer. The top layer represents the data consumer applications and AI Agents.

A more precise definition of a data product is data intentionally built and managed, with a clear focus on usability and value. It is owned, accessible, and designed for use and reuse. Feedback plays a critical role in iterating and evolving the data product.

Primary characteristics of a data product:

What sets a data product apart from a simple data asset? It comes down to intentional design and key traits ensuring lasting value. Below are three foundational characteristics that define a successful data product:

• Accessible:  A data product must be easily accessible from a marketplace for data consumers to find and use.

• Reusable: A data product must be designed as a usable and reusable component that efficiently serves multiple use cases.

• Owned: Every data product must have a dedicated owner responsible for its quality, relevance, marketing, and upkeep.

The missing piece: A data product operating model

Gartner's findings show a need for formal data product management. Organizations struggle to formalize their efforts due to a lack of a data operating model.  Data and business leaders need an approach that helps them define, manage, and deliver data products that create real value by:

• Aligning people, processes, and technology 

• Implementing a producer/consumer model 

• Starting small and scaling smart 

These are the keys for any new organization and can also help fine-tune processes for those already producing data products.

Aligning people, processes, and technology

The most important aspect of a new operating model starts with the people. Organizations will need to consider changes to the structure of data teams and new roles to operationalize data products. 

The first new role is that of a data product owner. This role is very similar to a product manager role for software development, except for data. As described earlier, the data product owner is accountable to the business partners. Before the data product is built, the data product owner and business stakeholders must collaborate. Together, they validate the problems (and use cases) the data product will address. A core goal is to ensure the data product will deliver business value. 

Once the data product is validated as valuable, the owner works closely with the data engineers to ensure that the developed data product meets the business requirements. 

Once the data product goes live, the data product owner helps market it internally and improves it based on critical feedback from the data consumers (i.e., business users).

Other roles might include quality assurance engineers (or team members who assume this responsibility). The data producer team must be self-sufficient in creating high-quality, valuable data products that adhere to the organization's architecture and technology. Data quality and observability tools help automate testing to ensure the ongoing quality of the data product, which is critical to its trustworthiness once it is made available in the marketplace.

Outside the data producer team, data and enterprise architects provide essential technology guidance for data teams. The guidance can include various items, from continuous integration and continuous deployment (CI/CD) to data pipeline tools. The infrastructure must also include data observability, monitoring, and alerting tools to ensure the efficiency and quality of the data products.

The last critical item is working closely with the data governance team to ensure policies are implemented with every data product to minimize risk. The distributed data teams use a federated approach to managing and adhering to organizational data governance policies. The implementation is at the team’s discretion, but they must ensure conformance to meet data privacy and regulatory compliance.

Ultimately, the success of a new operating model hinges on building empowered, well-structured teams supported by robust technology and governance frameworks. The next step is instituting a producer/consumer model.

Implementing a producer/consumer model

Anyone with a product must find a way to make it available for consumers to find and purchase. For many years, this meant relying on brick-an​​d-mortar stores. The rise of the internet transformed this landscape, with digital marketplaces like Amazon providing an easy way for consumers to find and purchase products. Today, online shopping has become a familiar and preferred choice for many. The ease of browsing a virtual storefront like Amazon is an experience organizations try to emulate in data product marketplaces.

From a data producer perspective, making their products accessible should be simple, especially within an internal marketplace. Just as Amazon provides clear guidelines for its sellers, your organization must establish a framework for data producers to ensure a consistent and seamless experience for data consumers. Key components of a data product should include: 

• Value-first: Every data product must deliver clear business value. Defining value starts with business stakeholders and their strategic objectives, ensuring data products directly contribute to key initiatives.

• Easily accessible: Similar to e-commerce platforms (like Amazon), data consumers should be able to find relevant data products in the marketplace effortlessly. Organizing products by business domains and including precise metadata enhances discoverability.

• Clear ownership: Each data product must have a dedicated owner responsible for its quality, relevance, and upkeep. Ownership ensures accountability and prevents duplication or inconsistencies.

• Well explained:  Effective data products are understandable with clear and comprehensive descriptions of what the data product is intended for and any limitations. 

• Increase trustworthiness: Display key data quality metrics—such as accuracy, completeness, and freshness—so data consumers can assess trustworthiness. Data lineage and governance policies should also be visible for transparency.

• Globally unique:  Data products must have a unique identifier because they must be accessed from a GUI and programmatically through APIs.

• Native accessibility:  A crucial aspect of data products is ensuring that once a data consumer discovers a data product, they should be able to gain access in a streamlined manner.

• Modular design: Data products must be designed as reusable components for increased efficiency and consistency.  The intention is that the data product will serve multiple use cases.

• Composable/interoperable: Design data products to integrate with others to achieve richer insights. Standardized schemas, global identifiers, and aligned refresh cycles enhance cross-domain usability.

• Secure access: Security is paramount in data products. Ensure security and data governance controls are in place for data access and that sensitive data is classified correctly, protecting the data from any non-compliant use.

A data product marketplace can increase self-service for well-governed, valuable data products and shorten the data delivery time for data consumers, including people, business applications, and AI applications. The main thing to remember is that the data producer and consumer perspectives are equally important. 

So, how can data leaders launch impactful data products? In the next section, we’ll walk you through how to conduct a pilot project.

Starting small and scaling smart 

The people and processes are in place, and it is time to launch a data producer pilot team. The team will allow the data product owner and engineers to test and refine methods for delivering valuable data products. A pilot also gives the team time to establish communication and accountability expectations, foster collaboration, and ensure the timely delivery of quality work to its stakeholders. There must be business stakeholders to validate that the data products being created during the pilot phase meet their expectations. 

The pilot helps the data product owner establish key metrics for managing a data product portfolio. These metrics will highlight areas for improvement as the team learns and adapts. As the first data product is delivered, these metrics should be visible to the team, demonstrating how their efforts generate value for the organization.

Another crucial step is launching a marketing campaign to inform business users (data consumers) about the organization's data strategy and the availability of data products. This campaign raises awareness inside the company. In addition to marketing communications, training sessions must be held to enable the data consumers how to navigate and utilize the marketplace effectively.

The pilot data producer team can serve as a model for establishing additional teams. The new and existing teams should collaborate, iterating on successful practices to deliver business value and improving processes based on lessons learned. A culture of continuous improvement will support the scaling of efficient teams. This stage also provides a prime opportunity for the teams to establish how to work together to ensure they make composable and interoperable data products. 

Conclusion

The vision of consistently usable and reusable data products that drive business value is possible. Imagine the day when data product management isn’t a chaotic scramble but a structured, efficient process aligned with your organization's business initiatives. Efficiency soars, teams collaborate effortlessly, and your organization taps into the full potential of its data.

The way to achieve this efficiency is using Alation’s proven data product operating model. It helps organizations define, manage, and deliver data products that create real value by:

• Aligning people, processes, and technology – Follow best practices to get everyone on the same page and streamline workflows.

• Implementing a producer/consumer model – Build a framework that matches your organization's structure and needs.

• Starting small and scaling smart – Launch manageable pilots, learn, and optimize to maximize impact.

Alation provides a comprehensive solution for your data product marketplace, supported by an expert services team with deep expertise in implementing our data product operating model. We help transform fragmented data efforts into efficient, reusable, and valuable data products for the entire organization.

Contact Alation today to create a thriving data product ecosystem.

To learn more about Alation’s approach to data products, watch the Alation Brief “Data Products for a Meshy World: Deliver Business Value”