What Is a Data Product Manager?

The explosion of data has reshaped how organizations operate. With data now a critical strategic asset, companies are moving away from traditional data management approaches and embracing data products—structured, reusable, and high-quality data assets designed for specific business purposes.

This shift has introduced new roles, including the data product manager. Much like a traditional product manager, a data product manager ensures that data products are designed, built, and maintained to deliver real business value to a range of data consumers across the enterprise.

Key takeaways

• Data is a strategic resource, but organizations struggle to make it accessible, trusted, and reusable at scale.

• Treating data as a product improves its usability, governance, and business impact while easing discoverability and access

• Data product managers play a crucial role by aligning business needs with data strategy, ensuring data products are well-designed and effectively managed.

• Reusability is key to scalable data product strategies, reducing redundancy, improving efficiency, and enabling cross-functional teams to leverage trusted data assets.

• A data product operating model aligns people, processes, and technology for the efficient creation of high-quality data products.

• Data marketplaces provide a familiar resource for discovering, understanding, and selecting data products to meet data consumers’ needs. A key outcome of a highly effective marketplace is its ability to facilitate the evolution of data products—products live, iterate, and sometimes retire in response to consumer demand, ensuring relevance and value over time.

What is a data product?

A data product is a curated, high-quality data asset (or collection of assets) that is discoverable, reusable, governed, and designed for clear business value. It enables data consumers—whether humans, applications, or AI models—to confidently access and trust the data. Unlike traditional data assets, which often exist in silos, data products are built for reusability, allowing them to serve multiple business functions.

The vast majority of modern organizations understand the value of data. A study by S&P Global found that 96% of organizations say data utilization is highly important in decision-making. Those who actually follow through on data-driven decision-making are three times more likely to realize “significant improvements” than their peers. Where it used to be common to treat data as a byproduct of operations, leaders now view data as an asset for generating value. 

The challenge is getting data to those who need it. The notion of treating data as a product, where users can request, choose, and use data easily, has become commonplace. Gartner found that 50% of organizations have already deployed data products, and another 29% are considering it.

Common examples of data products include:

• AI training datasets – Curated data designed for machine learning and AI applications.

• Recommendation engines – Algorithms leveraging structured data to personalize user experiences.

• Financial risk models – Automated credit risk assessments based on predefined datasets.

• Operational dashboards – Pre-built, regularly updated insights that enable data-driven decisions.

• Customer 360 views – Aggregated customer data providing a unified, actionable profile.

Like a physical product, data products have a consumer and a producer. Data consumers in the business need data-driven insights to make decisions. Data producers are those who design, develop, and manage data products. One key role on the data-producing team is that of the data product manager.

What is a data product manager?

A data product manager (DPM) bridges the gap between business needs and data capabilities. They ensure that data products are valuable, usable, and well-governed, facilitating cross-functional collaboration among data engineers, business leaders, and data consumers, while continuously maintaining data products as the data or business objectives change. 

Key responsibilities of a data product manager:

• Understanding Business Needs – Identifying data use cases that deliver measurable business value.

• Ensuring Data Reusability – Designing scalable data products that multiple teams can leverage.

• Managing Data Governance – Enforcing policies around data quality, access, and security.

• Driving Adoption – Promoting data product use through education, documentation, and marketplace visibility.

• Aligning with AI & ML Needs – Ensuring data products are structured to support AI-driven initiatives.

• Enabling Data Mesh at Scale – Empowering business units to own and manage the full lifecycle of their data products, decentralizing data ownership and enabling a scalable, federated data architecture.

DPMs serve as authorities on data, selecting the right strategies, governance frameworks, and deployment models to ensure data products meet the needs of diverse data consumers. Importantly, this is not just a role within data teams—it is a critical function that enables data mesh, allowing business units to take full ownership of their data products in ways that centralized data teams never could.

Beyond governance and strategy, DPMs must stay ahead of evolving data product technologies and trends, understanding how advancements in AI, machine learning, large language models (LLMs), and other innovations drive the need for new data products and influence their development. As organizations embrace AI-driven transformation, DPMs play a crucial role in ensuring that data remains trusted, accessible, and usable at scale.

Data product managers vs. traditional product managers

A traditional product manager oversees physical or digital products, ensuring they meet customer needs. A data product manager, on the other hand, focuses on making data itself consumable, valuable, and reusable. Here’s how the roles compare:

• A product manager must understand what it takes to create and sell a particular product, including the needs of consumers, materials science, production manufacturing methods, packaging, delivery, costs, and more. 

• A data product manager must understand similar aspects to get a viable data product into the hands of data consumers. These requirements include the goals of the data consumer, attributes of the necessary data, enabling technologies, user interfaces for delivering the data, resources required to develop and deliver the product, and more.

One critical parallel between the two roles is their respective partnerships. Just as a traditional PM is paired with an engineering manager, a data PM is paired with a data steward. This pairing reflects a familiar dynamic: the PM is the visionary, driving business goals and innovation, while the engineering manager—or in the case of data, the data steward—grounds decisions in practical constraints and governance requirements. This ensures that while data products are innovative and valuable, they also adhere to quality and compliance standards.

Here’s a table comparing the two roles:

For a data product manager, data can be considered the materials and the data product as the insights. Data product management is distinct from data management, where the latter focuses on ensuring data integrity.

Key skills for a data product manager

Successful DPMs possess a combination of technical expertise, business acumen, and communication skills. The most critical areas include:

• Data analysis & interpretation – Understanding data quality, trends, and business insights.

• Data engineering & machine learning awareness – Knowledge of data pipelines, ML models, and automation.

• Business alignment – Ensuring data products directly support business objectives.

• Communication & collaboration – Working across teams to define, develop, and deploy data products.

• Governance & compliance – Managing security, privacy, and regulatory requirements for data products.

It’s helpful for data product managers to have experience in the following fields: 

• Software development and back-end engineering to understand the constraints, potential, and requirements for building specific software products. 

• Traditional product management to understand the product life cycle, how products are scoped and designed, where market-requirements documents (MRD) and product-requirements documents (PRD) can help, and how users will eventually interact with a particular product. 

• Data analysis, data governance, data science, and other data-related roles to effectively create data products that are trusted, capable, and can fulfill the needs of data consumers.

Additionally, core skills for any product manager, including a data product manager, include proficiency in problem-solving and analytical thinking, project management, business acumen, and writing and communication.

Why reusability is a key attribute of data products

Reusability is a core principle of data products—it ensures that once a data product is created, it can be used across multiple applications, reducing duplicate efforts, inconsistent data interpretations, and unnecessary costs. Without a reusability-first approach, organizations face significant challenges:

• Redundant data workflows – Different teams create similar datasets repeatedly, wasting time and resources.

• Data silos and inconsistencies – Lack of standardization leads to conflicting versions of the same dataset.

• Slow time-to-value – Data consumers must manually clean and reconcile datasets before they can be used.

• Increased governance risks – Uncontrolled duplication makes it harder to enforce compliance and security policies.

A well-designed data product operating model helps mitigate these challenges, ensuring that data products remain scalable, discoverable, and maintainable across the enterprise.

Utilizing a data product operating model

As data products have become more crucial to business success, the practice has become more formalized, studied, and researched. Organizations with a mature data product management model typically have the following foundational characteristics for a successful data product:

• It’s easily accessible from a data marketplace.

• It’s reusable and can serve multiple consumers and functions.

• It’s owned by a dedicated data product manager who is responsible for its quality and delivery.

To facilitate the creation and use of data products, it’s common to use a data product operating model to align the necessary people, processes, and technologies. People include the data product manager, quality assurance engineers, enterprise architects, data governance, data consumers, and others. These teams collaborate to create and deliver data products using viable technologies. 

With data teams aligned, the next step is to make the data products available to data consumers. This is typically done through a data products marketplace, similar to how consumers purchase products online through Amazon. Data consumers can browse, research, and select data products through the marketplace and then quickly put those products to use.

Creating data-driven success with data product managers

The constantly increasing demand for data and the ever-evolving world of technology have shifted data management to a more product-focused approach. The sheer volume of data and diversity of use cases demand sophisticated data solutions, which are further pushed and accelerated through AI innovations that demand more data and strain data governance, management, and quality expectations.

Organizations now view data products as valuable assets to be harnessed to advance data-driven decision-making and improve business performance in all areas. These data products, which range from simple dashboards to complex AI-powered recommendation engines, are intended to be reusable, accessible, and governed easily and effectively. It’s a fundamental shift in how data is used and managed, but it can drive better decisions and outcomes.

Ultimately, the key to unlocking data’s potential is with the data product manager. This role ensures the quality, accessibility, and strategic alignment of data products as organizations put them to use. However, if they leverage a data product operating model supported by a data marketplace and have combined business and technical expertise, data product managers can be a crucial component of overall organizational success.

Curious to learn how a data catalog can help you create and manage data products? Book a demo with us today.