The role of a data product manager

Since 2020, data management has shifted towards a data product mindset: thinking of data development projects as versioned products with “customers,” i.e., the internal teams (engineers, data scientists, analysts, business stakeholders, etc.) that use the data.

With a proactive product development model, data teams need support prioritizing their projects. That’s where a data product manager steps in. In this article, we’ll look at what a data product manager does, their roles, and the skills they need.

What is a data product manager

Let’s begin by looking at the data product mindset for context. Using a data product mindset means thinking of data development as projects designed to serve some specific organizational purpose – identifying churn, tracking annual recurring revenue (ARR), managing inventory, etc.

Data teams autonomously look for product opportunities that deliver value and align with strategic objectives rather than chasing metrics or reacting to requests. Focusing on meaningful projects activates an organization’s data assets and technical expertise. The resulting data products become a part of a larger data mesh that becomes a backbone for the organization’s data-driven initiatives.

In this context, a data product manager is the product manager who leads data teams and directs product development initiatives to take data products through the entire data product development lifecycle. They often go by titles other than “data product manager,” such as “analytics manager,” “director of data,” etc.

What role does a data product manager play

Data product managers help set the development priorities of data teams working on products. Working in conjunction with data engineers and other technical team members, they decide which products to pursue, set the goals for those products, and manage the development process. It’s the data product manager’s job to work with their team both to determine priorities as well as to set realistic estimates for the work to be done.

An essential part of a data product manager’s role is generating product ideas by observing and communicating with the larger organization. They:

• Investigate the organization's data landscape
• Look for pain points and inefficiencies
• Communicate with business teams to understand their needs
• Brainstorm with the development team to generate ideas for potential products
• Own the vision, delivery, and roadmap of the data product
• Understand, measure, and optimize the value that the product is delivering to the business

All of these factors are triangulated with the organization’s strategic priorities to ensure that data development supports broader initiatives.

Data product managers also advocate for data development. They present a products' value to stakeholders, communicate product use cases to business teams, field development requests, and weigh new requests against other priorities. They keep the organization on the same page regarding data assets and development by acting as an interface between data teams and stakeholders.

What happens without data product managers

Without a data product manager, organizations risk under-utilizing their data assets and teams. There may be issues with:

• Project prioritization
• Data trust
• Data development focus

Project prioritization

When there isn’t a manager to field and prioritize requests, data teams are treated like IT. When stakeholders need data for a project, they file a request and a data engineer fills the ticket.

This works for a while. However, over time, data teams become completely occupied with a task queue, having little time to pursue other development avenues. This leads to repetitive work, as data teams address requests as one-off jobs instead of developing comprehensive solutions that others across the organization can leverage.

At the same time, the requests coming in may not match organizational needs. For example, a team might receive a request for a list of customers ordered by ARR. But maybe an analysis of churn risk or last contact date would better serve the initiative in question. If data teams are just reacting, they can’t leverage their expertise to support organizational strategy.

Data trust

With no data product manager, there are also issues with data trust. Stakeholders don’t have anyone presenting products to them, so they don’t understand what exists, how to access it, and what’s coming down the pipeline.

In this environment, past products sit unused while engineers repeat similar tasks. Data teams have to spend their time arguing their worth rather than providing that worth.

Opaque data operations also undermine stakeholders' trust in the data they encounter. They may feel lost when a data team provides them with an unfamiliar data product. Without a product manager providing context, users may not feel comfortable that they understand the product’s purpose or how it works. The result is underutilized assets and wasted data infrastructure.

Development focus

On the other hand, without clear leadership, data teams can lack a focused direction for their projects. From a data side, building a view summarizing revenue statistics across departments seems valuable. However, if the current organizational push is to increase user retention, that view may languish unused.

When data teams’ priorities aren’t aligned with organizational initiatives, data products fail to deliver value even if they are theoretically useful. This gives stakeholders the impression that data products have little practical impact, further undermining data trust. Data teams’ time is wasted, and initiatives struggle, lacking data products to inform decision-making.

This can also hurt data team morale. If team members feel the organization neither understands nor values their work, it can lower their motivation and engagement. This can harm data team retention - and thus slow down the organization’s overall data engineering strategy.

The value that data product managers deliver

Data product managers connect data teams and stakeholders together. They stay on top of organizational strategy and bring that information to engineers through product priorities and context. This connection keeps data development goals aligned with broader organizational goals, ensuring the work data teams do doesn’t go to waste.

On the stakeholder side, data product managers communicate new data products and ongoing development. This communication spreads awareness of what products exist and how to use them so that useful products don’t go overlooked. Data product managers also help ensure that data products are discoverable in easily-accessible tools, such as a data catalog. Seeing the value data products provide, stakeholders feel more confident approaching new and existing products, increasing data trust overall.

Data product managers also negotiate the expectations placed on data teams. Stakeholders may have various ideas and requests for data products but don’t see the existing priority stack and lack the expertise to understand the extent of a proposal’s complexity. A data product manager handles that balancing act, ensuring that data teams’ time goes towards the most impactful products and doesn’t get sidetracked by filling a ticket queue.

Skills for a data product manager

An effective data product manager needs to have:

• Technical understanding: A data product manager doesn’t need to be an expert but should have some understanding of how data development works and what tools are involved. They need this knowledge to help a team assess project work hours and weigh them against the proposed business value when prioritizing.
• Communication skills: Most of a data product manager’s work involves communication—discussing priorities with data teams, explaining products to stakeholders, and responding to requests—so strong communication skills are a must.
• Managerial skill: Since data product managers coordinate development schedules, they should have knowledge and experience of efficient project organization, team coordination, etc.

Conclusion

Data product managers connect data teams to the larger organization. They communicate with engineers and stakeholders, align development priorities with organizational strategy, and present data products to provide context. Their work maximizes the impact of data teams’ time and improves data trust across the organization.

Want more information on the data product mindset and data product management? Check out these presentations from Coalesce or read our data mesh series.