As our lives become increasingly digital, companies are adapting to harness digital activity and turn it into revenue. The goal is not just data collection from prospects and customers, but translating that data into actionable insights that will transform their business.
To do this, they need someone well-versed in both data and product development. Enter: the data product manager.
Data product managers can help businesses leverage data at scale. They might use this data to personalize user experiences, recommend relevant products, increase retention, and identify cross-sell and upsell opportunities. They might also spearhead new product development that uses data to serve customers (think: Amazon’s Alexa or Spotify’s recommended playlists).
What is data product management?
Data product management is a function that collects, organizes, stores, and shares data within an organization. This data is used to inform product and business decisions.
In many product organizations, individual product managers are responsible for collecting and synthesizing insights related to their product. While this remains part of a PM’s role, a data product management function allows an organization to centralize the collection, storage, and use of data.
This reduces inconsistencies in how data is tracked and reported on—and allows organizations to derive insights at scale. This centralization of data allows your typical product manager to focus more on strategizing and working with their team to bring new features and products to market. It also helps them feel confident that the data they’re referencing is accurate and complete.
What is a data product manager?
A data product manager is primarily responsible for finding ways to use data throughout the product lifecycle. However, much like a traditional product management role, the responsibilities of a product data manager can differ from company to company.
The data product manager (DPM or data PM, for short) may be skilled in machine learning, data science, or data programming languages—and so their role may largely leverage those skills as part of product development.
In other organizations, the DPM may serve as a dedicated data resource for the product team. Rather than owning a product, they may own the data that informs other product decisions. In this case, they support the product team in understanding what’s happening in the product so they can fix problems or build new solutions.
Like other product managers, data product managers must be skilled in communicating across teams. They must be able to facilitate conversations with executives, engineers, analysts, product managers, and external customers. Because of the diverse groups of people the DPM connects with, they must be adept at context-switching and communicating both high-level concepts and technical details.
Why does a product team need a data product manager?
Product teams need a data product manager to centralize product data across teams and turn insights into action. While every product manager needs to know their way around data and be comfortable with it, it’s not the core responsibility of your typical PM to own data collection and analysis. Data product managers spend a lot more time thinking about the best ways to gather, analyze, and distribute data to inform product decisions.
If you think about your average product manager, their job description includes writing stories and epics, planning roadmaps, meeting with stakeholders across the organization, speaking with customers, and validating work.
Any product manager would tell you: that’s a lot of work for one person. Add to that finding ways to track events in-product, connect disparate data, and report out on product and feature adoption, and you can see why having a dedicated data resource is invaluable to a product team.
Beyond simply tracking and reporting on product data, DPMs often have deeper knowledge of things like artificial intelligence (AI) and machine learning (ML). These skills allow them to process larger quantities of data faster—driving more valuable insights for the team as a whole. For organizations with a large volume of data, a big data product manager can help process more complex data sets to help you address business problems you wouldn’t have been able to tackle before.
5 main responsibilities of data product managers
Data product manager job descriptions vary depending on a company’s needs. Here are some of the types of data product management work a DPM would typically be responsible for.
1. Lead a cross-functional team.
Product managers are a lot like conductors in an orchestra. They have to know who’s involved and at what time to successfully develop a product. Data product managers work closely with data scientists, data engineers, and architecture leads, as well as partners in marketing and design.
2. Oversee and report on the data management lifecycle.
A large part of the DPM’s role is collecting, storing, organizing, and analyzing data. The ideal candidate for a data product management role is someone who’s confident wrangling large data sets, writing queries, and communicating out their findings.
3. Leverage advanced technical skills in product development.
Most data product managers are proficient in programming languages like SQL and Python. And many have advanced skills in machine learning and big data management.
4. Create and maintain a product roadmap.
Like your average product manager, a DPM needs to develop a product roadmap. This roadmap will include innovative ways to leverage real-time data to create successful products and features.
5. Understand customer needs.
Just because a data product manager is tech-savvy doesn’t mean they’re disconnected from consumers. DPMs stay close to the voice of the customer to understand their needs—so they can identify ways they can use data to solve those problems.
What is the difference between a product manager and a big data product manager?
The biggest difference between a product manager and a big data product manager is their understanding of data. Product managers need to have an understanding of key business metrics and KPIs related to their product, and they often partner with a data analyst to create dashboards that help them track these metrics.
Big data product managers do a deep-dive into data on a day-to-day basis. They streamline data collection and analysis in a way that allows them to gather real-time insights.
Another difference is that big data product managers are actively building ways to collect and manage data. So they’re not just using data to build better products, they’re responsible for capturing and organizing all that data.
How does a data product manager benefit the product development cycle?
There are several ways a data product manager benefits the product development cycle. Here are a few of the top reasons:
Product teams have more visibility into product data.
Product management data isn’t always readily available. Depending on how a product organization functions, they may require the support of a data analyst, product operations manager, or business intelligence team to tag and report on product events.
Data is no longer siloed with analysts.
With data management in the hands of another team, it becomes more difficult for product managers to have ownership of and visibility into insights that can help them build better products. A data product manager puts data into the hands of the people who will use it as part of their decision-making process.
Data is clean and current.
When data is owned by different teams around the company, the likelihood that data is unclean and out of date increases. Many organizations struggle to accurately report on product metrics because there’s no clear ownership of how events are tracked, and the information product managers need is found in different softwares. This requires a lot of manual reporting, which consumes time and often gets put on the back burner.
Product teams can create more sophisticated, data-driven products.
When product managers have actionable data in hand, they can build products that are more useful to users. Take, for example, navigation apps. When navigation apps first launched, they plotted routes from Point A to Point B. That’s it. Now consider Waze, a crowd-source navigation app that gathers real-time data from drivers about traffic, accidents, police cars, and more. Waze uses this data to provide users more accurate information on travel time and the best route to take to avoid delays.
You might have a few questions following that data product management summary. And we’re happy to answer them! Here are some frequently asked questions about the role of a data product manager.
Why is data important in product management?
Data plays a critical role in product development. Without data, product managers can’t make informed decisions about what to build, fix, or sunset. Most product teams have a long backlog, a list of product features they’d like to work on someday. Data helps with opportunity prioritization so they know what to work on first, second, and third—as well as what to scrap altogether.
Data comes in many forms, both quantitative and qualitative. This can include customer feedback, support tickets, NPS, industry trends, and user activity. Each of these data sets provides insight into what your customers are doing and what they find valuable. All of this data is useful in driving product decisions.
What makes a good data product manager?
A good data product manager has strong communication, leadership, problem-solving, organization, and analytical skills. They’re comfortable navigating high-level conversations with senior stakeholders, then transitioning to much more in-the-weeds discourse with developers and programmers on their team. Good data product managers are proficient in data programming languages and data extraction tools.
As product managers, DPMs are also responsible for communicating up and out about their product. This means a good DPM needs to be able to summarize and communicate about the data and their projects to stakeholders, senior leaders, and others on the product team.
How much does a data product manager make?
Data product managers make an average of $111,140 a year. Their salary can range from $71,000 and $175,000. Keep in mind that salary can vary depending on experience, education, location, and advanced skill sets. The best way to determine what you can expect is to use a site like Salary.com or Glassdoor to see what data product managers with your experience and in your location are earning.
How do you get started as a data product manager?
While there isn’t a dedicated degree program to help you launch your career as a data product manager, there are ways you can start to gain the experience and skills necessary to become a DPM. People with experience as a product manager, project manager, or business analyst could be great candidates for becoming a product data manager.
Many DPM jobs do require a degree to apply. A Bachelor’s degree in a related field, such as information management, engineering, or data analysis, would increase your chances of landing a role as a data product manager.
As many DPMs have product management experience, you may find it helpful to pursue a product management certification. Joining a professional network—like the Product Collective community or LinkedIn—can also help you connect with job opportunities.