Data Product Management: What it is and Why it’s Important

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With the rapid digitalization of today’s world, it is no surprise that businesses have had to revise their product management structures. 

As systems governing our everyday lives become automated, it is easier than ever for companies to obtain valuable data regarding their target customer base, current market trends, and future business prospects.

But, with the widespread availability of data, companies require someone who can analyze that data and use those insights to maximize business productivity and profitability. This is where traditional product development management has evolved into data product management, with the role of the data product manager at its center.

So, what exactly is data product development management, and how is the job of a data product manager any different from a traditional product manager?

What is data product management?

Data product development management is a specialized form of product management that focuses on the collection, organization, retention, and transmission of data within a company to develop a new product.

Data product management is similar to traditional product management in the sense that all the data collected and shared is utilized for the development of new products.

But while both data product development management and traditional product management focus on developing a new product or launching a new service, the key difference lies in the fact that data pm specializes in the development of ‘data products’.

Data products are products that require an extensive amount of data, analysis, and machine learning techniques throughout their development process. The customized playlists created by Spotify is an example of a data product. It analyzes data extracted from the users’ past listening sessions, to curate a list that offers similar songs.

Since the development of such data products requires the handling of large amounts of information, traditional product management systems were replaced by product management data systems, which feature the integral role of the data product lead.

Why does data product management matter?

Data product management is especially important in today’s world because of the increase in this type of product in the market. 

Since developing these data products requires companies to collect and sift through copious amounts of data, they require data product systems that can handle large amounts of information.

But this doesn’t just mean that data PM only holds relevance in the development of data products.

Since the primary objective of product management is to develop products and services that are popular among customers, collecting data regarding consumer preferences and market trends is essential in designing any product customers will love.

Product managers are expected to go through this data and make changes in their product design at every step of the development process so that the final product is aligned with customer preferences. Of course, for these changes to be effective, the data behind them has to be credible.

This is where data product management comes in. 

With data product management, companies can create a centralized unit dedicated to the collection, retention, and transmission of their data. This ensures that the handling of data is streamlined and organized, and minimizes errors involved in data collection.

Thus, data product management is the driving force behind the development of data products, as well as just about any other product or service. This is because data product management ensures that data is handled appropriately and that the information fueling important production decisions is credible.

Product management data team structure

Here are the key roles usually found in a product management data team.

1.  Data Product Manager

The data product development manager is an indispensable part of a product management data team.  The role of a data product supervisor is synonymous with that of a product manager in a product management team.

A data product lead manager helps translate insights gained through data collection into the product development process. 

In essence, a data product manager is responsible for ensuring that a product develops following the customer preferences that the collected data shows.

2.   Data Product Owner

A data product owner is responsible for the data product that is being developed. They are involved in the product development process, but also communicate with data professionals.

A data product owner focuses more on the details of the actual product development process, while a data product manager is in charge of integrating data collection and product development, and ensuring that the final product is popular among customers.

3.  Data Scientist

While a data product owner is more concerned with the developmental details of a product, a data scientist is focused on handling the data that fuels the development process.

Data scientists have excellent statistical skills and are experts at analyzing data to generate hypotheses. Along with this, data professionals are also involved in testing the credibility of data, and interpreting tests to determine the likelihood of a product’s market success.

4.  Product Designer and Developer

Product designers and developers are the ones in charge of creating the actual product design.

The product design team uses the information data scientists present to develop designs that are aligned with customer preferences and current markets.

Product designers and developers are also in charge of testing the product with consumers to gather valuable feedback. These tests are used to generate data which is used to improve  the product.

5.  Data engineer

Data engineers collaborate with data scientists to analyze and interpret data.

Because of their skills in data/software engineering, they are also involved in creating AI and machine learning roadmaps that focus on making the product development process more efficient.

The Role of Data Product Manager

At the center of data asset management is a data product manager. A data product manager is like a specialized form of a product manager.

Data product managers can be responsible for both the development of data assets, as well as the development of regular products or services.

Compared to traditional product managers, data asset managers are well-versed in computer science, data programming, or machine learning.

A data product manager acts as the link between data scientists and product development teams, to ensure that credible data is driving decision-making throughout a product’s lifecycle. While a regular product manager would be more involved in generating product roadmaps, selecting product development teams, and communicating with customers, a data asset manager would focus most of their attention on sifting through data to choose credible pieces of information that support product-related decisions.

Hence, the primary difference between a data product manager and a traditional product manager is that a data asset manager will communicate more closely with data scientists and data engineers, and will be expected to be proficient in handling data themselves.

The primary responsibilities of a data asset manager include:

1. Supervise data management

A data product manager is responsible for overseeing the data that influences a product’s lifecycle.

A lot of a data product manager’s work focuses on collecting data, analyzing it, and using those insights to make decisions that lead to a product that is more in line with customer tastes. 

For this reason, data asset managers usually have a technical background in data science or data programming.

2. Coming up with a product roadmap

Similar to a traditional product manager, a data product manager is tasked with creating a product roadmap. 

The product roadmap is a visual representation of a  product strategy that is used to develop the product according to the owner’s product vision.

Data product managers are responsible for creating a product roadmap that can adjust for any data-backed changes in the development process. 

Data managers are also in charge of creating roadmaps that can incorporate creative ways to use current data to develop successful products.

3. Staying in touch with consumer preferences

One of the jobs of a data product manager is to conduct research and collect market data.

Data product managers have to remain aware of the needs of their customers at all times so that they are in a better position to use data to make product development decisions that are aligned with consumer preferences.

4. Using their technical skill set in the product development process

Data product managers, when compared with traditional product managers, are proficient in data management fields, such as data science and data engineering. Because of their technical skill set, data product managers are well-versed in coding languages like Python and SQL.

This allows data product managers to work closely with data scientists and engineers, and ensure that the product development process runs smoothly.

5. Leading the product development team

Similar to a product manager, a data product manager is expected to have great leadership skills

A data product manager is in charge of collaborating with the product development team, data scientists and engineers, as well as ensuring that both parties work together effectively.

As a leader, a data product manager is in charge of selecting new members of the product development team, and training and guiding them throughout the development process.

Product Manager vs Data Product Manager

Similar to a traditional product manager, a data product manager’s primary objective is to help teams develop products that are guaranteed to be popular with customers. 

To do this, a data product manager holds many of the same responsibilities that a traditional product manager holds.

For example, data product managers are in charge of assembling a product development team, generating product roadmaps, choosing performance metrics to measure the growth of products, and developing a management system that prioritizes different features of a product and distributes resources accordingly.

But, despite the similarities in their jobs, there are key differences between the roles of a data product manager and a traditional product manager.

Product managers are more focused on overseeing business issues, generating marketing strategies, and ensuring that the final product is designed while keeping consumer preferences in mind.

In contrast to this, data product managers correspond closely with data scientists and engineers and are more involved in collecting and processing information that will ensure a better product/market fit.

The primary differences between data product managers and product managers as as follows:

  1. Background

Traditional product managers are not required to have specialist certifications or degrees in technological fields, instead, they mostly have degrees in areas like finance, business, or marketing.

In contrast, a data product manager needs to have a degree in some field of data management, such as data or software programming, data science, or data engineering.

While product managers may have some sort of exposure to the technological field, it is not a requirement for them to know how to code or have work experience on technological sites. 

It is highly preferred for data product managers to have technical insight, since they are expected to work on large data sets, and collaborate with data scientists and engineers.

  1. Core Skills

Although both data product managers and product managers need to be up to date regarding market research techniques, UX practices, and effective communication mechanisms, there are specific core skills necessary for a technical product manager.

Data product managers, unlike traditional product managers, need to be well-versed in AI, machine learning, data science, data engineering, or coding. 

This is important because it helps them communicate effectively with data scientists and engineers, as well as operate on large data sets themselves.

  1. Teams

Data product managers work closely with data management teams, like data engineering and data science teams. Whereas, a traditional product manager is more concerned with marketing, finance, and customer support teams.

  1. Salary

When it comes to salary, data product managers generally earn more than traditional product managers, because of their skills in IT, data science, and data engineering.

On average, a technical product manager earns $111,140 USD per year. This amount can vary between $71,000 USD and $175,000 USD per year. In comparison, a traditional product manager earns roughly $96,552 USD per year.


For a company to thrive in today’s competitive business world, it must gain a loyal customer base by developing products that cater to consumer preferences. To do that, business owners need to be familiar with customer needs, and for that they need data.

This is why data product development management is essential when it comes to developing products that are sure to be loved by customers. 

Data PM enables companies to collect information regarding customer tastes and market trends and interpret that data to determine changes that can be made in their product design.