product management

Want to become a great data product manager? Here’s what you need to know

Product managers that rethink how they use data, not just to guide internal decisions, but as a growth and revenue driver, are outperforming peers by double digits. One of the most effective ways to do so is to build a data product. 

That’s easier said than done. Creating a data product is hard. It's usually a mix of art and science, where you need to find the right balance between what your users want, what your data can provide, and how easy it is to bridge the gap between these two. Unfortunately in many organizations, these two roles are siloed, with different individuals - or entirely different teams - driving these two workstreams. However, the most successful data-driven organizations have taken a completely different approach. 

That's where the data product manager comes in: they're responsible for building the right experience that turns all that raw data into something useful for their company, partners, and customers. But what does that actually mean day-to-day? And how do you become one? 

What is a data product manager?

A data product manager is a role within an organization responsible for managing the development and implementation of data products. They work with stakeholders, engineers, and product teams to identify opportunities to turn data from an abstract concept into real products that customers, partners, and other employees can use to find insights and make decisions. As part of this role, data product managers are typically relied on to identify opportunities for leveraging the entirety of a company’s available data, from proprietary data to third party data, to create new capabilities. These capabilities must both meet user experience standards, without expensive or costly development overhead. 

The primary goal of a data product manager is to ensure that data is used efficiently, effectively, and in line with organizational objectives. As opposed to other fields like business intelligence, where data is primarily used to inform decisions, data product managers often think about how to turn data into a totally new product, or delivering a new data-powered capability in an existing product. This means they are also responsible for understanding customer needs, cultivating relationships with key stakeholders, coordinating resources across teams, and assessing potential risks associated with turning data into products. 

Data product managers must have an aptitude for data-driven decision making and be highly organized, analytical thinkers with strong communication skills. Additionally, they must possess a deep understanding of the business goals of their organization, a thorough knowledge of data science and analytics tools, including embedded analytics, and a keen eye for user experience design. 

How does a data product manager use data?

Data product managers employ data in two key ways. Like traditional product managers, data product managers use a variety of data-driven analysis and decision making techniques to identify opportunities, develop strategies, measure success, and assess risks around leveraging available data. This may include utilizing machine learning algorithms to detect trends or anomalies in datasets or creating predictive models to forecast outcomes. 

Data product managers, however, must take this further, not just using data to shape their products, but as the core component of their product. This is why familiarity with a wide range of analytics tools and technologies to make sense of data, like interactive data visualizations to empower users, whether those are internal users, external partners, or customers, to understand and communicate insights, is critical. Ultimately, data product managers use the insights gained through their analysis to steer business strategy, hit product management KPIs, and empower organizations with the most effective data solutions. 

How to become a data product manager?

While there is no singular career path to becoming a data product manager, successful candidates typically possess a combination of technical knowledge, analytical skills, and business acumen. A background in software engineering or data science may be beneficial for those looking to enter the field, although it is not required. Additionally, many employers value experience with programming languages such as Python, R, and SQL, along with analytics tools like ThoughtSpot. It is important for applicants to develop a strong foundation of data literacy and be able to demonstrate their ability to interpret data trends and insights, as well as their capacity for categorizing business opportunities and problem-solving. 

Professional certifications in data science, analytics, or product management can be beneficial for those seeking to gain industry-specific credentials. Building a portfolio of successful projects demonstrating your skills and qualifications is an excellent way to distinguish yourself from the competition. Finally, many employers look favorably upon candidates with a history of working collaboratively across teams or organizations to achieve common goals. 

What is the average salary for a data product manager?

The median annual salary for a Data Product Manager in the US is roughly $129,438 according to data from However, there can be significant variations depending on factors such as experience level, location, and industry. For example, a Senior Data Product Manager may earn $158,932 per year. Additionally, the salary range for this role tends to increase with experience and expertise. It’s important to note that data product managers typically receive additional compensation in the form of bonuses and other incentives such as stock options or retirement plans.

Make finding data easy

Data product managers use data to help create products that are not only useful but also profitable. By understanding how customers interact with data, they can produce products that make finding and using data easy for everyone. If you want to create a memorable search experience and drive more engagement in your data app, check out ThoughtSpot Everywhere today. Keep users coming back for more by creating a better experience.