The Skill-Set to Master Your Data PM Role | A Practicing Data PM's Guide
Key Competencies, Collaborations, Responsibilities, Career Progression, and More!
This piece is a community contribution from Gaëlle SERET, Staff of the Core Data Product team at Decathlon Digital. She closely collaborates with Group Product Managers and cross-functional teams to shape the org’s data product strategy, lead data discovery efforts, and ensure seamless data management practices. We’re thrilled to feature Gaëlle’s unique insights as a Data Product Manager on Modern Data 101!
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In a world where we constantly create and collect data through every click, swipe, and transaction, managing it has become more than just storing information. It’s about doing data as a product and delivering it with intention and purpose.
I’d love to give you a look into the role of a Data Product Manager, where I get to both create and explore. I’m excited to share my passion for this job and its importance, especially in how it helps my internal colleagues and how it shapes tomorrow's organization.
Let's dive into this product's craftsmanship.
As Data Product Managers
As Data Product Managers, our focus is on creating a seamless user experience within the data ecosystem, encompassing both data products and services. Our role is to transform data into valuable knowledge while delivering services that not only address current needs but also adapt to future demands.
1/ Scope
Data Product Managers engage at various stages of the data value chain. We collaborate with sources to distribute their data to the organization through source data products delivered via Kafka services, APIs, or platforms.
We can focus on data aggregation at the corporate level, shaping a core strategic vision. We also work on deploying data products tailored to specific consumer needs, often providing solutions via dashboards or self-service models. Additionally, we specialize in specific challenges, such as data quality or AI modeling.
Regardless of the scope, the role consistently involves key deliverables, along with product and data management responsibilities and expertise.
Related Reads 📝
Learn more about data product deliverables at each stage.
2/ Users
A Data Product Manager’s clients/users include everyone across the organization, from operational teams and executive committees to stewards, quality managers, analysts, and AI teams.
Data Engineers and Data Product Managers
Data Engineers, Data Scientists, and other peers that consume and produce data.
Use case: I deliver the product information on the platform. This data is used by another platform teammate to measure the CO2 impact of a finished good.
Data Governance
Stewards, Quality managers.
Data modeling is based on the knowledge of the business process owner. Functional understanding is constructed with data stewards. Data quality monitoring is created with data quality teams.
Use case: A new business process in the organization requires to change an existing business concept to integrate new attributes. Data quality teams need to understand if a new source has been smoothly integrated.
Head of Data & Strategic Stakeholders
Such as Chief Data Officer and Chief Technical Officer.
We help to conceive and serve the data transformation of the company. The Head of Data and Chief Data Officer rely on Data Product Managers to operationalize domain strategies, data governance, and platform strategies. We also help with organizational maturity.
Data Analysts
Analytics team members, such as analytics engineers and data analysts.
Use case: The product information is a dimension that is used in the company’s performance analysis.
Application Owners and Managers
Digital Product Manager, Group Product Manager.
We enable transversal data consumption across the organization and aggregation with different sources.
Use case: Monitoring the time to market of a single product, from conception to publication of the product information on the websites. This measure is directly impacted by the translation business processes, and helps teams improve their operational excellence.
Strategic Decision-Makers
Such as the Chief Product Officer.
I develop key indicators for my digital applications to measure the Key Results I aim for my team to achieve.
Use case: I’m able to understand the impact of a global marketing change.
Operations
Such as Sales Associate.
Operational teams and systems with aggregated information, complex IA models or calculated indicators.
Use case: Monitoring of products in stock and their financial valuation.
Business Managers
Such as Store Directors.
By improving the overall data maturity of the organization.
Use case: I help an operational team to digitize and automate their business process, saving hours of manual work.
Top Managers
Such as the Chief Financial Officer and Chief Executive Officer.
By enabling the piloting of the company and improving the resilience of the organization to manage its transversal problems.
Use case: I pilot the organization with key metrics defined with my North Star.
Related Reads 📝
Learn more about North Star metrics and how to pump them.
3/ Responsibilities
Data Product Managers responsibilities start with best in class Product Management practices ensuring a visionary and user-centric approach to data.
As Product Managers, we discover the teams and business processes that create and consume the data. We establish an initial “State of the art”, identifying existing business objects, determining the relevant sources to work with, and assessing what is already governed and deemed high-quality by those sources.
This discovery process entails a detailed data exploration. Then, we investigate if the scope already meets data management standards and discover new business requirements. This enables the construction of a roadmap and a target architecture. In addition, with these deliverables we assess the data and product maturity of the stakeholders involved.
Example: To successfully get an internal team to adopt an AI model, it’s essential to understand their current user journey, data maturity, and technological readiness. A Data Product Manager (PM) plays a key role by clearly defining the problem and its business challenges, which allows Data Scientists to create the right model.
While it's useful for the Data PM to know AI basics, their main job is to focus on user needs and business goals, much like a software PM who prioritizes user experience while engineers handle the technical work. For instance, if my team is building a model that impacts individual performance or costs, a strong Data PM will prioritize building user trust by ensuring they can easily track the model, its inputs, and its results—just like how people review their phone or energy bills when something doesn’t seem right.
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A Data Product Manager’s responsibilities entail to deliver, with the right stakeholders, the following elements for each “data product”:
Managing the data: Overseeing business objects, attributes, and calculated indicators to ensure the data is accurate and aligned with business processes.
Providing functional descriptions: Clearly documenting what each data element represents and how it supports various business processes.
Explaining how to use the data: Offering guidance and best practices to help users navigate, access, and leverage the data effectively.
Ensuring data lineage is clear: Tracing the journey of data from its origin to its final destination, ensuring transparency and trust.
Tracking metrics that assess data quality: Monitoring key indicators to ensure data remains accurate, complete, and reliable.
Tracking metrics that assess data usage: Analyzing how data is being utilized within the organization to improve accessibility and impact
Setting up monitoring and alerting systems: Implementing tools to monitor data health and automatically alert teams to any issues or irregularities.
Related Reads ↗️
In addition to these deliverables, we can implement key Data Management practices. These include adhering to company standards for governance processes, data architecture, knowledge management, change control, security, and privacy.
If no specific team or organization has established these standards, we need to define and implement the best possible solutions ourselves. This can be especially difficult in organizations with low data maturity, where the role of the Data Product Manager is poorly defined, and the organization doesn’t recognize that these practices are essential for us to perform our job effectively.
4/ Skills
Data Product Managers skills encompass a wide range of competencies, from cross-functional discovery, data management, and delivery and testing, all aimed at achieving operational excellence. The level of complexity of daily operations depends on the scope (data product, data family or domain) and the organizational maturity of the company.
5/ Next Evolution
Within numerous organizations, the next role after Data Product Manager can be Head of Data Product (or Group Product Manager) on a specific data domain. This role considers the transversal alignment between business domains strategy and the data strategy. The responsibilities taken by a “Head of” ( or GPM) are the following:
Managing legacy and company data strategies: Overseeing the integration and evolution of existing data strategies with new approaches to ensure consistency and effectiveness across the organization.
Embarking operational sources: Engaging with and influencing operational teams to align their data practices with strategic goals and standards.
Enhancing governance practices: Improving data governance frameworks to ensure data quality, security, and compliance throughout the organization.
Aligning the roadmap with cross-functional needs: Developing and maintaining a strategic roadmap that addresses the requirements of various departments and functions, ensuring cohesive progress and goal alignment.
In the skill matrix presented above, the “Head of” focuses on developing transversal discovery and agility at scale. He is also in charge of developing and animating transversal processes across business domains.
Clarifying the Whole Career Path
The role of a Data Product Manager can seem challenging or complex. In this section, I aim to simplify the career path by sharing the essential tasks and skills in product management, data management, and agile methodologies that you need to master to take over the role.
1/ Job ladder and required competencies
This graph outlines the career progression, highlighting the increasing scope of responsibilities and the shift in focus toward serving a broader customer base. As you advance, the focus expands from individual product management to addressing the needs of numerous customers. The graph also illustrates the evolving competencies required at each stage, showing how skills adapt to meet the growing demands and complexity of managing larger, more diverse customer groups.
2/ Daily activities
This graph provides an overview of daily activities throughout the course of a Product Manager's career. As you progress upward in the hierarchy, your responsibilities shift increasingly towards strategic alignment. The higher your position, the more your focus centres on ensuring that your strategy is in sync with the strategies of other key members across the company, emphasizing collaboration and cross-functional coordination.
3/ Detailed competence, deliverables, and responsibilities
This interactive board is designed to help you evaluate your current skill set in data management, product management and agile practices. Whether you’re just starting out or looking to refine your expertise, this resource provides a comprehensive overview of the essential skills you need to master.
As you explore the board, you’ll find various sections dedicated to key competencies, including data strategy, user journey mapping, agile methodologies, and collaboration techniques. I materialized job ladders and proposed specific deliverables that you should aim to produce for each level, guiding you in your professional development.
Conclusion
Use this article and this Miro board to assess where you excel and identify areas for improvement. If you are new to this field, I hope that you can strategically focus your efforts and set clear goals for your growth in the dynamic field of data product management. If you are a seasoned professional, I would be happy to receive your feedback and improve the model for our junior colleagues all over the world.
Let’s get started on delivering impactful results for your end users!
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