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Data Product Management
How to think about this oft-confused function
In this article, I will explore the practical aspects of the Data Product Manager role. Often misunderstood in large companies due to unclear definitions and expectations, this role’s ambiguity can lead to disappointment among product managers, (data and services) engineers, and most importantly, the customers of those data products.
My goal here is to clarify the responsibilities and value of a Data Product Manager to ensure better alignment and success for this role in the enterprise.
Role Definition
What is a Data Product Manager (DPM) supposed to do?
In a data mesh setup, a DPM manages the “data product”, which includes not just the core data assets such as tables, views and reports but also the data pipelines and the underlying data models. They also oversee data contracts up and downstream, product documentation and intangibles like refresh rates and a “quality score” that combines data quality, pipeline quality, freshness, timeliness, availability, and completeness.
I see this work more like an internal data marketplace, with each DPM managing their products similarly to a B2B SaaS company. In addition to the core data product development, the DPM provides product marketing support, understands the long-term vision of their customers, and keeps technical and architectural improvements in context of the overall lifecycle of their product.
Role Value and Expectations
A DPM is essential in managing how data is produced, consumed, and managed within and outside a company. Collaborating with data architects, the DPM defines a domain’s data model, enabling the data engineering team to innovate while ensuring cross-domain data consistency.
Beyond the data model, a DPM translates business initiatives into domain-specific impacts and collaborates with other DPMs to meet measurement requirements for strategic business initiatives which tend to cross domains.
As a product manager, a DPM works with internal customers to understand their roadmaps and establish a release cadence aligned with broader technology or data team goals, focusing on scalability, availability, resiliency, and…
