![]() Within product management, a similar trend is taking shape. These skills are rapidly being formalized into separate roles, such as data engineers, data scientists, research scientists and ML engineers. In the early days of the data revolution, orthogonal data skills like software engineering, statistics and modeling were rolled under the same umbrella of data science. The State of Data Product Management Roles ![]() Most companies at scale ultimately go with the second approach of having a product organization. While this might work for a free social network product, it’s potentially catastrophic for a paid and operation-heavy product like on-demand services. If it’s possible for a single IC to make adjustments to a product, immediately get objective feedback on how it’s performing, and roll-back in worst-case scenarios without major ramifications, then the first approach is extremely powerful. This is especially common for highly cross-functional products such as e-commerce and on-demand services. The second approach is to create a formal product management org that is responsible for maintaining source-of-truth roadmaps and coordinating different teams and ICs to execute. The first approach is to break down work into projects that are self-contained enough for a single IC or small technical team to handle end-to-end, reducing the need for some type of central coordinators. Gaps between individual technical teams widen.Īt this inflection point, there are two potential responses.Gaps between business units and technical teams widen.Not all ICs are well-equipped or willing to handle product work at scale.Product work ends up accounting for all of the IC’s time.This does not scale well for many reasons, the four main ones being: In small data teams without formal PMs, standard product responsibilities such as opportunity assessment, road-mapping and stakeholder management are likely performed by technical managers and individual contributors (ICs). This article aims to explain what product management looks like in the data space and why it is important. How should an organization prioritize among thousands of potential directions?Īt Insight, where we have helped thousands of Fellows transition into various roles in the data industry, we see a rise in industry demand for product managers who can tackle these prioritization and coordination challenges among data teams. Models need to be built, deployed and monitored in production.Īnd all of these undertakings need to produce concrete business results.Data needs to be processed, discovered and shared with relevant teams.Raw events and transactions need to be collected, stored and served.A large supporting ecosystem must be in place in order for data to flow through the veins of your organization: In most business settings, the models might actually account for the least amount of impact. Animal classification algorithms and Go-playing_agents dominate the AI hype cycle, but algorithms are just one part of the entire data product ecosystem.
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