Product Management in Alternative Data – When I first started doing Product Management in the world of data solutions, a Data Scientist leader said to me, “product processes like agile are not valid in this kind of work” and pushed back on any plan to implement scrum or any other proven product management methodology. I was perplexed and wondered if this was in fact true. I decided to ask others who had extensive experience doing product management in the world of solving problems with data.
As suspected, most of the product folks in the data solutions world have since confirmed that there is nothing unique about Data Solutions when it comes to the product management best practices. While the development and experimentation of data products has a unique set of problems (i.e. normalizing data, data governance, ETL, etc), these can all be accomplished through the setup of a cohesive agile team.
Some have proposed that Data teams should be run with individual contributors, without the need for Product Management. Anh Nguyen gives some good reasons why this is not true:
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 does not scale well for many reasons, the four main ones being:
- Product work ends up accounting for all of the IC’s time.
- Not all ICs are well-equipped or willing to handle product work at scale.
- Gaps between business units and technical teams widen.
- Gaps between individual technical teams widen.
As such, Data Product Teams need Product, Development, and Quality contributors just like any other technology solution. At the end of the day, the team is tasked with creating value and solving problems with the data assets and technologies available to them. It is in using the proven methodologies like agile that we are able to accomplish that task with efficiency and vision.