Organizing and scaling an effective data team

https://www.robdearborn.com/2022/03/13/organizing-and-scaling-an-effective-data-team/

At ~1 member:

• At least as soon as a company has any material volume of users (and thus data), it needs to start building its data team intentionally. It’s natural for a data team’s first member to be a technically-skilled bizops person or analytically-skilled engineer who gets sucked into doing analytics work and may not naturally think of themselves as a founding data team member. Encourage them to embrace the opportunity! A high-functioning data team has outsized influence in steering the company’s direction, and there’s tremendous value in establishing good practices early on.
• At this early stage, time is primarily spent setting up foundational infrastructure and dashboards. These days “infrastructure” should mostly be plug-and-play SaaS, but backend software engineers can be asked to pitch in on any additional custom stuff that’s needed.

At ~2 members:

• The company has made its first dedicated data team hire. The data team will eventually consist of data analysts, data scientists, data engineers, and ML engineers. Early on, however, founders should seek jack-of-all-trade data scientists who can range from strong business analysis to rolling their own infrastructure. They are hard to find but well worth waiting (and paying a premium) for.
• The data team operates mostly as a centralized service desk for the rest of the company, which itself is usually still operating more-or-less as a single team.
• Data work is still mainly about reporting metrics, and task ownership is informal and round-robin.
• There are a couple of reasonable ways of managing a nascent data team. One option is for one of the first hires to be someone with people experience who is hired to be the team’s leader from the start, though with a clear understanding that 1) they’ll mostly be doing IC work for a while and 2) they’ll be layered if their personal growth doesn’t keep pace with the team’s/company’s needs. The other is to have the team to report to a non-data leader for a while, provided this person is sufficiently technical and analytical and excited about devoting meaningful time to both developing the data team’s roadmap and being the voice of data in leadership settings. Usually the best person for this is the company’s seniormost engineering leader. After a while, the team can be spun out under an externally-hired or internally-developed senior data leader, or remain under the original person if they enjoy and are good at the role.

At ~4 members:

• The data team begins to specialize. As the company splits into multiple cross-functional teams, focus areas should emerge.
• Increasing specialization and data volumes enable new types of data work: experimentation and exploratory analysis. Over time, more and more of the team’s focus should shift from reactive reporting to proactive exploration and development.