Why is it important

Why is it important

Level 3

The "level" refers to the number of humans working on data engineering:

3 people - do some planning. Think about roadmaps, frameworks, features, and costs. At this point, whoever goes on vacation can leave the laptop in the office. Most of the work is still making data available and timely, but some time can be spent on actual forward-looking planning.

Three is also a good number where there is some variety in backgrounds, experiences, and interests, giving a higher chance of covering more relevant aspects of decisions. Just to set the expectations, though, it would be good if, within the group of three, one person doesn't hate working on the things mentioned above. The capacity to plan is good; taking the time for it is better, and coordinating it with the rest of the organization is best.

The skills layout of the team so far:

- second hire - good data-related generalist, good communication skills

- third hire - good data-related generalist, good communication skills, good planning/process skills

From here on, it gets more specific. As touched upon before, analytics is useful when it's timely. It means different things in different domains, so it follows that the skills required are somewhat more specialized. One worry might be that - what if an area gets "solved" and that person with those narrow technical skills is no longer needed? It depends on how specialized we are talking about here. If it's a sub-area, like A/B testing or machine learning engineering, then it's general enough that the reverse is probably true - as teams discover how valuable these things can be when done well, then the demand will likely outpace the supply.

On the other hand, overly narrow specialization might be a concern if a single tool needs a single expert user for the foreseeable future. In that case, it might make sense to outsource the skill for some time while gaining basic proficiency within the team. Once again - communication and planning are highly encouraged 🙂

Lastly, how much investment in the data platform and analytics is enough, and how much is too much? It's unevenly distributed! It might be enough for the HR team to automate a couple of weekly reports, but updating a dashboard once an hour for the marketing team might be too little, too late. Thus, distributing all the analysts uniformly between the teams/domains might be about the worst possible approach, almost guaranteeing that everyone is either bored senseless or burning out. In the following article, I will discuss finding the balance and how things change over time.