Two Halves Don’t Make a Whole

Blog Summary: (AI Summaries by Summarizes)
  • Chapter 3 of the "Data Engineering Teams" book explains how to do a skill gap analysis.
  • During the analysis, it's a binary decision of whether a person has a skill or not.
  • Some people want to use fractions to indicate partial skills, but this is a mistake.
  • This thinking leads to failures in Big Data projects and amateur mistakes.
  • Having several beginners with 0.1 skill level doesn't add up to an intermediate or advanced person.

In Chapter 3 of my Data Engineering Teams book, I show you how to do a skill gap analysis. During the analysis of the team, you either say the person has the skill or not. It’s a very binary decision.

Some people have written me asking if it can be a fraction. Instead of a 1 or 0, they want to say 0.5 or 0.1. In their minds, the person has some amount of that skill, but not the entire skill.

This thinking is a common mistake that leads to failures with Big Data projects. The thinking is that 10 beginners together will somehow aggregate together as a single intermediate to advanced person.

The short answer is no. The longer answer is no, it’s a really bad idea.

While working with companies, I see the manifestation of this thinking. It leads to all sorts of amateur mistakes and uses. I’m at the point now where I can estimate a team’s abilities on how poorly they implemented something.

When you have several 0.1 beginners, they all have the beginner understanding. It isn’t complimentary pieces of the pie all filling in gaps; it’s all the same piece of pie stacked on top of each other.

If you’re starting a data engineering team, make sure you do a skill gap analysis. Just as important, make sure the team has the technical skills to accomplish their goals.

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