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The DNA of an excellent (data) ANALYST

What does being a (data) analyst entail ? The following points are my distilled answer from my 8 years of experience as a data practitioner

In a nutshell, you need to excel at: Observation — Analysis — Synthesis

An objective sense of observation:

Here you need 2 critical components: Curiosity and objectivity

Curiosity helps you ask questions. If you can’t ask questions, you can’t be an analyst. It is as simple as that.

Objectivity helps you ask better questions. If you ask biased questions, you will get biased answers. Remember that we as humans are biased by ‘design’. The more you train yourself to ask less biased questions, the better you’ll become as an analyst.

A keen understanding of statistics, distribution and patterns

This is the analytics aspect of the mix.

You don’t need much fancy shit here (like statistical modelling or ML). The foundations can get you super long way. But the foundations are not as simple as they sounds. A couple of subtle aspects that I consider as foundations are: Do you understand how you data/phenomena behaves ? Do you understand why those pattern exist ? Do you know what could impact them ? Can you distill signal from noise ? What about the time factor ? etc… You can check my summarised 10 Commandments of Analytics

Developing an intuitive understanding of these aspects will set you apart from the rest as honestly not many get this level

A well developed flexible decision making skills

Basically, you need to be able to draw conclusions.

Analyses can get quite complex and messy: if you are good with asking questions, you will see that as soon as you start asking them they flood you: Each question branches into multiple new ones. The challenge for is to cut through the noise and be targeted in what to tackle and what to ignore. Ignoring a question is more difficult than it sounds but you need to do it if you want to be effective. The rule of 80–20 applies here: By the end 20% of the questions/aspects you answered will get you 80% of results. The skilful analyst can identify this sweet spot and act on it.

One thing is critical here: Never ever wait for others to draw conclusions for you. You need this aspect to ensure that you are driving impact and refine your ‘analysis pruning’ process. Also this part is generally the more difficult on a cognitive level as anyone who gets involved in decision making knows: You need to synthesis, decide and move to the action as soon as that’s needed to not fall into the trap of ‘analysis-paralysis’.

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Ahmed Omrane

Ahmed Omrane

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Head of Data & Analytics @ Fabulous. On Data, Analytics, Tech, Business and Life…