A.I. Tools

Making and avoiding mistakes as an Analyst


Ever been anxious about being called out for mistakes in your analysis? You’re not alone.

Dennis Meisner
Towards Data Science
Source: Unsplash

Producing a faulty analysis and guiding my organization in the wrong direction based on my results has been one of my greatest fears since starting out as an analyst. And to be honest, being exposed and called out on my mess even more so.

My nightmare scenario: A major feature my team has worked on for months, based on my recommendation, fails to deliver value during testing. People start asking questions, and after investigating, it becomes clear that my assumptions were based on incorrect analysis results.

Having spoken to and mentored many analysts since then, I’ve learned that I’m far from the only one who feels this way. On the contrary, this is a very common fear that many analysts share.

The reasons why one can feel anxious about an analysis are quite obvious: publishing and promoting wrong results and recommendations affects both the organization as a whole and at the same time poses risks for us personally.

Despite these risks, avoiding sharing analyses or shying away from controversial or assumption-challenging results is not the right approach. In fact, these analyses are often the most important. Instead, it is crucial to develop strategies that help mitigate the risk of producing flawed outputs. And this starts with appreciating the following:

Making mistakes as an Analyst is freaking easy.

Analysts work in a very complex environment with a high degree of freedom, so there are many opportunities to make a mistake or draw wrong conclusions.

Conducting an analysis involves several different steps:

Defining the research question and approachCollecting and cleaning the dataAnalyzing and interpreting the dataVisualizing and communicating the results

At each stage, it is fairly easy to make a mistake which will ultimately be reflected in our recommendations and the…

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