Measuring Success of a Data Analytics Project

To measure the success of a data analytics project, we should start by understanding the origin of the data.

Data is merely a byproduct of a process.

This process might be business-related, technical, or natural, but most importantly, it involves human decision-making.

We track data with the explicit intention of continuously enhancing the process by making better decisions.

We use data analytics to understand the inherent patterns in the data and transform them into better decisions.

All data analytics projects follow a similar trajectory, starting with a dataset and culminating in a report (of what has happened and why), a prediction (of what might happen), or a strategy (for the future).

It's a common misconception that the quality of insights and the sophistication of the method applied determine the success of a data analytics project.

Instead, success should be measured by how much data analytics can improve the underlying process.

It involves running multiple iterations of the process by applying the recommendations from the data analytics project.
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For aspiring data analysts, education should involve a foundational process, not just a dataset, that they can measurably improve by applying their data analytics skills.