Data Analysis Workflows and Good Practices 

Whether you are optimizing supply chains, predicting customer behaviour, or conducting research studies, data is your most powerful asset. What sets successful teams apart is not how much data they have, but how well they use it. A good data analysis workflow turns raw data into actionable insights. It ensures every decision is based on a process that is modular, scalable, and reproducible. Using tools like Snakemake or Airflow to create a workflow from your notebooks and scripts allows you to automate, schedule, and monitor each step, turning ad-hoc analyses into reliable, repeatable pipelines.

1. Introduction

You may not be a full-time programmer, but you have probably faced unclear processes: countless notebooks with slightly different versions, or scripts that mysteriously fail on line 1029 when analysing the week’s data batch. Often, the problem is not the code itself, but the order and timing of how tasks are executed, the execution environment, or maybe the person who usually run this analysis is on holidays? This slows down decision-making and creates frustration.

Workflows help organize the execution of different scripts and tools. They can handle multiple languages, different tools versions, and even different operating systems. A well-designed workflow is scalable, reliable, and easy to manage. But what exactly makes a workflow more than just a collection of scripts.

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