Die fünf Phasen eines Datenprojekts

Almost every data project follows the same five stages. Knowing the map keeps you from getting lost in the messy middle. 🗺️

You start by asking a clear, answerable question. "Why did churn rise in Q2?" beats "What is wrong with users?" every time.

A strong question names a metric and a scope. That way you will actually know when the data has answered it for you.

Next you collect the data that fits the question. It may come from a database, a CSV export, or an external API.

Then you clean: fix types, drop duplicates, handle missing values. This stage is unglamorous but it protects every later result.

Expect cleaning to eat the largest share of your hours. Pros say roughly 80% of a project is just getting the data trustworthy.

Now you analyze: summarize, group, visualize, and maybe model. This is where the answer to your question finally takes shape.

Finally you communicate the finding. A crisp chart or one clear sentence turns your work into a decision someone can make. 📣

In practice you jump back often. A weird chart sends you back to cleaning; a finding raises a new question. That is normal.

Write notes at each stage. Good documentation lets future-you and teammates trust and reproduce exactly what you did.

Five words to memorize: Ask, Collect, Clean, Analyze, Communicate. Keep them in order and any project feels manageable.

Test your sense of where time goes.

Five stages guide you: Ask, Collect, Clean, Analyze, Communicate. Expect to loop back, and document along the way. You now have a reliable map. ✅