IBIS Strategy Days 2026 – What a team cooking class has in common with SAP Data Analytics!

Given this headline, you’re probably wondering what these two things could possibly have in common. At first glance, they seem very different, but they actually have more in common than you might think. In the following article, we’d like to take a closer look at various points:

1 – The Ingredients vs. the Data

What would a dish be without the right ingredients? Quality, freshness, and careful selection determine whether the meal will taste good in the end. The same is true for data analysis. As the name suggests, it all starts with data. The quality, completeness, and relevance of the data are key factors in determining whether usable results are produced in the end. In summary, we can say that data is the ingredient of analysis.

2 – The Recipe vs. the Analysis Process

When cooking, there is often a structured set of instructions that specifies the order of the ingredients, the appropriate quantities, and the ideal technique for preparing them. This is what many people refer to as a recipe. The analytical workflow is similar; it consists of clearly defined steps: collecting data, cleaning it, transforming it, modeling it, and interpreting it. The analytical workflow is, therefore, the recipe for insights.

3 – Preparation vs. Data Processing

Good preparation when cooking speeds up the cooking process enormously. Imagine the following scenario in the kitchen: You need to cook a delicious meal under time pressure. If all the vegetables have already been washed, the necessary ingredients chopped, and everything is ready for the pan, you’ll save yourself a lot of stress and time. The comparison to data preparation isn’t far-fetched here. Data is also easier to process if it has already been cleaned, normalized, and structured in advance. Data cleaning is therefore the “mise en place” of data analysis.

4 – Experiment and taste as you go

During the cooking process, it is often recommended to taste the dish as you go and adjust the flavor with additional spices or ingredients if necessary. Recipes are sometimes modified slightly based on personal experience. This results in slightly altered, unique flavors. In data analysis, it is often the case that the process follows a specific model or framework. Here, too, it is essential to continually review this model, adjust it as needed, or expand it with fresh innovations.

5 – Presentation and Interpretation

The meal is ready to eat, and now it needs to be plated before it can be enjoyed. As we all know, you eat with your eyes first, so the food must be plated attractively if you want to create a truly satisfying experience. This principle also applies to the presentation of analysis results. Having the results is one thing; presenting them visually in a way that clearly highlights their value is another. Only well-presented analysis results create value for the customer.

CONCLUSION

Data analytics is similar to a cooking class in many ways: raw data is the ingredients that must first be prepared. Only through a structured process—comprising data cleaning, analysis, and interpretation—can a usable result be produced—much like how individual ingredients are transformed into a successful dish through technique, timing, and experience.