An Introduction: welcome!

Welcome to my blog! I am a data scientist and a consultant at SIRIS Academic, a consulting company that works with academic institutions, research organizations, and governments. Here, I discovered that my great passion is to help people and organizations understand themselves and develop a strategy by putting data in context.

Numbers by themselves don’t mean much, if context is not provided. As simple as it may sound, it is actually very challenging to put in practice. Let me illustrate this by a simple example.

The school, the teachers, the students

Imagine a school, with its teachers, and its students. Imagine you are the Principal of the school, and you want to improve the school’s performance.

So let’s say that you hire a consulting company, and ask to calculate the average marks of the students. The data scientist takes the school’s database, and comes up with a number: 6.7/10. What does this number mean? Is it high or is it low? What can you do to raise this number?

The problem is that this number, alone, doesn’t mean much. To give an idea, this number can be the result of – for example – two very different scenarios:

  1. There are many excellent students and many students that fail. 
  2. The difference between the best and the worst students is fairly reduced.

What can you do then to increase the average? Many different things, such as:

  • trying to increase the number of excellent students
  • trying to decrease the number of students that fail
  • trying to increase the global average and reduce the inequality between the best and worst students

Your choice, from this point of view, is entirely political, and no data scientist can help you decide what you want to do. However, data scientists and consultants can help you understand what to do if you have an objective. Then, you can have a strategy.

Numbers and objectives

Certain numbers, put in their context, will mean much. For example, let’s say that the result of a comparative analysis of the consulting company above is:

The proportion of students that have an average grade higher than 9/10 is 20%, which is remarkably higher than the nation-wide average of 10%. Furthermore, this percentage at the same school was at 15% a mere 5 years ago

This kind of analysis is rich in meaning, because it provides context, in terms of temporal and geographical comparisons.

Still, this finding would sound great only if you were interested in a specific objective, which is to maximise the number of excellent students in your school.

Say that, instead, your objective was to minimize the differences between the best and the worst students. This same analysis would mean that you have entirely failed your objective.

This example illustrates that not only temporal and geographical comparisons provide context to numbers, but also, and more importantly, meaning is given to numbers when they can be embedded into a wider context, which takes into account objectives and strategies.


This first post is just an appetizer.

I will try to provide some concrete examples in the future of how numbers may provide useful and interesting insight in some cases, or may be totally misleading in others. I hope to see you again here! Feel free to leave comments and start a discussion about the topics.

3 thoughts on “An Introduction: welcome!

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