Data have no meaning, only information has. Data are just a list of numbers or a set of words. Information is data that has been analysed in a certain way to provide meaning regarding a certain topic.

The first step to do this, the most important and probably the most difficult, is not to assume anything. You want to ask questions to the data, not torture them until they give you the answers you want. Of course you can manipulate data to give you the results you want, but that is not an analysis. Analyses are free of assumptions and prejudices (at least ideally). And maybe you end up being surprised.

The second step is to organise the data. Sometimes data are already organised (you need to check anyway), but sometimes it is chaos. Imagine you want to analyse the evolution of the number of daily infections by COVID-19. You want the data to be organised chronologically. But then again, when that is done, it is just an organised list of numbers that is hard to read. So, you try to visualise it with tables or charts. In this specific case, the best option is a line chart. You can see if and when the number increases and decreases. At this stage, you can start to do some analysis: you can see that there are some peaks and some lows at certain times and other times the line is relatively stable. So you question: why?

The third step is contextualisation. Here, you are going to connect your organised data with other information that you think is relevant. In the case of our example, you know governments around the world had taken measures to face the pandemic. So, you can mark the days when those measures were taken to see if the evolution line of the number of infections has changed subsequently. You also know that the effect of those measures was probably only felt some days later. So, you can shadow a period on the line chart and see what happened then. Then, other questions may arise. For example: were there differences between regions? And if you compare with other countries? And what happens if you cluster your data: instead of a daily number, you would have a weekly number or a monthly number? Sometimes you can organise data in a way that is not useful, but then you understand that is not the way and try another way.

The final step is to draw conclusions. You organised the data so you could visualise them in tables and charts. Then you analysed them, connecting them with other information that is relevant to the topic in question. At some point, you feel you are ready to draw conclusions. For example, were the measures taken by the governments truly efficient regarding the daily infections? What was the impact of the new strains? This is when your data turns into information.

Check other “How to…?” related to project management.