How to analyze and summarize digital data

With the growth of the big data phenomenon, data analysis and summarising are becoming crucial elements when it comes to revealing trends and sharing key information.

Beyond data analysis, it’s also important to know how to cross-check information, to overlap statistics, and to create visual forecasts and projections.

Here are the key steps for ensuring optimal use of your data and boosting its value:

Check the reliability of your data

This step is crucial – it concerns having a sound understanding of the data that you’re going to work with:

  • Where does it come from?
  • Is it reliable?
  • How was it created?
  • Can it be used to generate our own data, etc.? ?

Start with flat tabulation

This is particularly important if your data isn’t all the same type – you analyse the data elements individually, one by one. To do this, you might need to harmonise the data, if all elements aren’t in the same format.

Flat tabulation – this refers to descriptive statistics (e.g. %) for a qualitative variable. A flat tabulation is typically represented with a bar chart.

Move onto cross tabulation

This step involves analysing the relations between two or more variables. It allows you to highlight the relation between two or more data groups (e.g. place of residence and age). Cross tabulation enables the obtaining of finer analyses to check, validate or disprove hypotheses.

Create visuals

Present the most important results in the form of graphs, which will allow you to summarise data in a concise, uncomplicated and clear way.

Present your visuals through story-telling to contextualise them and to bring your data to life.