Data Interpretation: get the most out of your data

Nowadays more then ever, it is very difficult to grow and make trustworthy improvements without minimal data collection and interpretation. Without appropriate research and analysis, an idea is likely to remain in a lifeless state forever: this is why a good data interpretation approach is essential.

Data Interpretation is the process of making sense out of a collection of data that has been processed. The interpretation of data assigns a meaning to the information analyzed and determines its signification and implications. The purpose of data collection and interpretation is to gain useful and functional information and to make the most informed decisions possible.

It is evident how important is data interpretation and this is why it needs to be done in a proper way. We see data arriving often from multiple sources, having especially a tendency to enter the analysis process with uncoordinated ordering.

Data analysis tends to be extremely subjective. That is to say, the nature and goal of interpretation will vary from business to business, likely correlating to the type of data being analyzed. While there are several different types of processes that are implemented based on individual data nature, the two broadest and most common categories are “quantitative analysis” and “qualitative analysis”.

Concerning qualitative analysis, data is described through the use of descriptive context, like text; instead, quantitative analysis refers to a set of processes by which numerical data is analyzed. While qualitative data must be “coded” (techniques like observations, documents and interviews) ,in order to facilitate the grouping and labeling of data into identifiable themes, quantitative data is tipically measured by visually presenting correlation tests between two or more variables of significance.

There are same few easy steps that you can bring while interpreting and analyzing your data:

  • By collecting the data, make it as clean as possible –> avoid keeping information that you wont use.
  • Choose when to use a quantitative or qualitative analysis and apply the methods respectively to each.
  • Think about what you want/have to capture from your data:try to look at it from different perspectives, not only yours, but also the ones of the other people involved in the project.
  • Be aware of false information, wrong collected or inaccurate data.
  • Deliver your analysis in the easiest possible way: visualization is a good choice in this case, i.e. using dashboards.

Data dashboards are merging the data gap between qualitative and quantitative methods of interpretation of data, through visualization. They can literally help and prevent the major errors of interpretation and morever, once they are implemented, you can get from them as much information and insights as you need in very short time, helping you achieving your goals or preventing future errors.


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