Data Interpretation

What is Data Interpretation?

Figures, statistics and statements related to any event or topic are called Data. Data as such makes very little sense unless it is organised and then analyzed properly to reveal an overall picture of the situation. The act of organizing and interpreting data to get meaningful information is called data interpretation (DI). Data interpretation is the process of reviewing data and arriving at relevant conclusions using various analytical research methods. Data analysis assists researchers in categorizing, manipulating, and summarizing data to answer critical questions.

Need for Data Interpretation

Data interpretation is a vital cog in the process of decision making. As already stated, haphazard data makes little or non sense. To manage resources effectively, the top management needs to take important decisions based on statistical data. Top management people rarely find enough time to go through the entire details of any report. Hence, data needs to be presented in such a manner that they are able to cull out the required information with the least effort and time. Thus, effective organisation and presentation of data is of prime importance.

Data Interpretation Vs Problem solving

In problem solving each question has a basic concept and a specific methodology for solving it. DI requires only the basic concept of arithmetic and statistics. It is predominantly concerned with comparison of numbers and not formulae as such. In problem solving, exact data is given but in DI culling out the requisite data is the first step.


Different kinds of formats are used for presenting data and each kind has it’s own advantages and disadvantages. However, before we go into the detail of each format, let us take a look at the basic process of data interpretation.

Interpretation of data involves one or more of the following steps:

  • Observation
  • Calculation & analysis
  • Making suitable deductions.

Observation based problems require one to scrutinize, pinpoint and sort out the relevant data that is required to answer the precise query. In case of graphs, this requires us to read off the data values against the scales shown. Observation skill is especially important when quantum of data is large or when we need to refer to more than one table or graph simultaneously. Calculation based problems require us to calculate sum totals, fractions, percentages, ratios & proportions, averages, growth rates etc. from the data given. Hence, one needs to be thorough with these basic topics. Approximation skill is also very helpful, since it simplifies calculation without sacrificing too much of accuracy. A good many problems can be correctly worked out using approximation techniques.

During calculation, we may also be required to convert from one unit of measurement to another. Hence, one must look very carefully at the units of measurement given for tables/charts, unit of the query and unit mentioned in the answer options and make them consistent before proceeding with calculation part.

Deduction based problems require us to work out some critical unknown parameters based on reasoning.