What is Data Interpretation? – Part 2

Data Interpretation part 2

Table of Contents

In a previous blog, I had deeply explained the meaning of data interpretation, examples, and importance. Now I will be explaining how to interpret data, the differences, and methods.

Read More: What Is Data Interpretation? – Part 1

As you all know, interpreting the data seems to be difficult. But only with the right instruments and software, such as Tableau or Hypothesis. It is helpful to gauge the problem and make the perfect decision.

Key Takeaways

1. How to interpret data?

2. Difference between Data Interpretation and Analysis

3. Methods of Data Interpretation

How to Interpret data?

An analyser must endeavour to differentiate between correlation, determinism, and coincidence, as well as several other biases when reviewing data, but the person must also consider all of the factors that may have influenced the outcome.

Data interpretation’s goal is to help individuals make sense of mathematical data that has been gathered, analysed, and presented.

If an analyst team has a baseline approach (or methodologies) for data analysis, they usually have a paradigm and a consistent foundation. Indeed, if different departments use different methods to evaluate that very same data while pursuing the same objectives, mismatched objectives may result.

Diverse approaches will result in duplication of work, inconsistency in solutions, wasted energy, and, ultimately, time and money. We’ll look at the two basic types of data interpretation in this section: a qualitative and quantitative analysis.

  • Qualitative Analysis -Category research methodology is categorical data analysis in a nutshell. In qualitative analysis, descriptive context is used to characterise data rather than numeric values or groupings (i.e., text). Narrative data is often gathered through a variety of one-on-one interactions.

Some of the techniques are 

  • Observation
  • Group Focus
  • Secondary Research
  • Taking Interviews
  • Quantitative Analysis – If there was a single word to describe quantitative data interpretation (and there isn’t), it would be “numerical.” 

When it regards data analysis, there are few guarantees, and then you can be confident that if the research you’re doing doesn’t involve numbers, it’s not quantitative.

A set of processes for evaluating numerical data is known as quantitative analysis. Statistical modelling approaches such as mean difference, mean, and median are usually required.

Some of the statistical terms are 

  • Mean
  • Median
  • Standard Deviation
  • Frequency Distribution

Quantitative data is typically evaluated by visually presenting correlation tests among two or more significant variables.

Different procedures can be combined or utilised independently, and make comparisons to get a conclusion. Other quantitative data interpretation procedures that have their distinct signatures include,

  • Cohort Analysis
  • Predictive Analysis
  • Conjoint Analysis
  • Cluster Analysis
  • Prescriptive Analysis
  • Regression Analysis

By now you might be slightly confused between data Interpretation and Analysis, right?

Don’t worry! I will explain it to you.

Difference between Data Interpretation & Analysis

Analysis denotes the use of the scientific method to get a result that can be replicated by others evaluating the same facts. 

Interpretation, on the other hand, implies qualitative rather than quantitative inference, involving the analyst’s creative skills, yet it is much more susceptible to subjective biases.

What is Analysis and Data Interpretation?

The process of attributing meaning to the acquired data and deciding the conclusions, relevance, and consequences of the findings is known as data analysis and interpretation. 

When used in combination with the mean, the standard deviation allows for a better comprehension of the data.

Now let me tell you about the methods of Data Interpretation.

Methods of Data Interpretation

data interpretation

Analysts use data interpretation strategies to assist individuals in understanding statistical data that is collected, evaluated, and presented. 

When data is obtained in its raw form, it might be difficult for laymen to grasp, which is why analyzers must deconstruct the information so that others can understand it.

When appealing to potential investors, for example, entrepreneurs must analyse data (e.g. economic size, rate of increase, etc.) for a better understanding. Quantitative methods and qualitative methods are the two main approaches to doing so.

Quantitative Method 

The data type contains numbers but not texts. This method is also called numerical data.

Quantitative Data is further divided into 2 main types; continuous and discrete. Continuous Data is further divided into ratio data and interval data. These categories are all numeric.

The process of investigating quantitative data involves statistical modelling techniques such as Mean, Median, and Standard Deviation.

Qualitative Method 

This method only uses texts, not patterns/numbers, to describe the data. The qualitative method is also called a definite method.

Compared to quantitative research, qualitative data is typically collected utilising a variety of person-to-person procedures, which can be challenging to interpret.

Unlike quantitative data, which can be studied immediately after collecting and sorting it, qualitative data must first be encoded into numbers before being analysed. 

This is due to the fact that analysing texts in their original condition is frequently time-consuming and results in a high number of errors. 

The analyst’s coding should also be published so that it could be reproduced and evaluated by others.

Nominal and ordinal data are the two basic forms of qualitative data. These two data types are evaluated in the same way, although ordinal document analysis is much easier than nominal data interpretation.


Data interpretation is critical since it aids in the acquisition of valuable data from a stream of irrelevant information while making educated judgments. Individuals, organisations, and researchers have found it valuable.

The method for data interpretation is often time-consuming, and it should inevitably become more complex as the amount of data produced daily increases. 

Analysts have changed accordingly it is easier to comprehend data as advanced analytics and learning algorithms become more accessible.

Frequently Asked Questions

1. What are the 3 steps in interpreting the data?

When dealing with data, these are the three steps you need to follow; Analyse, Interpret, and Present.

2. What is the difference between data analysis and interpretation?

Analysis denotes the use of the scientific method to get a result that can be replicated by others evaluating the same facts.
Interpretation, on the other hand, implies qualitative rather than quantitative inference, involving the analyst’s creative skills, yet it is much more susceptible to subjective biases.

3. Why is interpretation important?

Interpretation is to establish explanatory concepts that can help for future research.

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