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Data Interpretation and Data Analysis is the process of ordering, structured, and giving meaning to the collected raw data. Well, it has a very important and crucial role in the business as it helps in making decisions for a business owner regarding the growth of the business.

Data interpretation and data analysis have now taken first place in the digital age. Increasing data in business can be so frightening if it is not taken care of properly. With this growing data source, you can be able to have an insight view of the business, easily manage the business flow, identify the upstream and downstream of a company, and more.

What Is Data Interpretation?

Data interpretation is a process of filtering valuable information from large amounts of data sets. The collection can be represented in various forms such as bar graphs, line charts and tabular forms, and other similar forms and requires some interpretation to present such forms.

Looking at life scientifically, we can say that life is about data. We are all surrounded by big data that becomes necessary for businesses to carry out their tasks. Almost every operation, from sales reports to trends to budget planning, requires some calculation or other.

In the blog below we will discuss important techniques and examples of data interpretation and we will also see how we can make sense from graphical data and its other forms.

Explanation of the term Data Interpretation?

Here first we discuss the word “Data” and “Interpretation”

The data is based on facts and figures collected for reference or analysis. Data is usually formatted in a specific way and it can exist in many types of forms, such as numbers, text, etc., which helps us to compare data and draw conclusions.

Interpretation is the act of explaining, re-framing or otherwise showing your own understanding of something.

Data Interpretation:

Data interpretation is a process of analysis bunch of data into a meaning full information. It is done to draw a conclusion from the given set of data. There have been different statistical tools are used to represent data into an organized format.

data interpretation

How to Interpret Data?

Use the checkpoints below to learn how to organize these well-structured data and conduct your data analysis to cover these four topics. But before you know how to gather and analyze your data and how to set up the system, its important factor is to gather and keep track of what you are learning.

data interpretation

Step1- Organize and cleaning data

The best practice should be to track and monitor the collected data. The size and complexity of data organizing and cleaning depending on how much data you will be collecting.

For smooth work, it is important to put data into a standard format or templates that can help you in analyzing phase. Make sure your data is inconsistent format because multiple people are entering data and may chance of data clustering. Cleaning data process involves reviewing a data, identify if anything is incomplete, data not understandable, and out of line in another way.

Step2-Analysing Phase

Analyzing the data can be simple or complex depending on the type of data you have and what you want to be able to say about the data.

Analysing phase is a process of transforming and modelling a data to discover a useful information for decision making.

Types of data analysis:

  • Text Analysis
  • Statistical Analysis
  • Diagnostic Analysis
  • Predictive Analysis
  • Prescriptive Analysis

data interpretation

Step3-Interpret data and develop conclusion

The next step is Data Interpretation and how to develop a conclusion. Here interpret data to define how well we are understanding from the collected data. It is a process of giving sense to a data that has been collected, analyzed and presented.

The common method of accessing numerical data is known as statistical analysis, and interpreting data in order to make predictions is known as inferential statistics.

To make sense of data, review your data for patterns, trends, or themes that help you tell a compelling story about your program of an organization. for example:

  • Compare your results with several aspects so that you meet a defined goal.
  • Define the trend of program data from the beginning to the end of the program. So, the data you collected at one point in time against data that was collected in the same way at another point in time.
  • Compare your data with other similar programs.
  • Check for boundaries – (g., high numbers, low numbers, or unique perspectives).

The conclusion from data can be clear if the data provides an answer to your evaluation questions. It can be difficult if an answer is not relevant to your question or less apparent output.

Step4-Examine the data and document the limitation

Identifying evaluation limitations has been an important part of the interpretation of data. In this process, any factor affecting your result can be ignored. Such as poor response rates, inconsistent data or biases that could be introduced.

Importance of Data Interpretation

The importance of Data Interpretation are numerous and can benefit your business in a different aspect. They are mostly used for decision making and predicting upcoming trends and business competition.

Data analysis and interpretation, regardless of the method and qualitative/quantitative situation, can include the following features:

  • Comparing and contrasting of data
  • Data identification and explanation
  • Identification of data outliers
  • Future predictions

Below is few importance of Data Interpretation:

Informed decision-making:

For your business progress, it is important to make an informed decision and gain knowledge that helps you achieve a competitive strategy over your competitors. According to studies, only one-third of companies are 10% productivity and 8% profitable when it comes to making informed decisions about their data. The best way to get started with the data analysis process is to define goals and identify a problem at an early stage.

Identifying trends:

Data gained from the market and consumers are used for analysis to predict the future market trends. By identifying the industry trend provides better insights and in-depth visibility on industry purpose.

It helps customer buying patterns and evaluate consumers requirement. This way gathering data and interpretation process can lift your business growth and provide high ROI and process of collecting, interpretation and analyzing the data become easier and done carefully.

Cost efficiency:

When the process of data interpretation is done properly, it provides the business with various cost benefits. Data analysis has the potential to alert management to cost reduction opportunities without any significant effort from human capital.

Data Interpretation types

Data Interpretation – Pie Chart

A pie chart is also known as a circular chart which is divided into the sector. Each sector defines a proportion or percentage of a quantity.

data interpretation

Data Interpretation – Column Chart

A column chart is one of the most important data interpretation charts. It is also called a bar chart. A bar chart is a visual depiction of a rectangle bar and its length depends on the volume. They can plot vertically or horizontally.

data interpretation

Data Interpretation – Line Chart

Line chart are used to compare progress of two quantities. For example, its use to compare two countries sales, performance of two stocks in the last quarter etc.
data interpretation

Data Interpretation – Scatter Plots

Scatter plots are used to display bivariant data that means measures of two different variables for each subject.

data interpretation

Data Interpretation Problems

data interpretation

Correlation mistaken for causation:

Our first misinterpretation of data refers to the tendency of data analysts to combine the cause of an event with correlation. It is the belief that because two actions occurred simultaneously, they were caused by each other. This is not accurate because actions can simultaneously absent a cause-and-effect relationship.

Confirmation partiality:

This is the second misinterpretation of the data, partly reflecting the confirmation of the work done. It is confirmed that the information which is not complete should not be sent to the team for further analysis. This leads to incomplete or irrelevant information that will not help in making good decisions.

Irrelevant data:

The third misinterpretation of the data is providing irrelevant data to the team for analysis. The source of data in business is many, but the team must refine the data before proceeding. This is the initial step and must be taken care of before proceeding as all the action steps are dependent on the data being analyzed.



Abhishek Sharma

Abhishek Sharma

Software Developer

Abhishek is working as a Web Graphics Designer at EzDataMunch. He is involved in Maintaining and enhancing websites by adding and improving the design and interactive features, optimizing the web architectures for navigability & accessibility and ensuring the website and databases are being backed up. Also involved in marketing activities for brand promotion.

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