There is data everywhere. Digitalization would be completely impossible without data. Any action that you are trying to perform on a computer or a mobile device indirectly calls for data. If there is no data stored in any of the software libraries, all processes will fail.

Earlier manual data used to be collected for analyzing. Data interpretation (DI) is a set of protocols to review particular data, the evaluation of which is used for making decisions, or ensure a clear inference about calculations. 

Why is Data Interpretation a significant process?

Data Interpretation can be extensively descriptive as data is accumulated from various sources, which lead to a disorderly arrangement for analysis. Data varies from one field to the other, and correlating them appropriately under the right categories is an absolute necessity. This need mandates DI.

How can DI be implemented?

The breadth of DI being vast, there are several methods by which data can be interpreted. While surveying data, an analyst or surveyor needs to contemplate many other factors from which that data arrives and what led to the generation of data. Careful examination of data results should be studied. To achieve this, we can very broadly classify data interpretation techniques into two categories – Quantitative analysis and Qualitative analysis.

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Quantitative Analysis

As the name goes, the quantity or quantum is scanned. Mostly arithmetic data is examined in such cases. Numeric data like several buses in transportation, cost of particular products in different places, polls, etc. can be analyzed.

In some cases, it can be a true or false value. Either case, results of data have a definite value to be scrutinized. Statistically, quantitative research involves the calculation of average, deviations from normalcy, and its frequency in a system.

Also, the correlation of numeric data warrants processes to establish the relationship between two variables. Regression analysis is the most commonly used method to accomplish this. 

• Average: Averages are estimated using the three apparent ways – Mean, median, and mode. Mean is said to be the most accurate of all three. Median is the value of the centric data or the average of two values that appear in the middle of the rest when arranged ascending orderly. 
• Standard Deviation and variance: After arriving at the mean, the standard Deviation is a formula to derive at how values are dispersed surrounding the mean. It describes the level of consistency and variation (inferred using variance). Understanding how scattered the data is from the statistical mean is important for further analysis of data.
• Frequency distribution: Number of times of appearance of the value in a distribution scale is known through frequency distribution. The most happening and least happening events or occurrences can be made aware of through this method. For example, if a test like distribution of a species in nature in a particular region is to determined, conclusions like rarest, rare, thinly populated, densely populated can be made under different sets randomly over a spread of area. Other conditions and biases need to be considered as well, like habitat, climate, life span, etc. Often such data are represented with tables, and interpretation of data is simplified and channelized.

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Qualitative Analysis

Non-numeric data like opinions may be analyzed through qualitative analysis. In some cases, it may be converted to numerical values by assigning merits.

Typically, illustrative descriptions are handled. Observation of details, documenting them, and surveying them is primarily used techniques for qualitative data analysis. Interpretation may often be pictorially represented.

Qualitative analysis is mainly performed to resolve ambiguities on judgments. The data is presented by grouping and labeling them into logical sets.

• Bar diagrams: Comparison of two quantities on their proportionalities using rectangular bars comprises the bar graphs. The height or length of each bar indicates prevalence or the degree of occurrence of a particular event. It is very widely used as a reliable resource of DI.
• Tables: The most primitive and still working systems are tabular columns. Arranging data into several rows and columns in a corresponding way portrays data ideally to reviewers. 
• Pie charts: It is the most efficient technique to symbolize the spread of data from the whole set of the data circle. It is quite often used to demonstrate the most and least preferred items in a distribution set.
• Line graphs: Line graphs are very commonly used with two values, with one of them dependent on the other. It is the best way to constitute data and can be easily read, especially when trying to interpret a variable over a certain period of time.

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Types of Format

Competitive exams focus on similar types of formats and here are some most common and basic ones.

1. Pie Charts: Pie charts are a diagrammatic representation of data that is presented in a circle. The circle is randomly divided into different parts, and the circle presents the total value. These types are mostly focused on the degrees of an angle, values of the parts, or the total sum of the value of all the parts.
2. Table DI: Table DI, according to the most, is easiest from where data can be interpreted. It is also a convenient way to represent almost any kind of data into a tabular form, which consists of placing data into rows and columns.
3. Line Graphs: Line graphs are where lines are used to connect the various points of data that change over time, which as a result, the line would look like a vertical random zigzag line. Depending on the types, there can be one single line or can be multiple lines, represented by different colors and representing different items.
4. Bar Graphs: Bar graphs use various heights and lengths of rectangular bars to represent the values different from data. The bars can be both horizontal and vertical, and it gets easier when you know the fact that the longer the bar, the greater is the value of the item it is representing.
5. Mixed DI: These are also known as combination DI. A combination of different other formats such as pie chart, bar graph, line, etc. is used in a particular one. Compared to the previous one, it can be a little time consuming for some.

Where to start Data Interpretation

Most of the students worry about this topic, but honestly, there is not much to it. If you have grasped the basics of your arithmetic topics and practiced them, then all you need is reading and understanding skills. Remember, it is about interpreting data, and rest are all basic calculations, and the simplest ones include the addition and subtraction as calculations. 

All you need to do is first read the question carefully, understand which data are important and which are just to confuse you. Read what questions are asked based on the data because you only need to note down only data that matters — not all. With practice, you will adapt a sense to know which question to skip and on which to invest time. 

Both types of data analysis are widely used and understandable. Choosing of which method needs to be used based on the type of data is important.

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