Data Interpretation

Data Interpretation definition

While searching for some data you get some unnecessary information too. The process of extracting useful data out of a collection of data through some process is called data interpretation. The data could be available in various forms like stats, graphs, line charts or tables hence data interpretation is important.

Data Interpretation Methods

We have professionals in this field of data interpretation. These analysts gather information on others behalf and make it understood by them as it is difficult for some people. For example, if an entrepreneur is pitching an idea to an investor he needs to interpret data about some basic information like market size, market value, growth rate, etc.

There are two main ways in which such data interpretation is done.

Let’s see what these are in detail.

Methods of data interpretation

• Quantitative Data Interpretation Method:

Quantitative Data deals with numbers. It is thus not analysed with texts. Rather there is an inclusion of stats and numbers for generating the analyses. 

The following are the statistical measures that enable in interpreting Quantitative Data:

• Mean of a Data:

To generate the mean value of a data, one needs to take the total of all the values against the observations and then divide it by a total number of observations. Basically, the average found from all the set of values numerically is termed as a mean of a data.

• Median of a Data:

After arranging the numbers in their descending or ascending order, its midpoint needs to be calculated. The midpoint value obtained is termed the median of a data. 

• Mode of a Data:

Mode is basically the highest common number amongst the other values in a set. There is a possibility that one data set has 1 or more than one mode value. 

• Frequency:

Frequency refers to the number of times a value is obtained or found in a data set. 

• Range:

Range is defined as the lowest value and highest value number amongst the set of values. 

• Qualitative Data Interpretation Method:

Qualitative data is any information that is not numerical, for example, video transcripts, class notes, audio recordings etc., photographs, and text documents such as articles and opinions. The following five categories apply to qualitative data interpretation:

1. Content analysis and interpretation: This is the process of subdividing behavioral data in order to classify, summarize, and tabulate the data for easier comprehension.

2. Narrative analysis and interpretation: This strategy entails rewriting respondents’ stories in light of the context of each case and the unique experiences of each responder. In other words, narrative analysis is the process by which researchers revise primary qualitative data.

3. Discourse analysis and interpretation: This is a technique for analyzing naturally occurring and simple dialogues, as well as all sorts of written material.

4. Framework analysis and interpretation: This is a significantly more sophisticated technique that entails numerous stages, including familiarization, identification of thematic framework, coding, charting, mapping, graphing, and interpretation.

5. Grounded theory: This approach to qualitative data interpretation begins with an examination of a particular example in order to develop a theory. Following that, other cases are analyzed to determine whether they conform to the theory.

The best part of data interpretation is visualizing. By visualizing your data, you make it easy to understand by all. It also helps in providing clarity while comparing. Whereas it also adds creativity to your data. Let’s discuss the different techniques to visualize data while performing data interpretation.

Techniques of Data Visualization

1. Column Chart: The column chart is also called a vertical bar chart where each category is represented by a rectangle. The height of the rectangle represents the value.
2. Bar Graph: Bar Graphs have rectangular bars in which the lengths represent their values.
3. Stacked Bar Graph: It is a bar style graph that has various components stacked together so that apart from the bar, the components can also be compared to each other.
4. Stacked Column Chart: Stacked column is similar to a stacked bar. here the data is stacked horizontally not vertically.
5. Area Chart: It combines the line chart and bar chart to show how the numeric values of one or more groups change over the progress of a viable area.
6. Waterfall Chart: With the help of a waterfall chart, the increasing effect of sequentially introduced negative or positive values can be explained.
7. Bubble Chart: Bubble chart is a multivariable graph that is a combination of Scatter Plot and a Proportional Area Chart.
8. Line Graph: In this graph, the data points are connected through a straight line. Representation of the changing trend is done using Line graphs.
9. Mekko Chart: Mekko can be called a two-dimensional stacked chart with varying column widths to represent values.
10. Pie Chart: Pie chart is a chart where different components of a data set are presented in the form of a pie which represents their segment in the entire data set.
11. Scatter Plot Chart: It is also called a scatter chart or scatter graph. Dots are used for representation of values for two different numeric variables.
12. Bullet Graph: It is the same as a bar graph but with a variation. A bullet graph is used to exchange dashboard gauges and meters.
13. Funnel Chart: The funnel chart determines the flow of users with the help of a business or sales process.
14. Dual Axis Chart: It combines a column chart and a line chart and then helps in comparing the two variables.
15. Heat Map: Heat map is a technique of data visualization that displays the level of instances with color in two dimensions.

Conclusion

Data interpretation is a very important topic. You use data analysis and interpretation for almost everything when you need to explain something to someone with exact figures. We discussed data interpretation with a type of data implementation and also had a good look at data interpretation questions.