What is Descriptive Analytics and how does it summarize past data in a simple way?

The term Analytics means analyzing the patterns or the trends of the data. Since data comprises patterns it is important to analyze them so that the companies can harness the results to gain profit. The term descriptive means description.

In this article, we will discuss What is Descriptive analytics, the Tools used for Descriptive analytics, how they work and How can it able to benefit other companies.

What is Descriptive Analytics ?

What is Descriptive Analytics?

Descriptive Analytics is a statistical technique that is used to interpret past data. Using Descriptive Analytics one can conclude the historical data and use the summary for other purposes. Descriptive Analytics is very useful as it helps us to understand the data. For example in stock markets reports are generated about the trend of each stock, how it is performing daily etc. This analytics is also used in social media nowadays. However, it must be noted that by using Descriptive Analytics one can understand the summary of past data. It cannot make predictions of data. For making predictions about future data one has to use predictive analytics to do the same.

There are four types of analytics: Descriptive, Diagnostic, Predictive, and Prescriptive. Let’s discuss each of them in detail:

• Descriptive: Getting a summary of the past data.
• Diagnostic: examining the reason for the occurrence of such patterns in the data
• Predictive: Analyse the past data and predict the future.
• Prescriptive: Summarize the results and provide suggestions accordingly

What tools can be Used for Descriptive Analytics?

Many tools are available for Descriptive Analytics. Some of them are as follows:

• Microsoft Excel: Microsoft Excel is a powerful tool that allows us to arrange the data in the spreadsheets. It is a powerful statistical tool and can be used to create graphs, pivot tools etc.
• Tableau: Tableau is a powerful data visualization and business tool. It is used to create real time and interactive dashboards. It can handle large datasets in real time. Some of the Tableau Versions are Tableau Public, Tableau Desktop, Tableau Server, Tableau Online.
• Power Bi: Power Bi is another data visualization tool introduced by Microsoft. It is a business tool that allows to create interactive business visualizations.
• Zoho Analytics: It is a business tool introduced by Zoho. It helps to create interactive visualizations using drag and drop techniques. One can create interactive dashboards with a few clicks.
• R: R is a low level statistical programming language. One can perform descriptive analysis using the powerful libraries of R. We can also create interactive plots using ggplot package.
• Python: Python is a high level, object oriented programming language that has English like syntax. Python is a powerful language as it has wide range of libraries. Using Numpy and Pandas one can perform Exploratory Data Analysis. Python also provides powerful data visualization libraries like Matplotlib, Seaborn, Plotly etc.

How does Descriptive Analytics work?

As we all know Descriptive Analytics provides results by using statistical techniques. Lets go through the steps:

• First collect the data from various sources. Sources can be excel sheets, CSVs, surveys etc.
• Since data can be noisy in nature, clean the data. Fill or remove the missing values. Use the techniques of normalization to transform the data.
• Use techniques like Measures of Central Tendency, Variance, Frequencies etc to get the summary about the past data
• Then perform Exploratory Data Analysis. In this technique bar plots, pie charts and other visualizations are used.
• Then from the calculations and the visualizations create a report and provide a complete description about the dataset.

Summarizing Past Data using Descriptive Analytics

One can summarize the past data using Descriptive Analytics as it is the foundation of data analysis. It makes use of Descriptive Statistics which includes Central Tendency: Mean, Median and Mode, Quartiles, Percentiles, Skewness etc. The five steps are as follows:

• Data Collection: This is the first step and in this data is collected from wide variety of sources. The data can be in definite format like spreadsheets, database tables, CSVs etc
• Data Cleaning: This is the second most important step as it helps to deal with missing values or inconsistent values. It also helps to structure our data in a precise manner so that it becomes easier to manipulate the data.
• Exploratory Data Analysis: In this step, statistical techniques are used to explore the insights about the data. It helps to summarize the main characteristics of the data.
• Data Visualization: After analysis data is visualized in the form of charts, graphs. Some of the commonly used visualization techniques are Bar Charts, Pie Charts, Histograms, Line plots, Box Plots etc.
• Summarising the data: After following all the steps , the whole of the summary is drafted using reports or presentations so that the client can understand it quickly.

Advantages of Descriptive Analytics

The advantages of Descriptive Analytics are as follows:

• It is used for summarising data.
• It helps to identify patterns and trends in the past data.
• One can identify the outliers in the historical data.

Disadvantages of Descriptive Analytics

There are many advantages of Descriptive Analytics however there are some drawbacks as well:

• Although getting inference, we cannot predict the future trends. For this one needs to use predictive analysis.
• Descriptive Analytics can be sensitive to outliers. For instance suppose we have a dataset which has a large number of outliers. Using techniques like Mean can provide inaccurate description.
• Descriptive Analytics provide summary about the past data however in some scenarios the future trend can differ from the past trend. In these cases misinterpretation can happen.

How Can Companies Benefit From Descriptive Analytics?

Nowadays the companies mine data so that they can draw the inference from the data and accordingly take measures so as to produce the output which will ultimately lead to profit. As we all know Descriptive Analytics involves dealing with the past data, companies can use this technique to identify the trends, outliers and accordingly take the steps. This technique ensures that the companies can understand the data easily and interpret it. Descriptive Analytics give a clear picture about the metrics and key performance indicators. Analysis also helps in decision making and mitigate the risks in future.

Conclusion

Descriptive Analytics acts as the building blocks for other analytics. When companies collect data, they need to understand the features and patterns. Here Descriptive Analytics comes to the rescue and helps to monitor the progress and business health.