What is Descriptive Analytics?

Definition:

Descriptive analytics is a statistical method for searching and analyzing historical data to uncover patterns or meanings.

Business metrics, reports, and KPIs (Key Performance Indicators) are produced by descriptive analytics to assist firms in tracking their performance and trends. As a result, firms have a better understanding of what has happened so far and, when paired with other types of business analytics, have a better understanding of why things happened, what might happen in the future, and how to prepare for it.

How does descriptive analytics work?

Data aggregation and data mining are two descriptive analytics techniques for discovering historical data. To make the datasets more manageable for analysts, data is first gathered and organized through data aggregation.

Descriptive analytics breaks down into five steps, including: 

The five steps of descriptive analytics are as follows:

1. Identify the Key Performance Indicators (KPIs) for your company.

The organization must determine the metrics it wishes to generate based on the company's overall goals or the main business goals of each group inside the company.

2. Identify the Information You'll Need:

The organization must then locate the data required to calculate the necessary metrics. Because the essential data may be distributed over multiple files and apps, this task may be difficult.

3. Data Extraction and Preparation:

If the required analytical data comes from several sources, extracting, merging, and preparing the data for analysis might be time-consuming. This is, however, a critical step in ensuring correctness. Advanced data analysts employ data modeling, a framework embedded in information systems that aid in the preparation, arrangement, and organization of a company's data. Data modeling is the process of defining and formatting complex data in order to make it useable and actionable.

4. Examine the Information:

Companies can use a variety of tools to do descriptive analytics, including business intelligence (BI) software and spreadsheets like those available in Excel. In most cases, descriptive analytics entails applying basic mathematical operations to one or more variables.

5. Data Representation:

All that remains after business analysts have completed all of the essential stages is to deliver the data. However, the data must first be presented in such a way that everyone can understand it, from stakeholders to finance experts. Stakeholders frequently prefer reports that are visually appealing, such as bar charts, pie charts, or line graphs. Data that is visible is easier to comprehend. Finance professionals, on the other hand, may prefer data presented in figures and tables.

What can descriptive analytics tell us?

Descriptive analytics gives businesses essential information about how it’s doing, where it’s going, and how it’s stacking up against the competition. But there’s much more to the story. 

So what does this tell companies and aspiring professionals in the field?

1. The company’s current performance

2. The business’s historical trends

3. The company’s strong and weak points

Example:

Descriptive analytics can be used to monitor progress toward objectives. Reporting on progress toward key performance indicators (KPIs) might assist your team figure out if they're on track or if they need to make changes.

If your organization's goal is to earn INR.500,000 as monthly revenue, for example, you may utilize sales statistics to indicate how you're progressing toward that goal. You may have generated INR. 200,000 by the middle of the month. This would be underperforming because, at that point, you should be halfway to your objective at INR. 250,000. This descriptive study of your team's success might help you dig further into what you can do differently to boost sales and get back on track to meet your KPI.

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