A Definitive Guide to Descriptive Analytics (With Examples)

By Indeed Editorial Team

Published 10 May 2022

The Indeed Editorial Team comprises a diverse and talented team of writers, researchers and subject matter experts equipped with Indeed's data and insights to deliver useful tips to help guide your career journey.

Businesses often change over time, which can affect a company's profits, target market and growth potential. Descriptive analytics provides a way for business analysts or company leaders to better understand progressive or regressive changes and how they've affected a company's development. Understanding how to use descriptive analysis can help you identify important trends that can guide a company's strategies. In this article, we define what this type of analysis is, review the steps of this analysis process, explore its benefits and provide examples for reference.

What is descriptive analytics?

Descriptive analytics is a method of interpreting historical data with the purpose of understanding how a business has changed within a certain period. Unlike other analysis methods, it solely focuses on what has already happened and doesn't use data to make predictions or infer various conclusions. Businesses can use it as a starting point for other analyses. It's a relatively simplistic form of data analysis and only uses basic mathematical concepts, such as averages, percentages and simple statistics.

This kind of analysis depends on a larger and broader range of data to create greater accuracy in analyses. This data helps analysts or senior management compare very specific events or changes in a company's development over time, which can generate more actionable data. A company's strategic decision-makers can use the results of this analysis to identify strengths and weaknesses and alter ongoing strategies or implement new ones.

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Prescriptive analytics

While analytics from a descriptive standpoint can be easier to understand, this kind of analysis may not provide all the information a company needs. Another type of analysis, called prescriptive analysis, is popular for its ability to integrate data from other sources into the analysis process. This helps the person performing the analysis compare a more diverse data set and potentially predict short-term outcomes of specific actions or changes made by a company. Such predictions may help businesses make more informed management and strategical decisions to maximise positive outcomes and minimise more unfavourable outcomes.

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How to conduct a descriptive analysis

When conducting a descriptive analysis, analysts typically follow these steps:

  1. Choose business metrics: Choose the metrics that evaluate how a business has performed over a specified period compared to business goals that were previously set, such as increasing profits or improving operational efficiency. This step is usually crucial for the success of descriptive analytics, as everyone involved understands what data is being analysed and what the analysis aims to measure.

  2. Collect necessary data: After deciding on what metrics you're comparing, you can source relevant data that match them. Ensure to organise your data so it's easier to read and understand where each data set comes from and why it's necessary for the analysis.

  3. Prepare data: Before analysing any data, ensure data from different sources is in the same format and you can easily compare them.

  4. Analyse data: Once you have all your data correctly organised, you can start analysing it with the help of various tools, such as regression analysis, pattern tracking, basic statistics and clustering. The main purposes of the analysis is to identify various patterns in your data set and to observe how well a company has achieved its key performance indicators and whether any specific decisions had any noticeable effects on a company's performance and development.

  5. Present data: After analysing your data, it's important to format it so stakeholders can easily understand the results of your analysis. You can present statistical information to non-experts with the help of visual tools like graphs and charts, so trends are more visually noticeable.

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Benefits of using a descriptive analysis

Some of the main benefits of using descriptive analysis are:

  • Assessing an organisation's current goals: By clearly identifying a company's key business indicators, you can assess the effectiveness of various processes designed to achieve those goals. By identifying various patterns, you can determine if an organisation is on track to meet its goals regarding revenue, organisational effectiveness and other relevant metrics.

  • Putting current actions into a historical context: An analysis can help you look into the company's past decisions and understand certain ways in which customers interact with products. You can then use this information to create more comprehensive strategies.

  • Understand the effectiveness of various changes in the way a company operates: Most businesses operate within a complex network of suppliers, customers, contractors and other involved parties. By using descriptive analysis, you can observe how various changes, such as changing a supplier, introducing a new product or altering prices, affect a company's operations.

  • Detecting various procedural flaws and performance issues early: By constantly performing this kind of analysis, you can notice how certain procedures and actions are detrimental to an organisation's financial health and resolve them as quickly as possible.

  • Helping compare various periods in an organisation's past: You can use descriptive analysis to help you analyse different periods and compare them in terms of key performance indicators. If some periods are significantly more or less prosperous than others, you can use the data gathered through descriptive analytics to find the exact reasons for success or lack thereof.

  • Making understanding data simpler: This kind of analysis typically presents data in simple and easy-to-understand terms, allowing any interested party with access to this information to understand its conclusions easily.

  • Providing a foundation for other analyses: This kind of analysis also provides an important foundation for other analyses. For example, it may help a management team narrow down a specific issue with historic production to see why a company's manufacturing output increased for six months and then dropped significantly for nine months.

  • Driving business changes and structures: The results from a descriptive analysis can also help influence company changes and internal structures by identifying key areas for improvement.

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Examples of using a descriptive analysis

Here are examples of descriptive analysis you can reference:

Example 1

This example explains how a company may use this type of analysis to review its revenue streams and learn about a sudden change in profitability in the previous year:

Derring Automobiles produces high-end luxury vehicles in their factory. Since January, the company has profited from these operations with a 20% profit margin and continued to be profitable until May. In June, the company recorded a profit greater than in previous months, which initially seemed positive, but upon further inspection through descriptive analysis, the company's financial analyst found that the profit margin for June had suddenly decreased to 10%. This analysis helped the company notice a sudden decrease in profit margin and perform further analysis to determine the cause.

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Example 2

This example shows how a company may use this type of analysis to study historical data to understand why the demand for their core product is very inconsistent:

Hot Charcoal sells charcoal and grills for people to barbecue at home. The company encounters an issue where they either have too much extra charcoal and are required to rent space to store it or they're barely producing enough to keep up with demand. To resolve this issue, the company performs several analyses, revealing consumer behaviour that differed from expectations.

The demand for charcoal drops sharply around the middle of August instead of the end of September and increases again in April instead of May. By continuing normal production through September, the company produced too much charcoal. By predicting the start of the barbecue season as May instead of April, they didn't produce enough for April.

Example 3

This example shows a company forming a foundation for other analyses with this type of analysis:

Dan Chang Knives produces high-quality steel kitchen knives. Management wants to understand why demand dropped significantly during what's historically a high-demand season for kitchen knives. The company performs a descriptive analysis to identify the root issue. They analyse the quality of their products and find that consumer demand decreased shortly after the company switch to a new supplier. Further analysis allowed the company to find out that the new supplier had been sending them sub-par iron ore. With this information, the company uses prescriptive analysis to predict higher demand if they change vendors and return to using high-quality iron.

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