Descriptive analytics is one of the four main types of data analytics, along with diagnostic, predictive, and prescriptive analytics. DA is here to help businesses interpret data and better understand changes that occurred during any specific period.
Essentially, descriptive analytics uses historical data or content to communicate what has already happened in a business. Think of it as a time machine that allows you to return to any period of time and, fortunately, analyze the past and the decisions you've made. Considered the simplest form of analytics, descriptive analytics aims to summarize findings and help companies understand the changes that have occurred in their business without making inferences or predictions from their findings. It allows businesses to acquire invaluable insight into the performance of a company by analyzing relevant data and thereby putting all the information obtained into an understandable format.
Instead of the complex calculations required in predictive and prescriptive analytics, this type of analysis uses simple calculations and statistical tools, such as arithmetic, percentages, and averages. To illustrate findings, descriptive analytics uses visual tools such as line graphs, pie charts, and bar graphs that are easily understood by a wide business audience.
The insights provided by descriptive analytics equip managers, investors, operational employees, and other stakeholders with indispensable information. It helps them make better, data-based decisions and improve business strategies, which in the end lead to optimization of the overall business performance.
As stated above, descriptive analytics uses historical data to provide insights into a company's performance. It is often considered to be the simplest form of analytics and usually serves as a starting point for particular data analysis. Predictive analytics, on the other hand, has its focus on the future and its main purpose is to provide insights into what might happen in the future.
This type of analytics is far more complex than simple descriptive analytics and requires more advanced techniques and tools to achieve its goals. Simply put, descriptive analytics states and describes the facts, while predictive analytics predicts the possible outcomes.
The first step in the descriptive analytics process is to create business metrics (e.g., year-over-year price change, month-over-month increase revenue growth, number of users, or total revenue per subscriber) that effectively evaluate business performance against business goals. Once the metrics are established, the next step is to obtain the necessary data, which must then be collected, managed, and prepared for the next step - data analysis.
Data aggregation and data mining are the two key techniques that are usually employed by descriptive analytics in its discovery of historical data. Data aggregation involves the collection and organization of data into manageable data sets, while data mining uses the created data sets to determine trends, patterns, and meaning. The information gained from these processes is then illustrated in a way that is easily understood by the target audience.
Using tools and techniques such as summary statistics, pattern tracking, clustering, and regression analysis, analysts examine the data to discover patterns and estimate performance. Finally, the results of the data analysis are presented in a comprehensive way by using visualization tools such as charts and graphs.
Below are some examples that illustrate the use of descriptive analytics:
As with any type of data analysis, the use of descriptive analytics has its advantages and disadvantages. Its simplicity, easy to use in day-to-day operations, and reliance on simple calculations and historical data make it a great asset to businesses. It offers a quick and easy way to gain insights into performance and make any necessary improvements or changes if needed.
Obviously, any business may benefit from descriptive analytics as it helps transform business data into meaningful insights that ultimately aid company leaders, managers, and operational staff make better more informed business decisions. By analyzing historical data, descriptive analytics provides a clear picture of where a business stands, measures its performance and finds out if certain goals and objectives are being met. In addition to presenting the facts, it can also reveal areas of weakness that require improvement or change.
On the other hand, descriptive analytics has its focus only on past performance and it does not attempt to look beyond the surface of the data. For further, more in-depth investigations, and more valuable insights, data analysts use diagnostic, predictive, and prescriptive analytics.
Descriptive analytics will help you get valuable information that answers the question “what happened” in terms of your company’s operations. Even though forward-looking analytics like predictive and prescriptive analytics are steadily paving their way to shape the new era of analytics, surprisingly the majority of large corporations are still relying on the old-fashioned ways of decision-making. They continue to use the analytics tools that have been in the market for decades. As much as predictive and prescriptive analytics are yet to be recognized and appreciated, it has been acknowledged that eventually it will be used on a daily basis.
Now that you’ve gained valuable insight into:
you can easily decide whether this data analytics tool is the right choice for your unique business needs. However, if your organization requires a more in-depth approach, then you should consider one of the more advanced forms of data analytics to meet your specific needs.
Published on May 24, 2021
WRITTEN BYWhatagraph team
The Whatagraph blog team produces high-quality content on all things marketing: industry updates, how-to guides, and case studies.
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