Descriptive Analytics: What They Are and Related Terms

What Is Descriptive Analytics?

Descriptive analytics refers to the interpretation of historical data to better understand changes that occur in a business. Descriptive analytics describes the use of a range of historic data to draw comparisons with other reporting periods for the same company (i.e. quarterly or annually) or with others within the same industry. Most commonly reported financial metrics are a product of descriptive analytics, such as year-over-year (YOY) pricing changes, month-over-month sales growth, the number of users, or the total revenue per subscriber. These measures all describe what has occurred in a business during a set period.

Key Takeaways

  • Descriptive analytics is the process of parsing historical data to better understand the changes that occur in a business.
  • Using a range of historic data and benchmarking, decision-makers obtain a holistic view of performance and trends on which to base business strategy.
  • Descriptive analytics can help to identify the areas of strength and weakness in an organization.
  • Examples of metrics used in descriptive analytics include year-over-year pricing changes, month-over-month sales growth, the number of users, or the total revenue per subscriber.
  • Descriptive analytics is used in conjunction with newer analytics, such as predictive and prescriptive analytics.

How Descriptive Analytics Works

Descriptive analytics takes a full range of raw data and parses it to draw conclusions that managers, investors, and other stakeholders may find useful and understandable. This data provides an accurate picture of past performance and how that differs from other comparable periods. It can also be used to compare the performance with others within the same industry. These performance metrics can be used to flag areas of strength and weakness to inform management strategies.

For instance, a report showing sales of $1 million may sound impressive, but it lacks context. If that figure represents a 20% month-over-month decline, there is cause for concern. If it is a 40% YOY increase, then it suggests something is going right with the sales or marketing strategy. However, the larger context including targeted growth is required to obtain an informed view of the company's sales performance.

Descriptive analytics is one of the most basic pieces of business intelligence companies use. It can often be industry-specific (think the seasonal variation in shipment completion times) but there are broadly accepted measures common throughout the financial industry.

Descriptive analytics is an important component of performance analysis so that managers can make informed strategic business decisions based on historical data.

What Does Descriptive Analytics Tell You?

Companies can use descriptive analytics to gain valuable insight into how they are performing. Because it is generally an industry-specific tool, one company can use it to compare its performance and position in the marketplace with its competitors by looking at its past performance, such as growth in its revenue and sales. It is also useful to determine current financial trends, including goals for individuals within the company.

How Is Descriptive Analytics Used?

Descriptive analytics is a very important tool that can be used in different parts of any business. That's because it allows companies to understand how well it is performing and where there may be inefficiencies. As such, corporate management can identify areas for improvement and use it to motivate different teams to implement changes for continued success.

There are two primary methods by which data is collected for descriptive analytics. These are data aggregation and data mining. Before data can be made sense of it must first be gathered and then parsed into manageable information. This information can then be meaningfully used by management to comprehend where the business stands.

For instance, return on invested capital (ROIC) is a form of descriptive analytics created by taking three data points—net income, dividends, and total capital—and turning those data points into an easy-to-understand percentage that can be used to compare one company’s performance to others.

Descriptive analytics provides the "What happened?" information regarding a company's operations, whole diagnostic analytics provides the "Why did it happen?" information, and predictive analytics provides information as to "What could happen in the future?"

Steps in Descriptive Analytics

There are a few steps that companies can take in order to successfully implement descriptive analytics into their business strategy. The following list highlights these steps along with a description of each.

  1. Identifying which metrics to analyze. Before beginning, it's important to decide which metrics companies want to produce and the time frame for each, such as quarterly revenue or annual operating profit.
  2. Identifying and locating the data. This step requires locating all of the data required to produce the result. This means going through all internal and external sources, including databases.
  3. Compiling the data. Once all the data is identified and located, the next step is to prepare and compile it together. Part of the process here is to ensure that it's accurate and to format everything into a single format.
  4. Data analysis. Analyzing datasets and figures means using different tools

Once all these steps are completed, it's important to present all the data to the appropriate stakeholders. Using appropriate visual aids, such as charts, graphics, videos, and other tools can be a great way to provide analysts, investors, management, and others with the insight they need about the direction of the company.

The larger and more complex a company is, the more descriptive analytics it will generally use to measure its performance.

Advantages and Disadvantages of Descriptive Analytics


One of the main benefits of employing descriptive analytics in the corporate workflow is that it disseminates information in a simple manner and provides all major stakeholders with a way to understand complex ideas. This is usually done through easy-to-understand visuals like charts and graphs. It isn't uncommon to see side-by-side comparisons of where the company was before with where it is now.

Major stakeholders can see how a company compares to its competition within the same industry. That's because the variables tend to be the same, such as production costs, revenue streams, and product offerings. This allows one company to see whether there are any areas for improvement in their own business plans and models.


While descriptive analytics helps understand what happened in the past, it doesn't necessarily open up a window into what to expect in the future. As such, companies can't count on it to determine how market forces, changes in supply and demand, economic swings, and other variables may affect them in the future.

Stakeholders may find it challenging to read between the lines, especially when explicit or implicit bias comes into play. For instance, stakeholders may choose favorable metrics to analyze and ignore others. Doing so may give others the feeling that a company is profitable and that there are no areas that require change.

  • Breaks down information so it is easy to understand

  • Allows companies to see how they're doing compared to the competition

  • Can't be used to determine future performance

  • Stakeholders can pick-and-choose (favorable) metrics to analyze

Descriptive vs. Predictive, Prescriptive, and Diagnostic Analytics

Descriptive analytics provides important information in an easy-to-grasp format. As such, there will always be a need for this type of analysis. But there may be more emphasis on newer fields of analytics, including predictive, prescriptive, and diagnostic analytics.

These analytics use descriptive analytics and integrate additional data from diverse sources to model likely outcomes in the near term. These forward-looking analytics go beyond providing information to assisting in decision-making. These types of analytics can also suggest courses of action that can maximize positive outcomes and minimize negative ones.

Predictive Analytics

As its name implies, predictive analytics tries to make predictions about future performance. This is done through the use of statistics and modeling. Current and past data are used to determine whether similar outcomes are likely to happen again in the future.

Companies that employ predictive analytics can benefit by identifying and addressing inefficiencies. They can also use it to find better and more efficient ways to put their resources (such as supplies, labor, and equipment) to work.

Prescriptive Analytics

Prescriptive analytics allows companies to use technology to analyze important data to determine what they need to do to achieve specific results. It takes certain situations and available resources, along with past and current performance into account to develop suggestions for the future.

Stakeholders that use prescriptive analysis may be better equipped to make important decisions across any timeline, including whether they need to invest more in research and development (R&D), if they should continue with a specific product offering, or if they need to enter a new market.

Diagnostic Analytics

Diagnostic analytics involves the use of data to understand the relationship between variables and why certain trends exist. Put simply, it's another way to determine why something happened. This type of analysis can be undertaken manually or with the help of computer software.

Unlike other types of analytics, diagnostic analytics does not try to understand a company's historical performance or to make predictions about what companies can expect in the future. Instead, it is commonly used by key stakeholders to figure out the root cause of an event and make changes in the future.

How Can Companies Benefit From Descriptive Analytics?

Descriptive analytics is a form of analysis that tries to answer the question "What happened?" As such, it takes historical data to understand changes that have taken place. This allows companies to draw comparisons with other reporting periods or similar companies. By employing descriptive analytics, companies are better able to identify inefficiencies in their operations and make changes for the future.

What Is the Relationship Between Descriptive and Predictive Analytics?

Descriptive analytics tries to answer the question "What happened?" Predictive analytics, on the other hand, attempts to answer the "What will happen?" query. This means that descriptive analytics uses historical data and past performance to figure out where improvements can be made. Predictive analytics can try to help companies understand how those changes will impact performance in the future. As such, these two types of analysis can be used together to work hand-in-hand.

What Is an Example of Descriptive Analytics?

Companies can use descriptive analytics to analyze various metrics during a specific reporting period to help them achieve success. These can be financial and non-financial. Some companies choose to measure engagement with their audience through social media because it can tell them whether what worked with a certain ad campaign or product launch. This can be measured by analyzing how clicks and likes lead to increased traffic on their sites and, therefore, increases in sales and referrals.

The Bottom Line

Descriptive analytics can be a great way for companies to begin analyzing their performance metrics. That's because it's one of the easiest forms of data analysis. It's a straightforward approach to provide management, investors, and analysts with a direct comparison to similar metrics, such as quarter-over-quarter revenue. Using past performance can help key stakeholders better understand what happened so they make better, more informed decisions for the future.

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