What Is Descriptive Analytics?
Descriptive analytics is the interpretation of historical data to better understand changes that have occurred in a business. Descriptive analytics describes the use of a range of historic data to draw comparisons. Most commonly reported financial metrics are a product of descriptive analytics, for example, year-over-year 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.
- Descriptive analytics is the process of parsing historical data to better understand the changes that have occurred 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 now being used in conjunction with newer analytics, such as predictive and prescriptive analytics.
- In its simplest form, descriptive analytics answers the question, "What happened?"
Understanding Descriptive Analytics
Descriptive analytics takes raw data and parses that data to draw conclusions that are useful and understandable by managers, investors, and other stakeholders. A report showing sales of $1 million may sound impressive, but it lacks context. If that figure represents a 20% month-over-month decline, it is a concern. If it is a 40% year-over-year increase, then it suggests something is going right with the sales strategy. However, the larger context including targeted growth is required to obtain an informed view of the company's sales performance.
Descriptive analytics uses a full range of data to give an accurate picture of what has happened in a business and how that differs from other comparable periods. These performance metrics can be used to flag areas of strength and weakness to inform management strategies.
The two main methods in which data is collected for descriptive analytics 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.
Descriptive analytics is an important component of performance analysis so that managers can make informed strategic business decisions based on historical data.
Descriptive analytics is one of the most basic pieces of business intelligence a company will use. Although descriptive analytics can be industry-specific, such as the seasonal variation in shipment completion times, analytics use broadly accepted measures common throughout the financial industry.
Return on invested capital (ROIC) is a descriptive analytic 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.
Generally speaking, the larger and more complex a company is, the more descriptive analytics it will use to measure its performance.
Descriptive analytics provides important information in an easy-to-grasp format. There will always be a need for descriptive analytics. However, more effort is going towards newer fields of analytics such as predictive and prescriptive analytics.
These types of 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.
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?"
That said, society is not quite yet at the point where benevolent and prescient computers will helm all major corporations. The majority of decisions in offices and boardrooms worldwide are made by people using the same types of descriptive analytics used 10, 20, and 30 years ago, such as whether sales were up or down compared to last month, is the product getting to market on time, and does the company have sufficient supply based on last month’s numbers?