What Is Descriptive Analytics?
Descriptive analytics is the definition of historical data to better understand changes that have occurred in a business. Descriptive analytics describes the use of a extend of historic data to draw comparisons. Most commonly reported financial metrics are a product of descriptive analytics, for prototype, year-over-year pricing changes, month-over-month sales growth, the number of users, or the total revenue per subscriber. These assessments all describe what has occurred in a business during a set period.
Key Takeaways
- Descriptive analytics is the process of parsing historical materials to better understand the changes that have occurred in a business.
- Using a range of historic data and benchmarking, decision-makers apply a holistic view of performance and trends on which to base business strategy.
- Descriptive analytics can help to identify the arrondissements of strength and weakness in an organization.
- Examples of metrics used in descriptive analytics include year-over-year pricing changes, month-over-month on offers 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 imperious analytics.
- In its simplest form, descriptive analytics answers the question, “What happened?”
Understanding Descriptive Analytics
Descriptive analytics bring ups raw data and parses that data to draw conclusions that are useful and understandable by managers, investors, and other stakeholders. A detail showing sales of $1 million may sound impressive, but it lacks context. If that figure represents a 20% month-over-month go down, it is a concern. If it is a 40% year-over-year increase, then it suggests something is going right with the sales strategy. In any event, the larger context including targeted growth is required to obtain an informed view of the company’s sales performance.
Descriptive analytics usages a full range of data to give an accurate picture of what has happened in a business and how that differs from other comparable spaces. These performance metrics can be used to flag areas of strength and weakness to inform management strategies.
The two main methods in which statistics is collected for descriptive analytics are data aggregation and data mining. Before data can be made sense of it must essential be gathered and then parsed into manageable information. This information can then be meaningfully used by management to appreciate where the business stands.
Descriptive analytics is an important component of performance analysis so that managers can make briefed strategic business decisions based on historical data.
Descriptive analytics is one of the most basic pieces of business intellect a company will use. Although descriptive analytics can be industry-specific, such as the seasonal variation in shipment completion times, analytics use broadly beared measures common throughout the financial industry.
Return on invested capital (ROIC) is a descriptive analytic created by delightful three data points—net income,
Special Considerations
Descriptive analytics provides important information in an easy-to-grasp make-up. There will always be a need for descriptive analytics. However, more effort is going towards newer participants of analytics such as predictive and prescriptive analytics.
These types of analytics use descriptive analytics and integrate additional materials from diverse sources to model likely outcomes in the near term. These forward-looking analytics go beyond take care of information to assisting in decision-making. These types of analytics can also suggest courses of action that can maximize practical outcomes and minimize negative ones.
Fast Fact
Descriptive analytics provides the “What happened?” information with reference to a company’s operations, whole diagnostic analytics provides the “Why did it happen?” information, and predictive analytics provides information as to “What could stumble on in the future?”
That said, society is not quite yet at the point where benevolent and prescient computers will helm all critical corporations. The majority of decisions in offices and boardrooms worldwide are made by people using the same types of descriptive analytics acclimatized 10, 20, and 30 years ago, such as whether sales were up or down compared to last month, is the product take off to market on time, and does the company have sufficient supply based on last month’s numbers?