Descriptive Analytics

Descriptive analytics is the interpretation of historical data to better understand changes that have happened in a business. Descriptive analytics describes the past using a range of data to draw comparisons. Most commonly reported financial metrics are a product of descriptive analytics, e.g., year-over-year pricing changes, month-over-month sales growth, the number of users, or the total revenue per subscriber. These all describe what has occurred in the business in the time period being measured.

Descriptive analytics is necessary to make raw data understandable to managers, investors, and other stakeholders. Sales of $1 million may sound impressive, but it lacks context. If that figure represents a 20% month-over-month decline, then it is a concern. If it is a 40% year-over-year increase, then it suggests something is going right with the sales strategy, but it still needs the larger context of what the targeted growth was to fully judge.

Descriptive analytics is meant to provide 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 in order to inform management’s strategy.

Descriptive analytics is one of the most basic pieces of business intelligence a company will use. Although descriptive analytics can be very industry specific — such as the seasonal variation in shipment completion times — many are broadly accepted measures that are used all over finance. Return on invested capital (ROIC) is a descriptive analytic created by taking three data points — net income, dividends and total capital — and turning it 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 like predictive and prescriptive analytics. These take in all the descriptive analytics and use additional data from diverse sources to model likely outcomes in the near term. These forward-looking analytics go beyond informing to decision making and start suggesting courses of action that can maximize positive outcomes and minimize negative ones.

That said, we are not quite yet at the point where benevolent and prescient computers will helm all major corporations. Right now, the majority of decisions in offices and boardrooms across the world are made by people based off the same types of descriptive analytics they 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 enough for everybody based on last month’s numbers.