The Differences Between Descriptive, Predictive, and Prescriptive Analytics

Roughly 90% of the world’s data was created within the last several years. With so much new information becoming available, businesses are scrambling to capture as much of it as they can. After all, if knowledge is power, then it stands to reason that information is as valuable as gold.; But unlike gold, data doesn’t hold any real value in itself. For it to be worth the costs associated with capturing it, it needs to be properly analyzed. Unfortunately, the vast majority of digital information quietly passes into obscurity without ever coming under the scrutiny of the analyst’s proverbial microscope. According to a study by IDC, only about 1% of the world’s data is ever analyzed for valuable insights, which means that a lot of the time and effort spent by companies to extract raw data is being wasted.; Proper data analysis, on the other hand, can turn captured, structureless information into real business advantages. To do this, organizations use three different kinds of analytics: Descriptive analytics, predictive analytics, and prescriptive analytics.

While traversing the path between raw data and useful insight, the first stop made by most organizations is descriptive analytics. This is because descriptive analysis is less about examination and extrapolation, and more about refinement and organization. At its most basic, descriptive analytics is used to make large quantities of information more manageable, by condensing it into smaller, more-easily comprehendible chunks and summaries.

Slightly more abstractly, descriptive analytics is used to review past events, and to accurately identify (or describe) their significance in relation to business actions. This focus on the past is essential for businesses that wish to learn from past successes and mistakes. Say, for example, that a business suffers an unexpected quarterly loss. Descriptive analytics can be used to review the associated numbers, as well as any other contributing data, to help identify exactly where the problem lies. Of course, this isn’t to say that descriptive analytics is only useful in identifying past mistakes; businesses of all types use this form of analytics to generate accurate reports for everything from company finances, to inventory management, to office productivity, and beyond.

Descriptive analytics is especially useful when it comes to social-media analysis, where organizations are interested in keeping track of the direct numbers associated with shares, likes, retweets, followers, etc. Additionally, descriptive analytics encompasses the majority of statistical analysis. Descriptive analytics are used by those who want to better understand the reality of their business and the market as it is, and who want to easily summarize various aspects of it.

A step removed from descriptive analytics, predictive analyticsbuilds upon captured data to answer the difficult question: What could happen next? To do so, predictive analytics builds upon the same or similar methods used in descriptive analytics, but adds various forms of machine learning and statistical modeling to locate possible patterns and causes hidden in the raw information.

For this form of analytics to be effective, it requires a number of additional factors, the two most-important being (according to a survey by Forbes) effective technology, and organizational support.

Predictive analytics tools, usually in the form of predictive analytics software, enable users to mine through large volumes of data to find valuable relationships between causes and consequences, and to make educated predictions about data that is not currently available. In essence, predictive analytics is used to fill in the missing pieces of the data puzzle, through a better understanding of the data that is available. This information can be shared throughout every level of an organization, and be put to use in improving processes on all levels, rather than just in relation to sales or marketing.

With predictive analytics, businesses can take the comprehension of consumers to the next level and predict their wants and needs. Companies can now show product recommendations based on previous actions, which is key with Netflix and Amazon. Display ads more accordingly based on the type of consumer and also send out better targeted email campaigns, all in the effort to maximize the data for the best ROI or metric you are tracking.

And while the conclusions produced through predictive analytics can never be 100% accurate, they are none-the-less generally-reliable forecasts that can be used to improve business. In fact, (86%) of executives who oversee predictive-marketing efforts report increased return on investment (ROI) as a result of their predictive marketing.

The final, and most-abstract form of analysis is prescriptive analysis. Prescriptive analytics takes the forecasting ability of predictive analytics a step further, by allowing users to create various ‘what-if’ scenarios, and then extrapolating possible outcomes based on a variety of variables. This makes it possible for organizations to see beyond the consequences of decisions, and into the consequences of potential decisions, in effect, creating a computer simulation for business growth.

Prescriptive analytics are often used in healthcare professions, where physician interpretation of facts are as important as hard evidence, and where this analytical model can be used to determine possible outcomes from a variety of variables. Other examples of businesses that benefit from prescriptive analytics are package delivery services, which use this method to determine the most effective package delivery routes, and airlines, which rely on prescriptive analytics to take into account the many potential factors used to determine fair pricing on airline tickets.

Although prescriptive analytics are generally much more complex to use, and expensive to integrate, than other, more-traditional analytics models, those organizations that adopt prescriptive analytics solutions often find that it is well worth the additional cost and effort.

These three forms of data analysis provide organizations with the freedom to use their captured data however they see fit, from the factual, down-to-earth reporting of descriptive analytics, through the more-abstract forecasting and calculation of predictive and prescriptive analytics. But despite their differences, each of these three approaches retain the same fundamental function: to turn raw data into valuable insight. As such, these approaches are anything but mutually exclusive; organizations that learn to take advantage of all three forms of data analytics are more likely to find the understanding they need, in order to improve and grow in their industries. If you choose to focus on just one or two forms of analysis, you may benefit from the increased data-comprehension that they provide, but you’ll be missing out on some potentially-valuable insights. And, in the competitive world of business, even the most seemingly-insignificant bit of information may mean the difference between success and failure. Because when data is worth its weight in gold, understanding data analytics is a veritable gold mine.