Descriptive, Prescriptive and Predictive Analytics
Distinguish Business Intelligence from Decision Support Systems.
Research and distinguish Business Intelligence from Decision Support Systems.In your own words, compare the three analytic styles: Descriptive, Prescriptive, and Predictive Analytics. Share the likeness and uniqueness with each style.
Read Pearson Chapter 1 – Overview of Business Intelligence, Analytics and Data Science
Descriptive Analytics is a preliminary stage of data processing that creates a summary of historical data to yield useful information and possibly prepare the data for further analysis.
A descriptive statistic is a summary statistic that quantitatively describes or summarizes features of a collection of information, while descriptive analytics is the process of using and analyzing those statistics. Descriptive statistics is distinguished from inferential statistics (or inductive statistics), in that descriptive statistics aims to summarize a sample, rather than use the data to learn about the population that the sample of data is thought to represent. This generally means that descriptive statistics, unlike inferential statistics, is not developed on the basis of probability theory.
The first stage of business analytics is descriptive analytics, which still accounts for the majority of all business analytics today. Descriptive analytics looks at past performance and understands that performance by mining historical data to look for the reasons behind past success or failure. Most management reporting – such as sales, marketing, operations, and finance – uses this type of post-mortem analysis.
Prescriptive Analytics extends beyond predictive analytics by specifying both the actions necessary to achieve predicted outcomes, and the interrelated effects of each decision.
Prescriptive analytics incorporates both structured and unstructured data and uses a combination of advanced analytic techniques and disciplines to predict, prescribe, and adapt. While the term prescriptive analytics was first coined by IBM and later trademarked by Ayata, the underlying concepts have been around for hundreds of years. The technology behind prescriptive analytics synergistically combines hybrid data, business rules with mathematical models and computational models.
Predictive Analytics encompasses a variety of statistical techniques from predictive modeling, machine learning, and data mining that analyze current and historical facts to make predictions about future or otherwise unknown events.
The next phase is predictive analytics. Predictive analytics answers the question what is likely to happen. This is when historical data is combined with rules, algorithms, and occasionally external data to determine the probable future outcome of an event or the likelihood of a situation occurring. The final phase is prescriptive analytics, which goes beyond predicting future outcomes by also suggesting actions to benefit from the predictions and showing the implications of each decision option.
Prescriptive analytics not only anticipates what will happen and when it will happen, but also why it will happen. Further, prescriptive analytics suggests decision options on how to take advantage of a future opportunity or mitigate a future risk and shows the implication of each decision option. Prescriptive analytics can continually take in new data to re-predict and re-prescribe, thus automatically improving prediction accuracy and prescribing better decision options. Prescriptive analytics ingests hybrid data, a combination of structured (numbers, categories) and unstructured data (videos, images, sounds, texts), and business rules to predict what lies ahead and to prescribe how to take advantage of this predicted future without compromising other priorities.