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Descriptive, Predictive and Prescriptive Analytics 101

Descriptive, Predictive and Prescriptive Analytics 101

Due to the endless flow of data made available to organizations, businesses look to analytics solutions to draw conclusions and to help their decision making process. As evidenced by the organizations that have lowered operating expenses, boosted customer service, increased revenue and improved their processes, a significant return on investment can be enjoyed by those who employ analytics.

With all of the available choices for analytics solutions, determining which ones can help your business can be a difficult task. While one is not necessarily better than another, these analytic options can definitely complement each other when used properly. They can be categorized into three specific types:

Predictive Analytics
Forecasts and predictive models are used here to provide insight on what could happen in the future.

This form of analytics is all about understanding the future and its ability to forecast what could happen. Companies are able to gain actionable insights based on collected data from various sources. While no algorithm can completely predict future outcomes, predictive analytics is able to provide estimates about their likelihood of occurring. Whenever you need to fill information that you lack or to know something about the future, predictive analytics is the key.

Prescriptive Analytics
Advice on how to optimize processes to handle possible outcomes is gained by using simulation algorithms and optimization techniques.

The newest of the three forms of analytics, prescriptive analytics provides different possible actions that a user can take, and guides them towards the best solution. This analytics solution aims to quantify the effect of a decision and advising on possible outcomes before the choice is actually made. This form of analytics is ideal for times when you need to provide users with advice on what choice is best.

Descriptive Analytics
This type of analytics uses both data mining and data aggregation techniques to provide insight into what has happened.

With descriptive analytics, raw data is summarized and “described” or made into data that can be interpreted by humans. Whether the data comes from a second ago or several years ago, this analytic option can describe the past and allow us to glean valuable information from past behaviors, and find out how these behaviors can influence possible future outcomes.

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