Data Analytics is a science by definition, used by organizations to examine data in a way that can help them draw conclusions. The practice of studying data to gain insights is used in virtually every major business sector. There are four major types of analytics that enable organizations to explain what happened in the past, understand what is happening in the present, and predict what is likely to occur in the future.
Diagnostic analytics help stakeholders reflect on why something happened, and descriptive analytics explain why said thing is going on. In this post, we’ll take a look at the two types of analytics that take place in the foresight stage, predictive and prescriptive analytics. These two forms of analysis are run when companies want to peer into the future in hopes of getting ahead of trends they observed via former methodologies.
The core focus of predictive modeling is to use explanatory variables from past occurrences and exploit them to predict the previously unknown future. The accuracy and usability of predictive analytics is wholly dependent on how granular the analysis has been run and the type of assumptions that are being made. Forward-thinking organizations will utilize predictive models for a variety of business functions. Some of the most common ways this type of analysis is being used is to detect fraud, optimize marketing campaigns, improve operations, and to manage risk.
Businesses will construct predictive models which can help them use historical and real-time data to identify opportunities as they arise. This enables informed decision-making and a higher likelihood of impactful pattern recognition. Having the ability to predict future events with some degree of certainty can act as a major advantage over the competition and hedge against any unsuspected events that act as a barrier to improved business processes.
Prescriptive analytics is the final phase in analysis where organizations apply algorithms to their predictive models. These models will then suggest decision options to take advantage of the results of the three previous phases. In this way, business stakeholders begin to make decisions based on what earlier analysis has confirmed, essentially weeding out all the noise and allowing users to pick an action to proceed with. Analysts also take this opportunity to simulate various outcomes against earlier analysis to gauge prospective benefit for an even deeper understanding.
Contrary to the predictive phase, which uncovers what is likely to happen in the future, prescriptive models are applied to unveil what the appropriate reaction to the predicted event should be. One could view prescriptive analytics as advice provisioning. Though prescriptive models are being used to optimize production and provide insight into inventory and supply chain environments in some verticals, widespread adoption remains limited due to the complexity of administration. As automation and machine learning functionality technologies continues to advance, we can expect to see an uptick in enterprises who deploy this type of analytics in the future.
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