Predictive analytics is the utilization of analytics to forecast what will happen in a business. Predictive analytics is the usage of everything gathered in descriptive and diagnostic analytics along with the continued use of modeling, machine learning and risk assessments to make predictions about future events/trends by scoring data and forecasting. Predictive analytics are generated in order to inform, influence and guide decision-makers with proven data. A company’s use of predictive analytics is a major improvement from looking at historical transactions/events and now looking to using analytics to make and forecast the future.
A common example of predictive analytics is an individual’s credit score. This credit score is based on past events and experience and provide a bank/lender on the risk of default in the future. Just as credit score is not perfect, a predictive analytic is not an exact science and change in assumptions and risks, can impact the future and the analytics’ prediction.
Predictive analytics generally use predictive models to analyze relationships, typically between the performance of an event and that event’s attributes. The predictive model’s goal is to determine the likelihood that a similar event in a different sample of data will result in the same or specific performance. With the increase in big data utilization and storage, the size of a company’s sample data is growing, which allows for more precision on the likelihood of future events. In addition to prescriptive models, predictive analytics also employ descriptive models and decisions models. Descriptive models quantify relationships in a series of data and then employing those relationships across future events. While predictive models typically look at a single performance, a descriptive model generally identifies many relationships and categorization. Decision models are also utilized in predictive analytics, where the relationships across all items of a decisions are utilized. Thus, the results of predictive models, plus a decision made and the forecasted results are merged to predict the results of the decision made. Using the credit score example; a predictive model based on credit score plus the decision to grant credit to an individual will lead to forecasted results of default on the credit.
As companies continue to store and maintain large sets of data across different business segments, exploring this data and using predictive analytics is more within reach of companies than in prior years, and the continual development of new software models for predicting outcomes allows for companies to utilize their stored data in new ways. The use of predictive analytics allows for a Company to answer What Will Happen?
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Kevin Bach, CPA