Predictive Analytics Vs. Prescriptive Analytics
What Is Predictive Analytics?
Predictive Analytics is the sub-field of Supervised Learning where users attempt to model data elements and predict future outcomes through the evaluation of probability estimates. Rooted deeply in mathematics, specifically statistics, predictive analytics shares many components with Unsupervised Learning, with the prescribed difference to the measurement of a desired predictive outcome.
How This Analytics Works in Real Time
Predictive Analytics is the development of probability models based on variables, including historical data, related to possible events (purchases, changes in price, etc.). When it receives other pieces of information, the model triggers a reaction by the organization. The triggering factor may be an event, such as a customer adding a product to an online shopping basket, or it may be data in a data stream, such as a news feed or utility sensor data, or an increased volume of service requests. The triggering factor may be an external event. News being reported about a company is a big predictor of a change in stock price. Predicting stock movement should include monitoring news and determining if news about a company is likely to be good or bad for the stock price.
Real-Time Data Streams in P.A
Frequently, the triggering factor is the accumulation of a large volume of real-time data, such as an extremely high number of trades or requests for service or volatility of the environment. Monitoring a data event stream includes incrementally building on the populated models until a threshold is reached as defined in the model.
Predictive Analytics and Low Latency Technologies
The amount of time that a predictive model provides between the prediction and the event predicted is frequently very small (seconds or less than a second). Investment in very low latency technology solutions, such as in-memory databases, high-speed networks, and even physical proximity to the source of the data, optimizes an organization’s ability to react to the prediction.
Forecasting Techniques in Predictive Analytics
The simplest form of the predictive model is the forecast. Many techniques exist for trending or forecasting based on regression analysis and benefit from smoothing. The simplest way to smooth data is through a moving average or even a weighted moving average. More advanced techniques can be useful, like the exponential moving average, which introduces a smoothing factor to be applied. Minimizing the error residual from the least squares can be a starting point, but several runs are necessary to determine and optimize the smoothing factor. Double and triple exponential smoothing models exist to address trend and seasonality components.
Prescriptive Analytics
What Is Prescriptive Analytics?
Prescriptive analytics take predictive analytics a step further to define actions that will affect outcomes, rather than just predicting the outcomes from actions that have occurred. Prescriptive analytics anticipates what will happen, when it will happen and implies why it will happen. Because prescriptive analytics can show the implications of various decisions, it can suggest how to take advantage of an opportunity or avoid a risk. Prescriptive analytics can continually take in new data to re-predict and re-prescribe. This process can improve prediction accuracy and result in better prescriptions.
