What do you get when you mix historical data, statistical modeling and machine learning? You’ve got it: Predictive Analytics. This new forecasting method is expected to reach $11 billion by 2022, with compounded annual growth rate of 21% between now and then, according to Zion Market Research.
Let’s address the basics of predictive analytics, offer real life examples and define how to get started in this trending technology.
Predictive Analytics Defined
This process involves extracting information from data sets with the goal of identifying trends and patterns that are used to predict current and future outcomes. It uses the following programs: big data, data mining, statistical modeling, machine learning and mathematics.
Major Reasons to Implement It
The traditional ROI factors still apply here: wanting to save or make more money, and having an edge over competition. Because of its real-time capabilities, your organization can be more agile in response to market demands.
In addition, for the first time ever, predictive analytics takes human biases out and through the use of machine learning, allows neural net technologies to learn from previous modeling.
Examples of Predictive Analytics
Here are just a few ways organizations are using it today.
- Retail: Forecast inventory, manage shipping and configure store layouts
- Airline: Set ticket prices and schedule maintenance on aircraft
- Automotive: Use multiple driver behaviors to develop driverless cars
- Hospitality: Forecast the number of guests and add-on services each night which has an impact on revenue and staffing needs.
- Energy: Determine short- and long-term demand. Adds in past weather events, regulations and equipment failures to finalize cost structures.
- Manufacturing: Optimize deliveries based on short-term product orders.
- Healthcare: Identify patients’ risk for readmission and improve staffing in areas of unpredictability, like the ER.
- Law Enforcement: Detect, predict and halt criminal behavior before it happens by classifying those individuals that are most likely to be repeat offenders.
What You Need to Get Started
First, determine what you want to model and how will it will help your organization.
Second, make sure you have all the data available to create actionable insights. If you have missing datasets, find out where they are and bring them into your organization’s repository.
Third, choose a software platform. The current ones are Microsoft Azure, SAP, SAS, Tableau, Teradata and TIBCO Software.
Fifth, hire one or more developers to write customized modeling code.
We Can Help Your Organization Get to Predictive Analytics
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