Ten years ago, these tools were deemed'state of the art', but technology has changed and now, with Big Data, incredible processing speeds, and predictive analytics software, a much more accurate way of predicting the future is available. Predictive analytics is not entirely new; in financial services it underpins computer-assisted stock trading and in retail,predictions of online buyer behavior are increasingly being used to design personalized marketing campaigns. Elsewhere, companies that understand the competitive edge that it can provide, are starting to invest. A recent Forrester survey1 of data and analytics decision makers found that 39% have data and analytics budgets of at least $10 million.
This global, cross-industry event highlights predictive tools being used to solve today's greatest workforce challenges. Until recently, predictive analytics tools were primarily used to drive enterprise performance by predicting customer, voter, debtor, and other human outcomes. Today, predictive analytics is similarly being applied to drive performance and lifetime value of an organization's workforce. Predictive workforce analytics can help answer questions such as; who will accelerate, who will terminate, who has the greatest lifetime value, and more. Predictive career pathing for optimal succession planning Predicting flight risk pre- and post- hire to reduce employee attrition Optimizing the workforce planning process using predictive analytics Using a Learning Recommendation Engine to increase retail performance Shifting the curve of sales performance with predictive analytics The State of Predictive Analytics for Workforce Enterprise Applications People Analytics: Using Wearables and Big Data to Reinvent Management Should HR be bold with predictive analytics projects?
Predictive analytics use historical data to deliver insights and results that predict future events, trends, and outcomes. Business leaders apply these predictions to their enterprise strategies to try to gain a competitive advantage. IBM Watson is the most well-known use of predictive analytics. Watson is used for a variety of purposes, including helping businesses predict customers' behaviors and spot cybersecurity risks. TechRepublic's smart person's guide on predictive analytics is a primer on this popular big data practice.
Predictive analytics, sometimes referred to as big data analytics, relies on aspects of data mining as well as algorithms to develop predictive models. These predictive models can be used by enterprise marketers to more effectively develop predictions of future user behaviors based on the sourced historical data. These statistical models are growing as a result of the wide swaths of available current data as well as the advent of capable artificial intelligence and machine learning. The applications of predictive analytics are extensive and often require four key components to maintain effectiveness. Fundamental to any aspect of data science, it's difficult to develop accurate predictions or craft a decision tree if you're garnering insights from inadequate data sources.
Predictive analytics is a category of data analytics aimed at making predictions about future outcomes based on historical data and analytics techniques such as statistical modeling and machine learning. The science of predictive analytics can generate future insights with a significant degree of precision. With the help of sophisticated predictive analytics tools and models, any organization can now use past and current data to reliably forecast trends and behaviors milliseconds, days, or years into the future. Predictive analytics has captured the support of wide range of organizations, with a global market projected to reach approximately $10.95 billion by 2022, growing at a compound annual growth rate (CAGR) of around 21 percent between 2016 and 2022, according to a 2017 report issued by Zion Market Research. Predictive analytics draws its power from a wide range of methods and technologies, including big data, data mining, statistical modeling, machine learning and assorted mathematical processes.