Nick Caldicott was working as an analytics manager at food giant ConAgra when he enrolled in the part-time Master of Science in Analytics program at the University of Chicago. Since earning his BS in Management Information Systems and Marketing, Caldicott had worked as an analyst--first in health marketing, next as a consultant. But he wanted something more. "I wanted to become a data scientist," he says. Designed for working professionals, the part-time MS in Analytics program's flexible schedule and hybrid learning model seemed like the right fit.
Predictive analytics has been used for many years to learn patterns from historical data to literally predict the future. Well known techniques include neural networks, decision trees, and regression models. Although these techniques have been applied to a myriad of problems, the advent of big data, cost-efficient processing power, and open standards have propelled predictive analytics to new heights. Big data involves large amounts of structured and unstructured data that are captured from people (e.g., on-line transactions, tweets, …) as well as sensors (e.g., GPS signals in mobile devices). With big data, companies can now start to assemble a 360 degree view of their customers and processes.
With the supply of analytics talent in the labor force slowly growing to meet demand, a majority of companies are looking within their own walls for solutions. While there is clearly a need for new graduates and data scientists who know the most cutting-edge techniques of analysis, there is just as much of a need for information workers who know both the business and technical sides of an organization. Current employees who are able to develop an analytics skill set and combine that with their knowledge of the business can be invaluable when moving analytical insights across the "last mile" to decision makers. For companies that find it difficult to lure top talent, their analytics capability can make substantial advances by looking differently at their own existing talent. Companies that are most successful with analytics have a very different approach to hiring, training, integrating and promoting data workers than other organizations.