Business Analytics for Managers: Using Data for Better Decision Making
March 3 – 4, 2014
Includes program materials, lunch, and break station refreshments.
Data are everywhere and the volume of new data is growing at an exponential rate. On almost a daily basis, news headlines are touting innovative applications of “Big Data”, while HR departments rush to fill newly created “data scientist” positions.
In this context and amidst this hype, businesses of all kinds have real opportunities to use data to develop knowledge and drive better decision making. Contrary to common beliefs, however, a data-driven business environment does not always require PhDs in statistics and computer science. What it does always require is decision makers who are analytically minded. Proficiency with data and analytics must extend beyond a single resource, individual, or even department within an organization. Business managers today must become comfortable with the scientific process of transforming data into insights for making business decisions.
“Business Analytics for Managers: Using Data for Better Decision Making” is a new two-day Babson Executive Education open enrollment program that provides an introduction to the iterative exploration of data that can be used to gain insights and optimize business processes. The program explores the data analytics lifecycle and introduces predictive analytics techniques in the context of real-world applications from diverse business areas such as marketing, financial forecasting, and operations.
“Business Analytics for Managers: Using Data for Better Decision Making” is designed to empower participants to identify areas of applications in which business analytics can be particularly successful in their own organizations. Through this unique offering, managers gain hands-on experience in working with data and an appreciation for the opportunities and challenges that data present.
During the program, participants gain exposure to different approaches used in business analytics including:
- Data management, data analysis and modeling: Techniques for generating ideas, experimenting with solutions, and evaluating alternatives
- Association: Recognizing patterns and drawing connections across questions, problems or ideas from seemingly unrelated fields in business contexts
- Experimentation and experiential learning: The process of setting up small experiments to gather data and learn