Wednesday, September 21, 2016
11:30 AM - 12:00 PM
Today, the ability to predict the future is closer to reality than fiction. Thanks to predicative analytics, which leverages data-mining and statistical algorithms, businesses are able to identify the likelihood of future outcomes based on historical data. Furthermore, as technology has evolved, so too has our ability to process data at an incredible rate, making it possible to perform what has become known as anticipatory analytics.
While still a new concept, anticipatory analytics is gaining prevalence as a methodology. It leapfrogs predictive analytics in that it enables companies to forecast future behaviors by identifying change, acceleration and declaration of market dynamics. Businesses can find that anticipatory analytics facilitates expanding prospect pools and getting ahead of the competition.
While anticipatory is certainly an exciting development in the world of analytics, it is not meant to replace traditional predictive analytics. The questions for business executives then becomes: which methodology do you use to address your business challenge(s).
Dr. Nipa Basu is the Chief Analytics Officer at Dun & Bradstreet. Nipa is the main source of inspiration and leadership for Dun & Bradstreet's extensive team of data modelers and scientists. Highly skilled in solving a wide range of business challenges with unique, basic and advanced analytic applications, the team partners with the world's leading Fortune 500 companies, creating solutions that drive business growth and results.
Nipa joined Dun & Bradstreet in 2000 and since then has held key leadership roles focused on driving the success of Dun & Bradstreet's Analytics practice.
Nipa began her professional career as an Economist with the New York State Legislative Tax Study Commission. She then joined Sandia National Laboratories, where she built a Microsimulation Model of the U.S. Economy. Prior to Dun & Bradstreet, Nipa was a database marketing statistician for AT&T with responsibility for building predictive marketing models.