Enterprise companies starting the transformation into a data-driven organization often wonder where to start. Companies have traditionally collected large amounts of data from sources such as operational systems. With the rise of big data, big data technologies and the Internet of Things (IoT), additional sources – such as sensor readings and social media posts – are rapidly becoming available. In order to effectively utilize both traditional sources and new ones, companies first need to join and view the data in a holistic context. After establishing a data lake to bring all data sources together in a single analytics environment, one of the first data science projects worth exploring is segmentation, which automatically identifies patterns.
In this DSC webinar, two Pivotal data scientists will discuss:
· What segmentation is
· Traditional approaches to segmentation
· How big data technologies are enabling advances in this field
They will also share some stories from past data science engagements, outline best practices and discuss the kinds of insights that can be derived from a big data approach to segmentation using both internal and external data sources.
Grace Gee, Data Scientist — Pivotal
Jarrod Vawdrey, Data Scientist — Pivotal
Tim Matteson, Co-Founder — Data Science Central