Today's companies run on data. We track trends, assess results and analyze feedback. Data helps us manage risk, plan for future growth and allocate resources. Data is the foundation of our lead lists, sales pipelines and connections with customers. We collect contact records, integrate industry insight and profile companies based on their technographics (tech stack) and firmographics (company demographics).

When the signal-to-noise ratio is high, modern machine learning methods trounce classical statistical methods when it comes to prediction. The role of statistics in this case is really to boost the signal-to-noise ratio through the understanding of things like experimental design.

Artificial Intelligence(AI) continues to be the next great topic of debate. In fact, Microsoft, Amazon, IBM, Google and Facebook announced on Thursday,Sept.29 the formation of the Partnership on Artificial Intelligence to Benefit People and Society. Within the predictive analytics discipline, though, we tend to use the term "machine learning" as our reference point for artificial intelligence. Much of our thinking in this area has focused around the role of the practitioner or craftsman versus the machine and the concept of machine learning. Yet, machine learning has now evolved into the usage of higher levels of mathematics and computer science with the most recent level being deep learning.