What is driving change in the world of data? In his keynote from the Big Data Summit KC 2017, our Making Data Simple podcast host and IBM Analytics VP Al Martin addresses disruption, the data maturity model and the five areas business must get right to succeed in the era of cognitive computing.
How did companies like Facebook and Airbnb get so big so fast? What can we learn from them? Why is data so important for growth? Nancy Hensley, Director of Strategy & Growth for IBM Hybrid Cloud, has the answers in this episode of Making Data Simple.
In this episode of Making Data Simple we hear insights from IBM Machine Learning Hub data scientists Jorge A. Castañón and Óscar Lara-Yejas as they discuss what machine learning is and is not. They also answer the most controversial question today: Will machines take our jobs? Come find out!
How do you provide answers to clients prior to them asking? What do you do with an abundance of client data? In this episode of Making Data Simple, Tracy Bolot, Director of Digital Client Support for Analytics at IBM, talks about how to maximize teamwork and strengths to enrich your clients'...
In this first episode of Making Data Simple, we welcome Daniel Hernandez, VP of IBM Analytics Offering Management, who helps us navigate "the big data problem" and shares why he doesn't like the term "big data."
Jeff Josten is IBM Distinguished Engineer for DB2 for z/OS Development, IBM Analytics, Platform Development. In this podcast, he discusses how the value of machine learning in enterprise applications of hybrid transaction/analytics processing. He will be speaking on this topic on February 15, 2017...
J White Bear is a data scientist and software engineer at IBM. In this podcast, White Bear discusses simultaneous localization and mapping, an ongoing research area in robotics for autonomous vehicles and well-recognized as a nontrivial problem space in both industry and research.
Seth Dobrin is vice president and CDO, IBM Analytics, platform development, at IBM. In this podcast, Dobrin shares experiences using Apache Spark for data science transformation and some thoughts on a larger vision for data science transformation at scale.