How do baseball scouts use machine learning and AI to predict player performance? Ari Kaplan, Principal at Aginity, and David Kearns, Offering Manager, IBM Analytics Ecosystem, join us to talk about the recent merge of H20.ai and IBM. They also discuss how baseball decisions are made using
On this week's episode, John J Thomas, Distinguished Engineer and Director for IBM Analytics, and Steve Moore, Senior Content Designer and Story Strategist, join us to talk about data science and how your business can best weave the skills of a data scientist into their decisions. Learn how to
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."
In this podcast recorded at WoW, IBM Data Science Evangelist James Kobielus interviewed two of those experts—Steve Ardire, AI software startup advisor, and Joe Caserta, Caserta Concepts—to gain their insights on trends facing data professionals.
Take a peek at the future of data science in this discussion with five thought leaders in the data analytics industry, the second installment of a two-part interview recorded at the IBM Insight at World of Watson 2016 conference.
Take a peek at the future of data science in this discussion with five thought leaders in the data analytics industry, the first installment of a two-part interview recorded at the IBM Insight at World of Watson 2016 conference.
As telecommunications companies offer a wider range of services, the amount of data they must process is increasing exponentially. This podcast discusses how telcos can use Apache Hadoop to keep up with rapid data growth.
Bernard Marr, one of the world’s top experts in big data and analytics strategy, made a return visit to our podcast to explain his vision of "SMART big data, analytics and metrics."
You may be surprised to learn that he doesn't really like "big data." In fact, he says "the challenge is to build the
IBM has a long and successful history with open source, from running Linux on IBM PCs to contributing initial codebase for Eclipse. We believe a mix of open source and closed source is the best way to drive adoption in the marketplace. Having the full support of a vendor like IBM can lower risk