Do you know how often you are using the cloud every single day? In part one of our discussion with IBM Fellow Sam Lightstone, learn about cloud computing and why it is increasingly important in our data-driven world. Also, learn alternatives to loading private data to the cloud, data movement, and
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."
Perhaps one the single most significant changes to the analytics landscape in recent years had been the emergence of the data scientist. This role is continuing to evolve, with many organizations still in the process of establishing how best to incorporate this relatively new discipline into their
The data lake can be considered the consolidation point for all of the data which is of value for use across different aspects of the enterprise. There is a significant range of the different types of potential data repositories that are likely to be part of a typical data lake.
Caregivers.com is changing the way senior care providers operate through powerful insights generated through the company’s mobile app. Justin Saul, Senior Director of Technology, Caregivers.com, explains, "with IBM Cloudant and the IBM Watson Data Platform, we were able to quickly iterate on
Hear from Nancy Hensley, director of offering management for IBM Analytics who speaks with Rob Thomas, VP of development for analytics on the subject of business transformation and a discussion of the data maturity curve.
The open source Hadoop framework accommodates distributed storage and processing of large data sets on clusters of computers through the use of programming models. If that description sounds complex, then dig into this breakdown of Hadoop components to gain an understanding of just how flexible
On the heels of several key announcements to broaden the IBM Cloud Data Services portfolio, see how a wide range of technologies can be implemented in a cloud-based, data warehouse architecture to support operational and analytical workloads.
Thanks to big data and analytics, the public sector has made great strides in a short time. See for yourself how much progress has been accomplished, and find out how you can take advantage of these advances.
This is part four in a series presenting, in small easily consumable bites, findings and insights from the IBM Institute for Business Value’s latest study and paper - “Analytics: The speed advantage - Why data-driven organizations are winning the race in today’s marketplace." In part three we
Let's explore in more detail the final two shifts highlighted in the new IBM Analytics study, “Analytics: The speed advantage - Why data-driven organizations are winning the race in today’s marketplace," including the transformation of business processes with digital capabilities.