IBM is well-known for its powerful legacy of design throughout the 1980s. But the company’s focus on design dimmed until Phil Gilbert stepped up to the plate in 2010 and instilled design thinking throughout the company, empowering a legion of designers. The focus on hiring talent, investing in
In episode 37 of the Making Data Simple podcast, Al Martin has a detailed conversation with Avijit Chatterjee, Chief Analytics Officer at IBM Morgan Stanley and Metlife. Together, the two discuss various use cases of AI transforming business processes along with tips for networking and time-
This episode of Making Data Simple features Brian O’Neill, product designer for Designing for Analytics. Brian and host Al Martin step back from the deep, technical questions of data science, architecture and governance to look at how principles of design can clarify and accelerate development for
IBM Hybrid Cloud Marketing VP Scott Hebner speaks with Big Data and Analytics Hub about the bets he’s placing on the offering to evolve into the company’s first AI platform and emulate WebSphere’s success.
Building on the success of the IBM Chief Data Officer Strategy Summit Fall 2017, the IBM Chief Data Officer Summit Spring 2018 took place 1 - 2 May in San Francisco. We've collected a full social recap in the below Twitter Moment, as well as interviews and keynote videos for you to peruse.
Organizations everywhere, from massive governments to the smallest start-ups, are in a race for the best-possible data expertise and tools. To help your team understand the data science journey, IBM created the Data Science for All webcast.
For the first time in human experience, there’s the opportunity to transform a city by listening to all of its inhabitants, individually. That’s the mission of the Jakarta Smart City (JSC) project, and it’s a major challenge in a district of some 10 million residents.
Data already is the new currency and is at the heart of everything digital. I like to repeat the adage, “Data becomes Information, becomes Knowledge, becomes Wisdom”. And “It’s all about the data”. So why do we send up probes, sensors or satellites — for the data?
In any successful modern organization, analytics is likely to play a central role in helping decision-makers design and execute effective business strategies. At IBM, as we work with clients across the globe, we’re seeing ever-increasing levels of maturity and confidence in data-driven business
Data, insights, cloud, agile, analytics. These are all terms that get thrown around a lot in technology these days. But the truth is that unless you can combine some or all of these concepts, the bottom line benefit to your business will likely not as great as you may expect.
For today’s data scientists and data engineers, the data lake is a concept that is both intriguing and often misunderstood. While there are many good resources about data lakes on ibm.com and other websites, there is also a lot of hype and spin. As a result, it can be difficult to get a clear
Building a data lake is one of the stepping stones towards data monetization use cases and many other advance revenue generating and competitive edge use cases. What are the building blocks of a “cognitive trusted data lake” enabled by machine learning and data science?