Rather than worry about your future performance, work to be the best today. The rest will follow. Seize the opportunity to respond to customers in the moment, rather than react too late. That way, everyone benefits.
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.
Where is AI headed? How should you be thinking about AI? What should you be doing with AI? To answer these questions, we recently convened a round table of tech influencers to discuss some of the most burning issues arising from AI — starting with why we should think of it as “augmented
Welcome to Thoughts on AI, a new blog series focused on issues and advances in artificial intelligence. We’ll cover the technologies and people shaping the future AI. First up: avoiding bias built into intelligence and analytics.
Augmented reality (AR) and augmented intelligence systems such as Watson are breaking data outside the confines of a two-dimensional monitor and putting them into a three-dimensional visualization format. Big Data and Analytics Hub spoke with IBM AR designer Ben Resnick about what’s next for
If you work in the field of sales commissions, you’re likely aware of ASC 606, the five-step revenue recognition model and timelines. The basic premise on which both ASC 606 and IFRS 15 have been formulated is that an organization can recognize revenue from a customer contract only when the
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.
Machine learning concerns in Silicon Valley tend to be different from those elsewhere in the U.S. — and outside of the U.S. So, here are five tips for those hearing about machine learning efforts in Silicon Valley, but who work elsewhere. These suggestions consider where machine learning and data