One could argue that many of the world’s problems can be solved with data. While I won’t be able to save the world just yet, I’d like to explain how statistical analysts and data experts use tools to understand data and how this data can then be managed to influence our environment.
We’re delighted to announce the launch of our all-new Business Analytics Community. It is an essential component of the IBM Community, which includes more than 100,000 users who connect, learn and share everything about IBM’s products and services.
Among organizations investing in AI hardware, software or services, more will buy IBM and rely on Watson than any other vendor. This according to a new IDC report which names IBM as 2018’s market leader in AI. So just what sets apart IBM as leader of the AI provider pack?
Today’s data science and analytics teams are often composed of individuals with a variety of skill sets, educational backgrounds, levels of exposure to open source tools and professional needs. Here’s a typical breakdown:
Business professionals need straightforward ways to first discover and then
Today, “doing more with less” is a key principle driving business strategy across many resource-intensive industries. Organisations are looking to get more out of artificial intelligence (AI) and machine learning (ML) than just great insights. They need access to recommendations that help simplify
Seizing the AI opportunity to tap new sources of energy inspired one ExxonMobil leader to take a collaborative approach to its big data problem. Now she’s been recognized by IBM as a top woman AI leader.
Recently, I sat down with Kyle Weeks, Program Director for Ecosystems in Data Science and AI. I wanted to review some exciting new opportunities made possible by several recent developments in IBM Data Science:
AutoAI, a powerful automated AI development capability in IBM Watson Studio, won the Best Innovation in Intelligent Automation Award, chosen by a panel of 13 independent judges yesterday for the AIconics AI Summit in San Francisco.
In my last blog post, I explained why businesses need product information management (PIM). I will now dive deeper into the key factors an organization must take into consideration when evaluating a PIM solution. Note that I am not going to cover anything about catalog, hierarchy, category