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?
Does your company have a single source of truth for your business’ most important data? Then it’s time to learn why you need to invest in product information management.
Product information management (PIM) is a subset of the overarching Master Data Management (MDM) space, and it’s primary use is
The event formerly known as IBM Analytics University is happening again this fall. Join us at the Data and AI Forum on October 21–24 in Miami, Florida. Here’s a preview of what you can get out of this exciting event.
For 2019, we’ve expanded the curriculum to include the entire IBM Data and AI
In a previous blog, I explained how data science capabilities, massive parallel processing (MPP)
and usability improvements in data warehouse appliances can help the bottom line—and why old-fashioned architectures might not cut it. But what does that look like in practice?
Research firm Quark +
On June 12th, IBM debuted AutoAI, a new set of capabilities for Watson Studio designed to automate critical yet time-consuming tasks associated with designing, optimizing and governing AI in the enterprise. As a result, data scientists can be liberated to commit more time to designing, testing and
When businesses evaluate cloud infrastructure, databases or other core business systems, they nearly always consider scale as a decision factor. But scalability – the ability to grow as planning and business grows – is often overlooked for enterprise performance management.
So what do companies
While data is an enterprise’s most valuable resource when it comes to gaining competitive advantage and improving business performance, time is a critical component. Businesses run 24x7, tasking our data citizens to maximize actionable insights that will drive the actions of tomorrow.
In my last blog, I stressed the need for a modern data architecture (MDA) to underpin the next generation of the cognitive enterprise, fully harness data using the latest technologies, and sustain a platform-centric business model that supports people, process and technology optimized around
IBM Cloud Pak for Data System is an integrated end-to-end platform that is cloud native by design, architected as microservices and containerized workloads. It offers instant pre-assembled provisioning and has capabilities to collect, organize and analyze data. It takes the IBM Cloud Pak for Data
There is no AI without data. That’s why we’ve put together a prescriptive set of five steps we call the ladder to AI to help our enterprise clients get their data ready. The journey of the AI ladder starts with collecting the data you need to build models, followed by organizing your data so you
A modern data architecture (MDA) must support the next generation cognitive enterprise which is characterized by the ability to fully exploit data using exponential technologies like pervasive artificial intelligence (AI), automation, Internet of Things (IoT) and blockchain. In addition, an MDA