The best data catalogs can automate the process to collect, classify and profile data to ensure the highest standards of quality. Here are three popular use cases detailing why companies are moving towards IBM’s Watson Knowledge Catalog.
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?
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.
It’s no surprise: most companies working with stream data today say they are planning to make changes to drive greater value. Advancements in machine learning (ML) and very-high-speed data persistence for real-time analytics are reshaping strategies and architectures. In addition, 88 percent of
In business, aspiring to world-class is not enough when your competitors are already there. About half of the companies listed on the S&P 500 will be replaced over the next 10 years. Compared to the past, what’s unique about the disruption happening today is the rapid pace of change. During
Capitalogix is a hedge fund, but it’s really a data science firm in disguise. They work to understand and exploit capital markets by building custom data science models that can analyze massive amounts of data from as many sources as possible. Capitalogix’s need for high-performance analytics and
Many companies struggle with outdated, duplicate or incorrect customer data. At Localiza, we can go beyond identifying the customer by name, profession and role -- to customizing the entire experience based on the customer’s past history with us. With this 360-degree view of each customer’s current
In part one of the Capitalogix data science story, I focused on their strategic need for a data platform that supports speed, data variety and custom-built algorithms to find advantages for their business. A key success driver: they worked to make life better for the people on the front lines of
With the automated AI and ML advancements, you may find yourself wondering--what are the overall impacts to business? How will all of this technological progress impact the ways we run our business and perform our jobs?
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
Intel's Melvin Greer, Senior Principal Engineer and Chief Data Scientist, Americas writes about the data strategy necessary to execute the promises of AI and touts their collaboration with IBM on Cloud Pak for Data. But before anyone can execute an AI strategy, they’ll need a data strategy.
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