How to build a modern data management platform ready for the AI future
Every data architect knows the value of keeping their data management platform up-to-date and ready for the next phase. Yet how to put this into practice is not always clear. With many businesses embarking on their journey to AI
With the publication of Gartner’s 2019 Magic Quadrant (MQ) for Operational Database Management Systems, we were happy to see recognition of some of our key efforts from the past year. The integration of the Db2 common SQL engine and other rich features, edition simplification, commitment to
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?
68 percent of surveyed businesses recently responded that they use machine learning (ML) or plan to do so in the next three years. AI technologies rapidly are becoming how businesses distinguish themselves from competitors. But choosing the best way to implement AI isn’t always a straightforward
Artificial intelligence and machine learning (ML) have become very popular recently due to their ability to both optimize processes and provide the deep insights that push enterprises and industries forward. In fact, 68 percent of respondents in a recent 451 Research Report, Accelerating AI with
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 +
Information analytics has never been a “one size fits all” proposition. That applies to the hardware and software technologies organizations employ, the information being parsed and the goals of specific projects.
Data visualization techniques can give data scientists a vital tool for representing the data that analysts and line-of-business users need to make strategic decisions. Discover how a few simple considerations of a specific data set in a real-world use case enables data scientists to implement cost