The fusing of analytics with leading technologies can unlock significant business value and bring new transformation opportunities for enterprise companies. In order to be successful, analytics-based initiatives such as AI and the Internet of Things (IoT) need massive amounts of big data—and also
Choosing the right data management solutions as the foundation for AI is crucial. Enabling AI optimization and usability is paramount, as is easy scalability to accommodate the increasing amount of data used by AI applications. This is true no matter where you store your data: on-premises, in the
Nearly every business is under competitive, disruptive, and regulatory pressures. As companies face digital transformation and modernization to meet their customers’ expectations, leveraging data and AI at the speed of business can be the biggest differentiator.
However, according to MIT Sloan, 81
The best decisions are made by extracting value from all the disparate data across your business. Yet aggregating data across external sources, regional silos and various forms of storage is not an easy challenge to solve.
Data-powered businesses need always-on access to data to keep operations
For the past nine years, Stack Overflow, a question-and-answer website for programmers, has polled developers to understand what technologies they are using and to find out what technologies they want to work with next. This year, the nearly 90,000 survey participants revealed that, once again,
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 +
“In 2021, AI augmentation will generate $2.9 trillion in business value and recover 6.2 billion hours of worker productivity,” according to Gartner. It will do so largely by learning how to make better predictions over time and supplementing people’s ability to complete tasks in more natural ways
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
Financial services organizations face considerable challenges today. From regulatory changes to globalization to shifting customer expectations, the urgent need to re-engineer outdated systems to better manage vast amount of data can apply additional pressure. Organizations must deal with the
Together, IBM and Cloudera offer a modern data platform with the governance and security to drive the future of AI and ML. Our solutions are optimized for the cloud, but we give our customers options to put their data where it works best for them.