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
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
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
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.
Owens-Illinois (O-I), the world’s largest manufacturer of glass containers, recently undertook a global migration from Oracle to Db2. Learn more about the migration and its success from O-I executives.
Big data doesn’t need to be a daunting challenge for small or midsized business (SMBs). Accessing, storing and exploring big data can be done by businesses of any size. An influx of data from sensors, streaming audio and video log files, web, and social media are increasing the volume, velocity,
A company only survives for 115 years by reinventing itself, questioning assumptions, and constantly looking for an edge. Owens-Illinois (O-I), the world’s largest manufacturer of glass containers, used worldwide by many leading food and beverage brands, recently began just such a reinvention.
If your business hasn’t already embraced a multiple-cloud strategy, it’s likely that one is in your near future. Recent research shows that 85 percent of companies already operate in multicloud environments, and 98 percent of companies plan to be multicloud by 2021.
While cloud-based platforms
IBM anticipated barriers to scaling enterprise AI. We developed a platform to help clients operationalize AI faster while infusing trust and transparency with IBM Cloud Private for Data and the add-on Watson OpenScale.