There are some people, like me, who like to know how the story ends and thus may occasionally read the last chapter before going back and reading the rest of the book. So, I guess this is a spoiler alert. The answer to the question is, “Netezza is still alive, well and evolving and IBM has now come
As organizations become more data-driven, they need to manage large volumes of data in a simplified and cost-effective way in order to remain competitive and nimble. Data stored in various data repositories, whether it be generated by client-facing applications or different internal and external
As we grow smarter and more sophisticated, thanks to rapidly enhancing technological innovations, enterprise data management and analytics have to keep pace to ensure organizations continue to remain effective and data- and insights-driven.
Let’s not underestimate the sheer scale of the problem
Today, it’s slightly inaccurate to say that “open source is the future.” Open source is here, and it has already won. Open source has been adopted by nearly every business discipline, allowing developers to solve their problems effectively with more flexibility and freedom. In enterprise business,
CIOs and other technology innovators are boldly leading their companies through change during this unprecedented time. As IT leaders make their journey to the cloud and prepare their business for the future, greater application modernization and agility is needed to meet these new marketplace
Today, “doing more with less” is a key principle driving business strategy across many resource-intensive industries. Organisations are looking to get more out of artificial intelligence (AI) and machine learning (ML) than just great insights. They need access to recommendations that help simplify
Two of the greatest challenges faced by organizations today are the rising volume of data and the lack of confidence to act on the insights this data reveals. Fortunately, there are AI-fueled data management solutions that directly address these two challenges to make data simple and accessible.
As an open, Kubernetes-based, data and AI platform, IBM Cloud Pak for Data integrates with an array of technology solutions that enhance organizations’ ability to make their data ready for AI. Among those core to the platform are DataOps capabilities that help operationalize data protection, data
It’s been said that data is the most valuable resource on the planet. But most companies aren’t getting the maximum value out of their data. If you look at the top three things that are really needed in the marketplace, it's really been around defining a data strategy, filling the skill shortage
In the years leading to 2020, public cloud databases were commonly seen as “dev/test” environments for applications living on-premises. And while this use case is important and should continue to be implemented across IT functions, businesses must expand more workloads into cloud to realize
Data breaches have far reaching consequences. They pose a significant financial cost in lost business, fines, and remediation, often averaging 3.92 million USD according to a study by the Ponemon Institute. Their impact on an organization's reputation spans many years. An organization's first step
Cloud Pak for Data, IBM’s leading data and AI platform, partners with Figure Eight to help companies address this growing sensitivity around data and make sure that security lies at the heart of any data-driven AI strategy.
To enable companies to get the most out of their machine learning, Cloud Pak for Data, IBM’s leading data and AI platform, partners with Datameer to build an end-to-end pipeline that collects, organizes, and analyzes data and helps infuse AI throughout the business.