Powered by a fast in-memory database that can ingest and analyze massive amounts of data - one million events per second - IBM's new Cloud Private for Data is an integrated data science, data engineering and app building platform. Designed to help companies uncover previously unobtainable insights from their data, the platform is also designed to enable users to build and exploit event-driven applications capable of analyzing the torrents of data from things like IoT sensors, online commerce, mobile devices, and more.
"Whether they are aware of it or not, every company is on a journey to AI as the ultimate driver of business transformation", stated Rob Thomas, General Manager, IBM Analytics. "But for them to get there, they need to put in place an information architecture for collecting, managing and analyzing their data. With today's announcements, we are planning to bring the AI destination closer and give access to powerful machine learning and data science technologies that can turn data into game-changing insight."
Launching on the IBM Cloud Private platform, Cloud Private for Data is an application layer deployed on the Kubernetes open-source container software and can be deployed in minutes. Using microservices, it forms a truly integrated environment for data science and application development. In the future, the Cloud Private for Data will run on all clouds, as well as be available in industry-specific solutions, for financial services, healthcare, manufacturing, and more.
Commenting on the news, Christian Rodatus, CEO of IBM business partner Datameer, said that "two of the biggest challenges for Data scientists is cleansing and shaping data, and operationalizing their insights to deliver value to business. The direction IBM is headed with IBM Cloud Private for Data is aligned with Datameer's strategy and can enable companies to more quickly prepare data for machine learning and AI projects and operationalize these across their organisations".
The Cloud Private Data solution also includes key capabilities from IBM's Data Science Experience, Information Analyzer, Information Governance Catalogue, Data Stage, Db2 and Db2 Warehouse. The cohesive set of capabilities is designed to help Cloud Private clients quickly discover insights from their core business data, while keeping that data in a protected, controlled environment. In other words, the new solution is designed to provide a data infrastructure layer for AI behind the firewall.
Separately, IBM announced the formation of the Data Science Elite Team - a new no-charge consultancy dedicated to solving clients' real-world data science problems and to assisting them in their journey to AI.
According to a report from MIT Sloan, Reshaping Business with Artificial Intelligence , an estimated 85% of 3,000 business leaders surveyed believed artificial intelligence (AI) would enable competitive advantage, however, only about 1-in-5 have done anything about it. For many organisations, the task of understanding, organizing and managing their data at the enterprise level was too complex.
That's where the new Data Science Elite Team comes in. This global team of data scientists, machine learning engineers, and decision optimization engineers is dedicated to assisting clients on-site to begin helping them better understand and control their data, and to start making machine learning an integral part of their business.
"Nedbank has a long tradition of using analytics on internal, structured data", stated Patricia Maqetuka, Chief Data Officer, Nedbank Ltd. "More data is available now than has ever been available before and analytical tooling has undergone rapid evolution in order to keep up. Nedbank has embarked on a journey to start leveraging both internal and external data, creating new data driven business models and new sources of revenue. Thanks to the first IBM Analytics University Live we were exposed to the guidance and counsel of IBM's Elite team. This team helped us to unlock new paradigms about how we think about our analytics and change the way we look at use cases to unlock business value."
Specifically, Data Science Elite Team client engagements center around a use case and begin with a discovery workshop that helps clients understand their data environment and break this use case down into 3 to 4 discrete deliverables that can each be realized in two to three weeks. Following the workshop, clients are provided access to powerful data science strategies, technologies and methodologies through data science sprints and validation. The team, which comprises more than 30 people now, and is expected to grow to 200 over the next few years, is currently aiding more than 50 clients.