Unleash the power of text analytics on your dark data with IBM Watson Explorer for Data Science Experience

With data coming from so many different sources nowadays (both old and new, both internal and external), it is inevitable that data will arrive in many different structures, schema, and formats, with other variables for latency, concurrency, and requirements for storage and processing. When data types are extremely diverse and combined, we now call it “hybrid data.” This usually drives users to deploy many types of databases and different platforms to capture, store, process, and analyze the data, which in turn results in hybrid data management architectures.

A growing number of organizations are turning to Docker containers to help solve really big application requirements. Among the biggest out there are those imposed by Oracle and Oracle RAC. What if you could deploy Oracle or Oracle RAC as a stateless cloud-native workload in your environment? Transform a complex process into one with an App Store-like experience.

It’s no secret that trust provides a competitive advantage, with trusted companies outperforming their market peers. Boards, executives, and businesses across the globe want to find ways to build trust with consumers, employees, investors, and all stakeholders. But how do you define metrics, quantify, and measure trust?

Today’s data-savvy companies actively seek ways to enhance their approach to analytics and evolve out of the past. While the first wave of BI involved static, rigid, IT-owned systems, and the second expanded the capabilities to a more business-facing analyst set, the third wave aims to infuse analytical activity into multiple layers of non-technical business roles. As a key strategy to enable this third wave, companies today are exploring an embedded approach that places analytical activity directly in the context of everyday user applications, and the independent software vendors (ISVs) and enterprises providing those applications have taken notice of this trend. Done efficiently, embedded analytics promotes faster deployment and pervasive usage of analytics for end-users while delivering a competitive advantage and the opportunity for new revenue streams for software vendors and enterprises.