Systems | Development | Analytics | API | Testing

Companies have had only mixed results in their decades-long quest to make better decisions by harnessing enterprise data. But as a new generation of technologies make it easier than ever to unlock the value of business information, change is coming. We’ve already reaped gains at Hitachi Vantara, where I run a global IT team that supports 11,000 employees and helps more than 10,000 customers rapidly scale digital businesses.

I’m starting to hear questions like: “What comes next?” “Do things go back to the way they were?” “Are some of the changes wrought by the pandemic here to stay?” I think we all know part of the answer: there is no going (all the way) back. In the analog world, sure, we need some things to revert to bounce back. We need to revitalize retail, tourism and hospitality to get our economies moving again.

With billions of industrial IoT (IIOT) devices in place, generating massive volumes of data from “the edge,” the potential for proof of concept success for use cases in the factory can be paralyzing. While the value of this digital revolution, aka Industry 4.0, is clear, realizing the full promise has been slow. Research and real-life experience from Accenture shows that many manufacturers get stuck early on or can’t get beyond proof-of-concept pilots to scale.

The move into any new technology requires planning and coordinated effort to ensure a successful transition. This blog will describe the four paths to move from a legacy platform such as Cloudera CDH or HDP into CDP Public Cloud or CDP Private Cloud. The four paths are In-place Upgrade, Side-car Migration, Rolling Side-car Migration, and Migrate to Public Cloud.

Many people consider data-driven culture exclusively as one that comprises a large data team (analysts, data scientists), perfect data-sets neatly organized in a structured data warehouse, and the perfect tools to both access and utilize data in every decision made. Ultimately, however, much of data-driven culture comes down to a clear focus on the end-goal. Many people forget data is just an implementation, something that helps us in what we do.