Adoption of IoT is going to continue to drive an adoption of hybrid cloud. As a result, the solution to these problems cannot simply be providing a managed service for an incredibly complex product. The product itself needs to be simple and easy to use, yet highly scalable, so that it can be deployed and managed directly on the edge and in the cloud with the same level of effort and skill set. In order to continue to drive and support innovation, the data management industry needs to design and build the traditional big data architectures required to consume IoT data and transform that data into valuable insights, identify patterns and make it actionable.
While DevOps is a well known and popular term, DataOps is now emerging as a practice that is of equal importance. DataOps is a blend of data science, DevOps, business intelligence, and data engineering. The goal is to produce agile, actionable, repeatable practices within big data to allow companies to see true value from big data. As data grows exponentially year over year the infrastructure and skills sets to manage that data are becoming more complex. By building competency in DataOps, companies can have groups that can work alongside their existing teams augmenting their existing capabilities pre-emptively.