Industrial IoT, the largest segment of the Internet of things (IoT) with the highest potential value, will require a deep integration between modern IT (Information Technology) and OT (Operations Technology). Modern IT technologies, to be truly extensible to OT, will need a hybrid cloud approach, with by far the majority of data and processing residing at the so-called "Edge". Architectures and software written by industrialists for industrialists, such as GE Predix, are showing how that can work.

In the big data domain, businesses are trying to solve complex problems with complex and novel technology -- and often failing. Simplifying the packaging of big data technologies will streamline big data pilots and accelerate big data time-to-value. CIOs looking to establish differentiating big data capabilities need to consider Single Managed Entities to help solve the complexity problem.

The premise tested in this research is that high value applications and software should be run on more capable converged performance-optimized infrastructure, even when they constitute a small proportion of the total workload. In contrast, cost-optimized infrastructure will save on short-term hardware costs, but will incur much higher overall costs long-term. The conclusion strongly recommends IT executives adopt a default of performance-optimized converged infrastructure for all mixed workloads when even a small proportion includes high-cost software and/or high-value applications.

The public cloud competitive environment remains turbulent, but sectors show signs of crystallizing a longer-term market structure.. SaaS remains turbulent with SaaS vendors successfully gaining share vs. incumbent licensed software providers who must migrate their core products to cloud-friendly offerings and/or acquire native SaaS applications to shore up their applications leadership positions. Public cloud IaaS segment leadership is crystallizing to a handful of viable providers as a function of scale requirements. PaaS is just formulating and finding its way, but is likely to gravitate towards a wide variety of development models suited for different application types and public cloud platforms.

Mainstream enterprises are beginning to deploy machine learning applications that add differentiation to traditional products and services. But the complexity of their big data infrastructure is becoming a problem. Big data pros need to deploy a digital business platform (DBP) to support modern applications and then extend the DBP to machine learning applications.