The management of industrial technology has traditionally been split between two separate fields: information technology (IT) and operations technology (OT). IT worked from the top down, deploying and maintaining data-driven infrastructure largely to the management side of business. OT built from the ground up, starting with machinery, equipment and assets, before moving up to monitoring and industrial-control systems.
With smarter machines, Big Data and the industrial Internet, the worlds of IT and OT are converging. Traditional enterprise data management such as ERP or CRM is being dwarfed by operations data due to sheer volume and variety. But most of this data is still in the dark.
There is a need to link analytical systems to operational systems. Today most business analytics do not support any connection back to the originating systems of data — analytics are on an island as well, inhibiting the ability to take action in a reliable and effective way due to the onus on the individual to connect the worlds in their brain and connect the systems and workflows via their own initiative.
IT and OT, developed separately with independent systems architectures, need to come together and find common ground to develop a new infrastructure.
Digitizing operations has become an imperative for the modern industrial corporation. Technological progress in computing, sensing, storage and communications technologies has made it easier, faster and cheaper for organizations to accelerate adoption of Big Data and asset management technologies.
However, with the growing volume of data from assets and operations, there are significant challenges that asset-centric companies need to overcome to reap the benefits of digitization. In this slideshow, Shefali Patel, director of strategy and marketing, GE Digital, discusses the challenges inherent in working with large volumes of data and how to successfully leverage enterprise data.