Rethinking Data Management and the Cloud: Horizontal Data Value Chain, Part 1


Like our customers, there’s a lot we’re learning as we redefine GE as a data-driven business. Among other things, we are deploying data management, predictive analytics, and advanced control systems across all of our businesses. To me, this is incredibly exciting; at GE, we’re not just selling software solutions, but also utilizing these new technologies in our own plants and facilities to drive operational improvements and increase productivity.

Declining sensor, storage, and memory costs are causing industrial companies alike to rethink how they manage the data in the field in order to maximize the value of the analysis in the cloud. 

One of the things that our customers are doing is looking at how to manage their data across the enterprise and to run analytical queries that provide immediate tangible insights to Operations and improve business metrics. Critical to this journey is bringing a wide array of data together and relating it into a common structure so you can then perform these types of analysis and other analysis with the help of enterprise historian solutions.

What we’re seeing is a lot of digital infrastructure renewal where this data set is then looked at as a flow horizontally across the enterprise and really into a cloud-based environment where that data can then be managed in much more cost-effective ways. 

Industrial companies desire to have a common data management capability, and we’re finding that our historical capabilities in control systems, in data management with our intelligent platforms, and processing capabilities in the field are complimented very well with our capabilities in the cloud. And we’re starting to design this data flow holistically with our cloud platform for the Industrial Internet called Predix.

Horizontally, this data value chain from the control system to the cloud is delivering a couple of outcomes right off the bat:

  • A dramatic decrease in cost. GE is nothing if not responsible with regard to the bottom line. If we start to manage operational data more effectively across the board, we then see a real decrease in cost of IT infrastructure and operations. 
  • An increase in speed. Speed of deployment, speed of development of new analytical queries, and applications. A unified, systemic approach results in repeatable speed and time to value.
  • A decrease in complexity with focus on optimizing at an infrastructure level capability within the field. 

So there are some real benefits: reduced cost, reduced complexity, and increased speed with which we can develop and manage in the field—all very compelling outcomes. Companies that can leverage this data value chain horizontally across their businesses, from their machines to the cloud, will reach new levels of efficiency and business performance. 

In my next blog on rethinking data management and the cloud, I’ll discuss other outcomes businesses can leverage through the use of analytics across the enterprise—enabled by the Industrial Internet.

Jeremiah Stone

As General Manager, Industrial Data Intelligence, GE Intelligent Platforms Software, Jeremiah Stone is responsible for identifying and evolving architectures and solution offerings that leverage GE’s innovation in analytics, digital and the Industrial Internet. Jeremiah was previously Vice President Natural Resource Industries and Sustainability Solutions at SAP, where he was responsible for strategy, market research, investment cases and development for SAP’s Sustainability, Oil and Gas, Chemicals, Mill Products and Mining industry portfolios. Additionally, Jeremiah was a leader within SAP’s cross-functional adoption of Lean and Agile software development as well as continuous improvement of solution and system oriented design. Prior to joining SAP Labs Jeremiah started his career as a programmer and systems administrator with the National Center for Atmospheric Research. Jeremiah is a graduate of the University of Colorado's Mathematics program, Summa Cum Laude, is an inventor or co-inventor of multiple U.S. patents and several publications.

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