The Energy, Policy, and Innovation Center (EPICenter) has released a study that examines the Industrial Internet of Things (iIoT) and the factors that affect the sharing of Industrial Data (ID) in the Southeastern U.S. The authors, Karim Farhat, a PhD. candidate in the Georgia Tech School of Public Policy, and Milton Mueller, Professor in the Georgia Tech School of Public Policy, built on a previous EPICenter study by Georgia Tech Associate Professor Jennifer Clark which was released in 2018.  The Clark paper examined the state of industrial data sharing in the Southeast, while the Farhat and Mueller paper delved into the factors that play into the industrial data sharing decision making process.  Electric power industrial data are the data produced and collected from real-time operations during energy production. With terabytes of data being collected, the challenge facing the electric industry sector is how to turn this data into meaningful metrics that can make this industry more effective and profitable. Reaching these goals requires both internal processes that ensure the reliability and integrity of the data, and external processes such as data standardization, to ensure that data structure and meaning is comparable and translatable between organizations.

The emergence of industrial data sharing is a recent phenomenon made possible by advancements in artificial intelligence and cloud computing technologies. Leveraging the large volumes of data produced and collected to improve electric power service and profitability is a challenge that requires a multi-prong approach that integrates a highly skilled workforce, data sharing agreements between firms, with policy that enumerates data standards, to ensure the transparency and integrity of shared data.

While the Clark paper found that a regional component is essential to the success of industrial data sharing success, Mueller and Farhat found that the primary reason for a regional focus is for data diffusion. Mueller and Farhat argue that “The evolution of firms, industries, regional institutional structures, and skilled labor that are required to transform this domain knowledge into actionable insights are disaggregated across locations globally. Consequently, national data policies matter for the evolution of the emerging ID industry”. In other words, regionality is important for data and knowledge diffusion, but since other factors that impact the adoption of ID sharing are not regional, policy at the national level will have a larger impact than state of regional polices. They do point out that a regional contextualization is important for future studies since innovation clusters exist in the aviation, energy, automotive manufacturing, and logistics industry in the Southeast. Understanding the full context of data sharing and data governance challenges can spur economic development and support innovation within the Southeastern energy sector.

Please find these reports on the EPICenter website.