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INTERNET OF THINGS DATA INTEGRATION IN THE INTERNET OF THINGS speaks frankly about how utilities and energy companies are impacted by the Industrial Internet of Things, focusing on the several challenges related to data integration as more and more devices get connected to the grid and generate additional data streams. The Industrial Internet of Things (IIoT) is creating a fundamental shift in advanced energy production and distribution technology, management and services while leveraging existing investments in infrastructure and operations. Utilities need to become more agile and flexible in light of increasing renewable and distributed power generation, both of which demand a more flexible smart grid that can handle multiple energy sources in a decentralised and bi-directional network. Utility companies are also expected to improve routine procedures such as outage management, predict asset performance and transform energy data into new services. To perform these tasks, data generated through sensors and other intelligent technologies need to be integrated, combining data from disparate sources into meaningful and valuable information. In an interview with Metering & Smart Energy International, Franco Castaldini, vice president: marketing at Bit Stew Systems, 46 Castaldini begins by defining the Industrial Internet of Things as the process of “leveraging data from connected devices and turning that data into new business value associated with asset performance and operational intelligence and efficiency.” In order to gain a better understanding of some of obstacles faced by industrial organisations taking on the IIoT, Bit Stew commissioned a survey that gathered the feedback of over 100 IT and operational executives. The focus was on the steps that industrial organisations are taking to prepare for the Industrial Internet of Things, the potential benefits the IIoT offers their businesses, and the major hurdles encountered along the way. The survey also served to establish whether multiple industries employing IIoT are experiencing similar challenges with regard to data integration. The survey revealed that as the IIoT matures, confidence in current data integration tools declines by half. The survey respondents indicated higher confidence in their existing data integration tools during the planning stages which steeply declined once they actually began implementing an IIoT solution. They discovered that their existing approach/ tools struggle to accommodate the volume and complexity of industrial data. Early adopters of IIoT have indicated challenges around the ‘technology limitations’ of current data integration tools. On the IT side, this refers to the extract, transform and load (ETL) tools, which are used in the process of extracting data from source systems and transform these into a new model that can be used for data warehousing. In Industrial IoT, data volumes increase substantially compared to consumer markets. Apart from volume, there is also complexity around data variety. Castaldini says: “It’s one thing to integrate data that is highly structured from relational databases that support your typical enterprise systems. It’s another to take data from your OT systems such as your distribution management system, outage management system and GIS system and try to bring data from these systems together with enterprise data. It’s a much taller ask than simply integrating data from traditional applications that IT has typically managed. IT/OT data convergence is a huge challenge.” Castaldini adds that there is a lack of access to the right skillsets, noting that early adopters are hiring data scientists and their equivalents on the OT side. He says most IT and OT departments will outsource data integration tasks to a system integrator who will spend time with individuals close to that data. “There tends to be a lot of back and forth between the system integrator and the utility, costing a significant amount of time and money, only to establish a baseline understanding of the data, so that you can start to map the data from source to target. Data mapping is one step in the data integration process whereby you are transforming the data to fit it to the target data model you are working with." METERING INTERNATIONAL ISSUE – 5 | 2016