Translation Program to Integrate the Common Roster Directly into RoD

We at Macrosoft are hopeful that the first Mutual Assistance Common Roster Template issued in the fall of 2016 will be adopted soon by the Regional Mutual Assistance Groups (RMAGs). The Common Roster Template is expected to be finalized soon, with 2017 becoming an eye opener for utilities to adopt a common data format.

Utilities using the RoD system will find it difficult to make the transition to the new common roster as there are significant differences between the data layout of the common roster and the utilities internal RoD roster. Our prime concern is the mismatch in the data layout feeds consisting of the data fields and the data field values. We will have to develop a translation program for RoD that bridges the gap between the data field and data field value between the common roster and the utilities internal RoD roster.

In order to streamline this mismatch in the data layout, we have come up with a two-step approach:

1. First map the fields between the common roster and the utilities internal RoD roster. We recommend performing a mapping exercise that maps each data field in the common roster to the corresponding field in the utilities internal RoD roster. This results in creating the data field mapping table embodying business rules required by the translation program.

2. Second, we need to develop and implement an automated program (using the mapped rules) directly into the RoD software, so that translation takes place each time a new common roster is either received or generated by the utility. This translation program will have to be customized for each utility using RoD software.

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The latest version of RoD offers utilities 3 different service levels of automation, so as to sync up RoD with the common roster. It is also necessary to consider the utilities firewall for each of the service levels as data is being transferred inside and outside the utilities firewall.

In service level 1, the utility receives common roster information (mostly an email with attached xls) from a responder which is uploaded to RoD. During the upload process the translation program will just follow the rules to populate the common roster field information into the corresponding team member information (TMI) within the RoD system. All the while the RoD and the common roster data is within the utilities firewall.

For service level 2, the utility sends common roster information (mostly an email with attached xls) from RoD to a partner utility in the common roster format. The translation program will do a reverse mapping of the RoD team member information, which will be populated into the Excel sheet as per the common roster template. Here the data sheet is still within the utilities firewall.

Finally, service level 3 offers utilities using RoD to exchange data with a high degree of automation in the crX cloud network. In this case the RoD system can directly import the Common roster from the crX exchange and likewise, use the reverse mode to export RoD team member information directly into a common roster on the utilities crX account. At no point during the import or export process the internal roster data within RoD is ever exposed beyond the utilities firewall. The outcome of this process ensures that utilities have a new common roster within their account area of crX that can be used to exchange information.

Moving forward the translation program will become less tedious and more-and-more utilities will be able to comply with the common roster of the RMAG they are associated with. Please download our White Paper to know more about building the translation program and see how this can be implemented to all the utilities using RoD software.

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About the Author

Dr. Ronald Mueller

Ron is CEO and Founder of Macrosoft, Inc.. Ron heads up all company strategic activities, and directs day-to-day work of the Leadership Team at Macrosoft. Ron is also Macrosoft’s Chief Scientist, defining and structuring Macrosoft’s path forward on new technologies and products, such as Cloud; Big Data; and AI. Ron has a Ph.D. in Theoretical Physics from New York University, and worked in physics for over a decade at Yale University, The Fusion Energy Institute in Princeton, NJ, and at Argonne National Laboratory. Ron also worked at Bell Laboratories in Murray Hill, NJ., where he managed a group on Big Data, including very early work on neural networks. Ron has a career-long passion in ultra-large-scale data processing and analysis including: predictive analytics; data mining, machine learning and neural networks.

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