AES Ohio, AES Indiana, and MVLt are part of the AES Corporation, which is a global company of nearly 25,000 employees. Indiana and Ohio combined serve approximately one million customers and both thousands of distribution and transmission lines. MVLt serves approximately 62,000 lights as well as 180 governmental and 200 commercial customers. In their current environment AES Ohio, AES Indiana, and MVLt are on varying GIS platforms as well as work management and graphic work design solutions. AES is currently undergoing significant digital transformation, implementing consolidated systems to support graphic work Design as well as an SAP implementation for work management. Another key aspect to this transformation and consolidation is the implementation of ESRI’s Utility Network. The UN project goals are to align data models across the three companies as well as alignment of solutions around the ESRI platform and SSP’s Productivity.

The UN project started with defining a common data model for Indiana, Ohio, and MVLt. The workshops included several weeks of discussing details in a tabular form around the current state data and the approach for future state. In those workshops we defined the nuance of the data such as Asset Groups, Asset Types, and the attributes needed to serve the future state solutions. We generated tabular content intended to drive our migration strategy as well as a means for AES to review and sign off on the decisions made. As we made our way through the finalization of the data modeling decisions, we identified several challenges that were presenting roadblocks. The challenges presented were namely centered around two key components which were, AES resource bandwidth due to the volume of digital transformation and the lack of visualization around the data decisions. Another challenge during this step of the project using tabular feedback, was that it also made it more difficult to introduce UN concepts that impacted these data decisions. Overall, without spatial visualization of the data these challenges made sign off difficult, which led us to introducing the concept of the micro pilot.

The micro pilot approach was presented to AES as a quicker introduction to the visualization of these data decisions within ArcGIS Pro. The micro pilot came with several benefits at this stage of our project. The first benefit was for us to quickly load data without needing more finalization around some of the decisions. Typically, the implementation of a data pilot comes further into the schedule of a project utilizing a more formal data migration process. Easier collaboration and a more agile iterative approach to changes in the data model were other net benefits to the micro pilot at this stage. The more tabular driven approach can potentially introduce a slower change process when making corrections to the data model further into the project. The largest benefit to the micro pilot at this stage was visualization of the data and the attribution surrounding it which allowed us to validate these decisions much earlier.

The process to generate the micro pilot consisted of out of the box ESRI tools. The tools allowed us to load the source date quickly and efficiently for both Indiana and Ohio respectively into an Asset Package. The decision to keep all source fields even with attribute driven decisions already made, allowed us to easily see where data immediately aligned and, in some cases, modify our decisions. The contents of the micro pilot consisted of the Asset Groups, Asset types, Attribute fields, and spatial data from the source content in ArcGIS Pro. It is important to note that the micro pilot did not contain full UN configuration, Attribute Rules, UN Rules, Topology, and even domains applied at this stage. The initial effort was to visually represent all the decisions made at this stage as well as all the source data, and to use the micro pilot to further build out the details.

After generating the micro pilot content, it became our primary feedback mechanism going forward as we developed the data model further. In this more agile visual format, we can support questions and answers much easier. The ability to directly look at a combination of current state data in the future formatting and resolve meaningful decisions at this early of a stage in the project has been invaluable. The ability to quickly represent options around connectivity decisions, containment options, symbology, labeling, and various other details has been very valuable to both development as well as educating AES on the selected approach. The visualization of these concepts has also allowed us to help AES take these new directions and present them to a much wider audience within the company and quickly answer questions from outside organizations. The benefit of the micro pilot has also aided us at SSP to better ask questions around intended use cases, give us a clearer picture of migration efforts based on the current data and the chosen details of the data model, and ultimately have a better fundamental understanding of how the data fits together and how to better serve AES.