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Utility Network Implementation for a Public Water Utility


GIS Assessment and Planning is a key component in getting the most return on your GIS. With the most current ESRI roadmap in mind, this particular water utility decided to be proactive in adopting the newest technology and capabilities provided by ESRI’s Utility Network solution to bring them into the future of GIS for Utility Systems. This included upgrading IT infrastructure (hardware and software), database standardization, and Utility Network (UN) implementation. The foresight was to better align the business goals to utilize GIS and bring life to the existing data in an architecture that would allow for better data driven decision making and future forward GIS utilization.


In order to implement these upgrades and prepare for a Utility Network implementation, upgrades to the organizations hardware and software systems were necessary. After the upgrade, a data health check was performed using Data Reviewer to establish a baseline of completeness and confidence level of existing data. While the Data Health check provided a baseline of features and attributes, a database architecture review was also needed to assess the structure in which the data was currently stored.


Adopting and implementing any new technology has its challenges and requires careful and considerate planning. The Utility Network is no different. Due to the complexity of this new structure, individualized configuration must be performed for each use case and it is common for it to take several iterations to get to a working product that performs as needed for the utility. In this particular use case, the implementation task included mapping features, fields, and attributes from a custom enterprise database structure to a standardized data model. This data migration was completed utilizing the data integration platform FME. The process takes existing data from their enterprise database model, proxied through FME, and loads it into the UN data shell. Once the data has been loaded, load errors are addressed, bulk data updates/edits are made, and UN rules are adjusted.

The goal early on is to reduce the number of errors as much as possible, however, errors will exist throughout this process and even exist within a working UN dataset. It is important to understand that within the errors, there are subsequent conflicts with the data model and data housed within it. So, for every conflict or error present, there is typically 2-3 more data conflicts. This can appear as significant issues at first but understand that by fixing the initial conflict it can resolve others downstream of the data.


The UN implementation was successful following this approach. The initial implementation included water and sewer datasets in a single UN system. After the first iteration, it was decided to split the two data sets into their own UN system. The following three iterations of loading from enterprise databases through FME into the UN allowed the conflict results to go from approximately 80,000 to a manageable 3,000 that are being addressed individually over time. Lessons learned from Magnolia River’s first successful water UN implementation are: invest the time and capital into hardware and software upgrades ahead of time, plan or roadmap the steps to success, communicate the process between client and consultant, and be patient.



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