"We're in a position now where we can run mass checks against our entire system during the workday. By implementing ArcGIS Data Reviewer, we went from taking hours to find and fix errors to minutes."
Florida Utility Company Enhances QA/QC with Data Quality Management System
Clay County Utility Authority (CCUA), based in Middleburg, Florida, focuses on three utilities: water, wastewater, and reclaimed water. With a dedication to conservation and community, CCUA serves 54,000 customers using 1,500 miles of utility infrastructure. From hydrant safety to asset repair and replacement, data is central to the utility's daily operations. As such, operators need reliable data, which requires an efficient quality assurance and quality control (QA/QC) process.
Clay County is rapidly growing and expects 12,000 new homes within the next decade, leading to the creation and attribution of new assets for new subdivisions on a weekly, sometimes daily basis. The GIS team at CCUA needed to work quickly and efficiently to keep up with the workload while also ensuring data integrity. The group implemented a comprehensive data quality management system, yielding positive results across the organization.
Accurate data is vital to GIS applications
Sarah Grimsley, GIS Analyst II, at Clay County Utility Authority
High quality GIS data was needed to support accurate and timely completion of drawing utilities for new projects
ArcGIS Data Reviewer provided the ability to configure checks for any scenario in the utility system. Grimsley was able to build a comprehensive set of checks to meet business requirements.
Grimsley and her team went from several hours to minutes to find and fix errors in their data, thereby increasing the efficiency and turnaround time for new projects.
As GIS data is used to support numerous projects for new subdivisions, it is critical that the data be of the highest quality. In 2016, CCUA migrated to the Local Government Information Model (LGIM) for water utilities, which is a database schema provided by Esri for use by local governments. This schema supported data management for maps and apps that were deployed across the organization.
Prior to migration, CCUA had configured a comprehensive set of data checks using ArcGIS Data Reviewer, an extension for ArcGIS Pro, ArcGIS Enterprise, and ArcMap that automates and helps improves data quality and data quality management.
About a year and a half after implementation of the LGIM, these checks were incompatible with the new schema and needed to be reconfigured. During this gap, QC was conducted visually with one person manually checking each feature in the field. With well over 300,000 features across all three utility types, this process was resource intensive and left a lot of room for human error.
Due to the rapid growth in the county, the primary challenge CCUA's GIS department staff faced was timely and accurate completion of drawing new utilities for new subdivisions into the GIS. They needed to prioritize the reconfiguration of ArcGIS Data Reviewer for their QA/QC processes to work with the LGIM.
Daniel Johns, GIS manager, tasked Sarah Grimsley, GIS analyst II, with leading this process. Johns also wanted to see how it could be improved with regard to the efficiency and performance of the previous QA/QC implementation.
To configure the data checks using ArcGIS Data Reviewer, Grimsley first referenced old checks and the sample water utility checks from Esri's Data Reviewer for Water Utilities template. Then she categorized the checks by type (geometry, attributes, and point/line valency) for each utility type. Each utility now has data checks that are in accordance with its new schema, thereby ensuring that its data meets the business requirements for its GIS applications.
Overall, it took two weeks for Grimsley, who previously did not have any experience using ArcGIS Data Reviewer, to reconfigure the checks.
Grimsley liked the ability to configure checks using ArcGIS Data Reviewer. It allowed her to create a check for any scenario in the utility system, giving her flexibility and room to use the tool in other areas, such as asset management.
Once the checks are configured, ArcGIS Data Reviewer’s Batch Validate tool allows for choosing the features to validate. When the checks are done running, the errors found are stored and displayed in the Reviewer Table.
The CCUA GIS team has seen significant improvement in its QA/QC processes since implementing ArcGIS Data Reviewer for LGIM data schema. One such improvement was the speed of the checks. In addition to organizing the checks by type, the number of checks required for validation was dramatically reduced due to the updated schema. For example, domain codes for valves, mains, laterals, and casings are always one diameter. Fittings, however, can be different sizes on either end, resulting in 325 domain codes. With several hundred fitting sizes, hundreds of data checks were initially configured for each size.
In the LGIM schema, Grimsley configured this check using the Feature on Feature Check and applying an SQL expression within the check configuration according to fitting sizes against the equivalent pipe diameter. This significantly reduced the number of domain-related checks. Similar improvements were experienced with the valency and geometry checks. Depending on the utility, these checks now take only 20 to 30 minutes.
Feature on Feature check configured with SQL expressions to validate fitting sizes against pipe diameter.
"ArcGIS Data Reviewer allows us to do our job better. Using this application, we're able to quickly and efficiently turn around new projects and get them into our GIS production system," says Johns.
Technicians are responsible for conducting their own checks and making their own edits, and with ArcGIS Data Reviewer, Grimsley can now review the results of their QC work to see what was fixed. This allows her to track the edits completed by technicians and verify them to ensure that any further potential errors are caught, ultimately improving data quality. Grimsley is also using ArcGIS Data Reviewer to go back and run the checks on older areas to address any outstanding issues.
"The way things are set up now, we're pretty much catching everything. We're also using [ArcGIS Data Reviewer] to capture errors related to the assets that are surrounding that job," says Johns. "So getting this application back up and running against the LGIM schema was very vital."
The response from other departments at Clay County Utility has been positive. "We haven't advertised the use of ArcGIS Data Reviewer, so a lot of our colleagues are just operating under the impression that we're able to get work out a lot quicker and with fewer errors,” says Johns. "We no longer have to deal with the slow turnaround linked to our previous processes and can now provide data to our field staff much faster."
Grimsley adds, "I've been told by multiple people within the organization that they are impressed with how fast we are pumping out work after getting the batch checks running. With ArcGIS Data Reviewer being so quick and efficient, we can have required edits accessible in the GIS all within minutes."
Grimsley continues to add new checks to improve QA/QC within the utility and for the rest of the organization. She and Johns look forward to ArcGIS Data Reviewer continuing to streamline their QA/QC.
"ArcGIS Data Reviewer enables employees to operate more efficiently and reduce the amount of time it's taking them to do their work," says Johns. "I can see this playing an important role as we continue to grow."