An enterprise asset management system helps you collect and visualize data. But human error and inconsistent processes can still create gaps in the information you rely on for decision-making. By aligning your workflows and technology with your organization’s data quality standards, you can implement a strategic quality assurance (QA) program and alleviate the need for constant data monitoring.

The most successful quality assurance programs rely on a culture of democratized, rather than governed, data quality management. The City of Garland, Texas, Water Information Systems team cultivated a collaborative quality assurance program with five key tactics: custom inboxes, clear communication, ongoing training, routine reporting, and automated GIS database checks.

1. Custom Inboxes

We created tailored Cityworks inboxes for each division as well as for specific team members, providing them with maps, charts, and individualized QA panels for quick reference. Since our water operators generate the majority of all service requests, each operator has their own QA inbox tab with three saved searches. These saved searches track the main components of a request to ensure it gets to the correct inbox: Division, Category, and Geocoded Address.

General QA inboxes monitored by the Water Information Services team can also assist in spotting trends. For example, if a crew member is consistently struggling with portions of a work order or inspection workflow, the right search and inbox can highlight those issues and create opportunities for training.

2. Clear Communication

The approach taken to address mistakes is just as important as the approach taken to find those mistakes in the first place. The Water Information Services team has invested time into building trust and rapport with the field teams. Communicating why we collect certain data helps the crews understand the importance of data quality monitoring and has helped them feel comfortable with suggesting process improvements so we all have the information we need to be successful.

Prioritizing relationship-building with the field crews is a crucial component to how data quality management is perceived. It is the difference between engaging someone as a contributor who understands how information is used versus telling someone that they have made a mistake.

3. Ongoing Training

We have instituted an ongoing refresher training for the crews that covers maps, inboxes, and searches, service requests, work orders, and inspections. We are able to customize the training by division based on their individual workflows.

These trainings provide an opportunity for crews to revisit best practices during periods of personnel turnover and role changes. The trainings also lead to questions and discussions that spark continued process improvements.

4. Routine Reports

Reports provide another means for ensuring high-quality data. Supervisors of high-volume crews, such as our Pump Maintenance Division, have reports like “Aging Work Orders” to highlight work orders that may have been missed. Additionally, a QA report for the Pump Maintenance Division shows discrepancies in work orders and inspections to help the supervisor focus attention where it is most needed.

Routine Report
Routine report.

5. Data Reviewer

Quality assurance doesn’t just apply to our Cityworks data. Our GIS data also plays a significant role in our planning, analytics and reporting, and asset management. The ArcGIS Data Reviewer tool from Esri allows users to create a single rule or an entire batch of rules that can be run ad hoc or on a regular cadence.

Our initial batch of rules was set up to reflect our base data standards and run on a schedule so that data quality can be maintained and measured over time. Our rules include checks on unique IDs, geometric network integrity checks, and attribute table checks to verify that our assets are correctly identified, positioned, and recorded.

Quality assurance is essential to confident decision-making. Through a suite of solutions, the City of Garland Water and Wastewater Divisions continuously monitor our data so that mistakes can be easily caught and corrected. By implementing a variety of solutions, each tailored to the workflows and data collected by different field teams, we increase the likelihood that the unique data quality issues possible within each team are accounted for in one or more solutions. 

Additionally, by building an environment that mitigates the fear of making mistakes, we have been able to open the dialogue for process improvements and strengthened communications between office and field personnel. High-data quality is achieved when everyone has training, tools, and a strong sense of shared purpose.

Data Reviewer
Data reviewer.

City of Garland, Texas, statistics

By Douglas Denniston, Application Programmer, and Meghan Peters, Senior Business Process Analyst, City of Garland, Texas


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