A Fortune 500 energy and utilities company updates legacy records with a data quality team.
Our client was preparing for a migration to ESRI's Utility Network, an advanced GIS network. They partnered with Apex to help reduce a backlog of 127,000 as-built/legacy source records that were insufficient to justify additions to their GIS spatial database. The work order backlog included over 100 years of work orders from various regions and types of gas utility facilities.
127,000 Work Orders Resolved
Apex deployed an initial team of 12 data and quality control analysts with GIS expertise including an overall lead, quality control lead analysts, and data quality analysts. At the onset of the engagement, Apex collaborated with key stakeholders to establish metrics, tracking tools and processes, dashboards, reporting cadences, and individual/team performance expectations. This initial team was scaled up to 75 consultants to support the volume of work as the project ramped up. Apex developed onboarding guidelines and workflow documentation to resolve source records in the backlog. Apex also created team-specific reference guides for common utility terminology and symbology and defined quality control sample size, desired quality scores, and workflow. Corrections to the records included correcting insufficient or conflicting data, incorrect addresses, and updating illustrations of property and gas lines.
The Apex team successfully resolved the client’s source record backlog of 127,000 work orders with a quality of 85% or higher, which allowed them to confidently digitize and include in their GIS spatial database. The client has since leveraged Apex for several additional data quality and GIS-related efforts due to the success of the original engagement. This effort greatly improved the data quality and reliability of the client’s GIS spatial database. In addition, our manual source record validation would have taken our client’s full-time staff at least 2-3 years to complete, and because we deployed a dedicated, specialized, and scalable workforce, we were able to resolve this in a little more than 12 months.