How to Achieve T&D Asset Data Integrity When Information is Shared by Multiple Enterprise Systems

An Electric Utility’s Transmission and Distribution (T&D) asset data is shared across multiple utility business systems such as Geographic Information Systems (GIS), Enterprise Asset Management (EAM), Customer Information Systems (CIS), Outage Management Systems (OMS), and Distribution Management Systems (DMS). For each system, the data must reside in the application’s native database to support its functionality. Often times, organizations want these systems integrated so they can utilize data within the context of another system. For example, many EAM users would like to leverage their GIS system to see their assets in a map context. The problem is (and we see this happen all the time) when you start sharing data across multiple business systems without well-defined data governance policies and maintenance practices, discrepancies between data shared by these systems occurs. When this happens, data integrity is compromised and the organization’s ability to make informed decisions is negatively impacted.

We recently engaged with a large electric utility that was in dire need of some help with their IBM® Maximo® (Maximo) and Esri® ArcGIS® asset data integrity. Prior to our engagement, they had performed a field study that found 90% of their asset data shared between Maximo and ArcGIS did not match. This was not surprising considering the two systems were managed separately and updated through redundant data entry processes. Regardless, the extensive inconsistencies caused their T&D Operations department a great deal of stress, having lost the capacity to trust their own data. This severely limited their ability to make informed decisions with their asset data and became a critical pain point for the organization.

In order to improve their data integrity and restore faith in their ability to make informed decisions about assets, this organization embarked on a data cleanup effort. As they started their effort, they found that they had no way to quantify their issues and track data integrity health. Additionally, they required a solution to keep their systems synchronized as the issues were resolved.

Given our extensive knowledge of ArcGIS and Maximo, we offered our support in their data integrity improvement work. We leveraged our tool, GeoWorx® Analyze, to perform data integrity analytics and provide insight into three types of issues: data discrepancies, duplicates, and orphans.

During the project, we focused on the organizations Transformers, Capacitors, and Reclosers. GeoWorx Analyze was configured to extract and compare the GIS and Maximo data and identify differences among the shared asset data between the two systems. The output included a report that quantified the issues per asset class across the GIS and Maximo systems. As expected, we discovered some major cross-departmental data integrity complications that were restricting access to reliable asset data. Figure 1 shows a sample data integrity scorecard for Transformers created from data provided by GeoWorx Analyze. This project provided a baseline understanding of the magnitude of issues they were dealing with, which helped define a strategy for their data clean up.

Figure 1

After reviewing the results produced by GeoWorx Analyze, we were able to provide some insight regarding areas of concern for data integrity. The first item we discovered was that their Maximo and GIS data models did not align. This was not surprising (or uncommon) since both were designed and implemented separately. The GIS feature classes contained subtypes that did not align with the Maximo asset classification structure making it difficult to associate common data. Secondly, their primary/foreign keys were not always populated in GIS, with the equipment number null on some records. Equipment numbers in GIS are used to link to asset numbers in Maximo, therefore the GIS records with a null key were documented as un-linkable records. Figure 2 shows GIS records that are not linked to Maximo due to “null” equipment numbers.

Figure 2

We also discovered records in GIS that had the same ID linking them to Maximo. This was referenced as a duplicate and was most likely caused by a copy/paste action in GIS during the data entry process. Figure 3 shows two transformer units with the same equipment ID but different operating locations. Comparing the records to Maximo, it is clear which GIS record needs to be updated.

Figure 3

There were also discrepancies with asset specifications. For example, GIS may indicate that a transformer has a KVA rating of 25 where Maximo indicated a rating of 5 for the same asset. Discrepancies like this are extremely problematic for any organization trying to make informed decisions about their assets. Figure 4 is an example of the discrepancy report for a single transformer asset with an asset number of 4633.

Figure 4

Based off of these results, we came up with a list of recommendations to assist with data clean-up efforts. These recommendations were created for this utility, but may be applied to many organizations facing GIS and EAM system data integrity problems.

  1. Align your GIS and Maximo data models.
  2. Determine the role of “locations” in your EAM.
  3. Create data governance policies across departments understanding that system of record (SoR) may be different than system of entry (SoE).
  4. Use integration (such as GeoWorx Sync) as a way to keep your data consistent and help quickly flag any discrepancies that show up so that once your data is cleaned up, it stays that way.

If you are interested in hearing more about the tools and strategies that we offer for data integrity management, shoot us an email at

Categories: 2017

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