Asset intensive organizations, like utilities, make key business decisions on a daily basis informed by the data managed in their enterprise systems. In order for an organization to make the best business decisions, the data used in the decision making process must have a high level of accuracy, consistency, and completeness. Using unreliable data when conducting business decisions can result in significant negative and costly impacts on operations and customer service.
Two technologies used extensively by utilities to maintain their infrastructure assets include Geographic Information Systems (GIS) and Enterprise Asset Management (EAM) systems. GIS is used to manage spatial location and associated descriptive attributes of assets, while EAM systems are used to handle asset condition and maintenance work throughout its lifecycle. It’s not unusual for both GIS and EAM systems to share common enterprise asset data, with each having a representation of the data in its local data store to support the application’s native functionality.
However essential, replicating enterprise asset data across multiple systems such as GIS and EAM increases the chance that data integrity will be compromised. Human error during manual data entry, transfer errors when data is sent from one system to another, hardware failure such as device and disk crash, and errors in data transformation algorithms are just a few scenarios where
asset data can quickly lose credibility.
A recent audit of one of our clients found that 90% of their asset data shared between EAM and GIS did not match.
A common case of this phenomenon transpires during pole replacement at electric utilities. In these situations, your GIS may indicate that a specific utility pole is made out of wood while the EAM indicates that the same asset is made of steel. Perhaps the wood pole was recently replaced by a steel pole and the EAM was updated during the work order closeout process, but the change was never sent to GIS. On the other hand, there might be a substantial backlog of GIS edits leaving the required update in a pending state. In this example, if the organization wanted to predict when one of their wooden poles would break due to age and location, their results could be skewed by data inconsistency. Utilization of proven, well-defined data maintenance practices and validation reports will ensure your enterprise does not fall victim to mismanaged data and the impending consequences of unreliable records.
“Data experts, like Thomas Redman, Jack Olson and Larry English all agree that approximately 15-45% of operating expenses of almost all organizations are wasted due to data quality issues.” (Kristine)
GIS and EAM Integration
Today there are a few ways in which most organizations integrate their GIS and EAM systems. One option that organizations tend to try first is a solution offered by their EAM vendor. IBM® offers an add-on to their Maximo® EAM called Maximo Spatial, providing an integration with Esri® ArcGIS®. Maximo Spatial creates new assets in Maximo from GIS records using a cron task that runs an automation script. This script queries GIS for a flag that gets set after a new GIS feature is created. Updates to records in Maximo Spatial are also based on a flag that gets set when a GIS record is updated. This approach is not fool proof; the flag must be manually set in GIS or the GIS system must be configured with an automation that triggers the flag event, leaving room for error.
A second predominant option is to build custom interfaces using in-house IT staff or outside consultants. When it comes to this option, the developer often uses a geodatabase version compare method in ArcGIS, where an edit version is compared to its parent version deriving the changes to send to the EAM system. This method, however powerful, is also not fool proof. If the edit version is reconciled and posted to its parent version prior to executing the change detection process, the changes are lost and the updates will never flow to the EAM.
The third leading option is implementing an enterprise service bus (ESB). An ESB is an architecture based on a set of rules and principals for communication between multiple enterprise applications using middleware. The benefit is that the application sends an event (create, update, delete) from the local system to the bus without the need to know anything about the receiving application. The rules for data message transfer are configured into the bus. The downside is that each system must have an event layer or mechanism to put data onto the bus. Not all systems, including GIS, have such an event layer making it difficult to send messages. Another pitfall is that messages can get lost during system crashes, network outage, or power failure and are never received by the application consuming the information.
All three of these approaches require an additional safeguard to identify and report data issues. Until these issues have been found and corrected, the data is unsuitable for making informed decisions for the organization.
To solve this dilemma, GeoNexus® Technologies has developed a tool called GeoWorx® Analyze that identifies and reports data integrity issues with common asset information shared by GIS and EAM. GeoWorx Analyze provides configurable end-user tools to help monitor and maintain high-quality data while keeping the data in their native GIS and EAM databases. The tool uses a full compare method to analyze 100% of the common asset data, reporting all data integrity issues. After just a few minutes of configuration and processing, GeoWorx Analyze provides a detailed report showing issues such as duplicates, orphans, and discrepancies that were discovered and need attention. The results are delivered in PDF and XML format. The XML file can be parsed for further analysis and imported to ArcGIS to view the exact spatial location of the data issues, giving a full picture of data integrity.
GeoWorx Analyze offers utilities the following benefits:
- Establish a baseline understanding of the mismatch between GIS and EAM
- Establish data clean up strategies and data governance from the results
- A better understanding of data health in order to implement strategies to increase accuracy
Using GeoWorx Analyze to regularly monitor data will provide confidence that your GIS and EAM databases are being synchronized properly. With the infallible safeguard of GeoWorx Analyze, you can rest assured that critical information shared by GIS and EAM are sound and suitable for making key business decisions.
GeoWorx Analyze currently supports data sources from Esri ArcGIS, IBM Maximo, ABB® Ellipse, and Oracle® WAM. Additional data sources, including SAP®, are planned.