Ensuring the spatial data in our databases adheres to data models and standards is a critical component of maintaining reliable and accurate information. This data is the root of many business processes, system integrations, and information products. In order to ensure that these integrations function as designed and the information products are of the highest quality the source data must be reviewed for errors or inconsistencies. The task of reviewing this data is tedious and monotonous, and dedicating staff resources to this data review can be difficult to prioritize among more immediately pressing projects.
This presentation will cover how FME was used to create a data review tool that identifies, isolates, and prioritizes data quality issues within underground infrastructure datasets. Using FME it was possible to model rules that extended beyond those dictated by the data model but also those defined by topological constraints and digitization standards. These rules can sometimes be complex topological conditions that are difficult for human reviewers to detect. We were able to use FME model these complex rules as well as those specific to our organization and data creation practices. The data review is now able to review and validate all of these conditions that would be difficult for staff to detect. As an output this tool generates spatial data pertaining to errors and summarized reports that can provide managers with an overview of the health of their data.
The process itself capitalizes on the process speed of FME, and is significantly faster and more efficient than previous methods that operated on the database; this has lightened the overall database and system load. Overall the advantages of using FME for this data review have been less staff time spent on complicated manual review, the results have been more consistent, and managers are increasingly aware of data health.
The City of Waterloo
FME World Tour 2018