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Product Abstract

Electric Utility Guidebook for GIS Data Quality: Conflation

Product ID:3002006006
Date Published:18-Sep-2015
Sector Name:Power Delivery & Utilization - Distribution & Utilization
Document Type:Technical Results

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Price:$ 15,000 (US Dollars)

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The utility industry has witnessed the rapid adoption and implementation of geographic information systems (GIS). Its importance is increasing with the widespread acceptance of smart grid technologies. This foundational technology relies on the quality of its data. As more accurate data becomes available, one of the biggest challenges is not only merging new data with old data but also improving existing data to meet the standards for today. Significant amounts of heterogeneity in the data, often due to differing data-acquisition methods, specifications, data formats, structuring, and attribution, can limit the full potential of GIS applications. Conflation is a solution to standardizing heterogeneous data to improve the quality of data. It allows the utility to improve data quality by addressing the issues of each input layer, resulting in a superior output product. Through conflation, business processes and workflows are more effective and efficient while meeting regulatory requirements. Interviews and utility surveys—along with other documented or published conflation reports—helped guide the research behind this report. A major commonality among the findings is that the benefits of conflation are endless, despite the relatively steep initial investment. Benefits have been observed in nearly every aspect of the business (both short- and long-term), including asset management, outage management, workforce management, customer services, safety, increased conformance and interoperability by breaking down silos, and a more efficient means for data management, data sustainability, and future data acquisition. Approaches and practices of conflation vary and are evolving as knowledge increases. Automated tools and processes combined with a little manual intervention are the most successful conflation approaches as well as the most feasible.

2015 Program 161   Information and Communication Technology
  • Data Integration
  • Data quality
  • Conflation
  • Spatial data fusion
  • Data accuracy
  • Heterogeneous data

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