Avoiding data de-evolution
Kevin Peters
Advantica Stoner
P.O. Box 86
Carlisle, PA 17013-0086
Abstract
Acquisition of accurate reliable data is a major investment in any geospatial system. Once
acquired via an initial conversion effort, maintaining data accuracy is key for continued
success. After conversion or migration into a new geospatial system, the data accuracy has
been accepted and is typically well known due to the fact that conversion efforts are
performed to very specific accuracy criteria. It is important for utilities to recognize from that
point forward that data evolution takes many paths over time, from the initial implementation
of new GIS maintenance tools/procedures, to what hopefully becomes a reliable maintenance
process. Without a watchful eye on that evolution, the initial known quality standard can
quickly diminish. This paper will explore the evolution geospatial data undergoes within and
across the lifespan of ever evolving geospatial systems and maintenance processes. In
addition, it will examine data audit techniques that can help ensure data does not ‘regress’
over time from a known acceptable quality standard. Finally it will describe how some of
these techniques where applied at a large utility.
Introduction
When utilities decide to convert their asset data into a geospatial system, they typically go to
great lengths to ensure the data adheres to specific quality standards that meet their business
needs. This begins by setting up data acceptance criteria that require vendors to provide data
that meets quantifiable quality standards. With the criteria in place, a QA/QC process
measures and ensures the delivered data truly meets the required standards. This diligence
and care is understandable as not only does the GIS rely on good data to function, but many
other applications depend on the geospatial data as well, in fact, geospatial data is becoming
more and more critical as it begins to function as an enterprise-wide repository. A single type
of problem such as poor network connectivity will impact a multitude of other applications
from network/hydraulic modeling to outage management.
With critical applications depending on the geospatial data and since data conversion is
typically the most costly aspect of geospatial system implementations, especially where field
collection is required, utilities want to safeguard that asset as they would any other large
investment. Part of that safeguard is applied in the form of maintenance where a team of
people keep the data up to date. However in protecting this critical asset via the maintenance
process, it is important to understand that data evolves over time. If not monitored carefully,
data can regress over time and the quality standard once known and measured during the
initial conversion may evaporate. To ensure data does not regress over time, it is critical to
recognize the following:
- That data does evolve over time and without a watchful eye it may evolve in the
wrong direction.
- Several safeguards can be taken when establishing the records maintenance process
that will help ensure data quality will not diminish.
- Audits of the data and records maintenance process can help ensure quality standards
are being kept.
By recognizing that data evolves, taking safeguards to minimize future error, and measuring
results via data and process audits, utilities can be assured that the quality of their data will be
enhanced rather than diminished over time.
