Avoiding data de-evolution
Measurable Acceptance Criteria/Quality Standards
The assumption was that a set of acceptance criteria was applied during initial conversion
and that it met user expectations and needs. If those standards were never put in place, then
create a set of acceptance criteria for new and updated data that defines not only the
acceptable error percentage by category, but also defines how errors in each category will be
determined and measured. This will help record maintenance staff understand quality
expectations and provide a way to quantify improvement in the quality of data.
Close Cooperation with Field Personnel
Field collection personnel are key to the data maintenance process as they provide the source
information for the updates that are to take place in the GIS. Often field personnel and record
maintenance staffs are under separate ‘departments’ which proves to be a challenge. In
addition, the two teams have slightly different drivers. The field personnel are interested in
getting things built/fixed, then noting the results, while the records maintenance staff wants
to make sure the data in the GIS truly reflects what was built. Especially in the case of
maintenance processes involving paper (as-built sketches, etc), this mode of operation often
results in key information not being noted on field sketches consistently and therefore not
being available for input into the GIS. The record maintenance staff’s recourse is, to have
someone revisit the site in the field, which can prove to be costly, or input the data with
missing information and allow quality to be jeopardized.
There are several proactive steps that can be taken to establish good communication and
cooperation between the two groups. An initial step is to develop a standard records/field
sketch process that is based on the geospatial system’s terminology and standards, i.e.,
standard, feature and attribute names, symbology, domain pick lists, etc. It is important to
follow up that process with training. While most utilities recognize that GIS training is
important, the training for field personnel, who provide the source information, is often
overlooked. Field personnel should be trained so they have an understanding of the GIS and
know what data is required of the GIS, what business rules are applied by the rulebase when
data is posted, etc. In establishing a process, the records maintenance staff should recognize
the people in the field are both their supplier and customer. The maintenance process should
allow users to be a guide to data quality. When field personnel discover incorrect data, there
should be a process for them to generate a job to have it fixed. Finally, the establishment of a
focus group consisting of field personnel, members of the record maintenance staff, and
users, helps to facilitate communication. The focus group might be charged with directing
‘change management’ by, examining the records maintenance business process on a
continuous basis, making suggestions for improving the process, and monitoring the results,
including changes to data quality.
Documented Maintenance Specifications
Don’t accept the premise that field personnel and records maintenance staff have an
‘unwritten’ understanding because they’ve worked together for so long. All data maintenance
specifications, including how data should be collected and noted in the field, should be
documented. It provides a standard against which data can be measured and safeguards
against staff turnover where, ‘knowledge gaps’ between new and old technicians might exist
due to ‘undocumented’ specifications.
Rulebase
A tight set of business rules applied via a rulebase will help prevent errors from being
entered. This is especially useful during the early maintenance period when procedures,
specifications and operators are in their infancy. The application of business rules within the
geospatial system can take many forms including, the use of domains that force operators to
choose only those values on a pick list, connectivity rules applied when data is posted that
require assets to have valid connections, and the application of ‘mandatory’ fields that
require records maintenance staff to populate critical fields. In developing these business
rules it is important to ensure that data adheres to the needs of other applications. For
example, if elevation is needed to perform hydraulic modeling and hydraulic modeling is a
key function of the GIS, then that field should be defined as ‘mandatory’ and the GIS should
force the operator to input the data.
Note that the use of a strict rulebase can have negative consequences. For example, if there
are mandatory fields and the source data is not complete, it could result in maintenance staff
entering ‘made up’ values for the sake of being able to complete their work. In addition, the
selection of valid values should be done with caution. Values like “unknown”, “x”, and
“other” in domains typically add little value and often get used when accurate values should
be applied.
Database or Code Triggers
As an extension to the set of business rules, database or code based triggers can be applied.
These types of triggers might address validity checks such as attribute combinations and can
be established to occur automatically when an event such as a record insert, update or delete
occurs.
