Lower Costs and Higher Quality Data Through Maintenance
Attaining Zero Defect
Total Quality Management is an innovative approach to ensuring quality. It was developed
during the 1950s by W. Edwards Deming, who is widely considered to be the “father of QC.”
324?At the heart of this paradigm shift in quality control is fixing the process when an error occurs,
rather than merely correcting the error. It is, after all, cheaper to do something once than it is to
do it twice.
Processes designed so that that errors cannot occur are known as Zero Defect Processes. Their
use allows Statistical Process Control to replace the expensive and time-consuming 100 percent
QC step that has traditionally formed the last lapin data processing. This approach can ensure
that the quality and accuracy standards established for the conversion process are adhered to
during the maintenance process, as well, at a cost significantly lower than traditional methods.
Making Maintenance Wrok
There is an entire palette of ways to improve the maintenance process once the underlying
structure has been addressed by applying TQM principles and techniques. Three of the most
cost-effective of these concepts involve establishing a complete set of clearly spelled-out
specifications for all data in the system, the use of process engineering to redesign the entire data
entry process, and the use of QA Metadata files. Each of these is briefly discussed below.
- Specifications
Specifications always start with the end user, who may be external or internal to the
organization. Because quality and accuracy are expensive, it is important to establish
accuracy specifications that meet the needs of the most demanding user--not more, not less.
Conversion specifications are a good place to start when developing maintenance
specifications. TQM and Zero Defect processes will ensure that these specifications are met
in a consistent, timely and cost-effective manner. These clearly defined specifications also
allow automated checking for data integrity--an important part of statistical process control.
- The Maintenance Plan
Draw a flow chart that maps the route currently followed by each type of data, from inception
to GIS. Then think about three things:
1) At what points can data be lost or altered?
Design processes that foresee all the ways in which this can happen and then embed
preventive measures into the process itself. For instance, rather than have an engineering
technician hand a GIS technician a piece of paper with data that needs to be entered,
could the engineering tech submit the data in such a way that it does not have to be re-entered?
At this stage, data is like food; the less it is processed, the better.
2] How can new (or old) technologies streamline this process?
Consider the Internet, handheld computer units -- even Post-it Notes, if these would
improve the process and avoid opportunities for error. Also search carefully for ways in
which technology may be producing, rather than reducing, error.
3)What level ofservice is needed?
This is the basis for the hlaintenance Plan. It is a written document that addresses each
type of data in the system in terms of
- how frequently must the data be updated?
- how current must the data be?
- how easy must access be?
- how accurate must the data be?
- what risks are run if the data is inaccurate or out of date?
- how quickly must users have access to each type of data?
Once these questions have been answered for every type of user and every kind of data, a
cost analysis can be performed to establish the least expensive way to meet these
parameters. In-house, partial out-sourcing and full out-sourcing are all options.
- QA Metadata Files
QA Metadata files area method of identifying the spatial accuracy of each segment or node,
as well as its source. Having a QA Metadata file allows a GIS manager to quickly and easily
prioritize quality improvement efforts, starting with the least accurate engineering drawings,
for instance. The development of these standards and the identification of sources are jobs for
the engineering department, but think carefully about the most efficient way to attach this
information to the corresponding spatial data.
QA Metadata files can be constructed wholesale during the conversion process; they can then
serve as a guide for integrating data accuracy improvement work into the maintenance
process. But the construction of the metadata file itself can also be part of the maintenance
process, done on a sector-by-sector, incremental basis.
Well defined and consistently defined metadata standards also permit automated verification
of data quality--feedback, if you will. If the metadata parameters are exceeded, there is a
quality problem.
QA Metadata Example
| Pipe-lD | Diam | Location | Material | Source Code | ‘%0 of stretch | Rotate |
| 1234 | .5 ft. | 27.25ft. | Pvc | 124 | -1,3% | 3.6 |
The source code identifies the type of document the feature was captured from, as well as the
conversion method used. The ‘Aofstretch represents the difference between the attribute and
the GIS measured feature, indicating how much a node or line changed in size when it was
entered into the GIS. Rotate indicates the angle of rotation that took place between the source
document, transformed to the State Plane Grid, for instance.
Geographic information systems, being infrastructure tools used to offer many different types of
services, often bridge numerous departments or sections of an organization. For instance, a water
utility may superimpose its data over the city’s planimetric base map. Of course, this means that
when the assessor’s office performs its routine maintenance of the base map, water main
information will probably need to be updated, too. This type of interdependency requires the
development of excellent data processes and well-established communication channels,
Some organizations outsource all data maintenance for several layers from all user departments
to a third party. This is being done more and more, particularly to ensure data integrity and to
guarantee that all layers are adjusted to the same standards.
Maintenance is not a chore that can be handled ad hoc by low level technicians. Over a period of
months or years, this approach to maintenance can easily pollute the data quality that was so
carefidly established during start-up. This is particularly true in organizations plagued by
understaffing, high turnover and too few training dollars. It can also come to pass simply by
virtue of the success of a GIS. Unanticipated demand by unforeseen users can hamper the ability
of a GIS staff to merely provide basic services; maintenance is put off until someone has time to
get to it.
Maintenance may seem like a crisis sometimes. Lack of it can certainly cause crises. But, in
Chinese, “crisis” also means “opportunity.” Innovative maintenance represents an opportunity to
simultaneously save money and improve GIS data quality.
