Life Cycle Approach to Managing an AM/FM/GIS Project
Jay Stinson, PMP
Intergraph Corporation
Huntsville, Alabama 35894-0001 (LR24B1)
Introduction
It can be argued that the most important AM/FM investment is in securing cogent
AM/FM data. Hence, it is vital that the AM/FM/GIS client be aware of powerful
techniques for assuring that they are receiving the highest quality data that their
project budget can secure.
Herein, the reader will find a brief discussion of several issues and concepts that
could be applied toward measuring the cogency of AM/FM/GIS data. Armed with
these concepts and techniques, the data provider and client can both derive
objective statistics about whether or not the data meets predefine criteria. These
techniques are more than a means for policing the work of the data provider, for the
data provider has an equal opportunity to apply these concepts in-house, before the
product is examined by an agent of the client.
This paper does not include a systematic work flow procedure than can be extracted
and subsequently adopted for use in a project. Aside from the fact that the size of
this work cannot accommodate such an exhaustive presentation, each AM/FM/GIS
endeavor has its own unique characteristics that together allude to advantages and
disadvantages of adopting the solutions of other endeavors. Instead, the concepts
360?presented herein are available for strong consideration while preparing a QA/QC
solution for evaluating the cogency of AM/FM/GIS data product.
Generally, the task is securing the means for the AM/FM/GIS client to objectively
determine whether or not data product is of high enough quality to be acceptable for
use in their AM/FM/GIS solution. The overall strategy is to find the least expensive
means for rejecting a deliverable unit (DU) within the context of project policies and
agreements between the conversion vendor and the Client.
In the interest of maintaining a cost-effective QA/QC effort, the suggested strategy
is to reserve the more expensive, labor-intensive inspection procedures to the very
end, whereby a DU would have already been vigorously tested using automated
tools or by exposing the DU’S to inspection types whose rejection vmuld render the
DU ineligible for further inspection.
It can be said that there are two levels of QA/QC: (1) Digital Integrity, and (2)
Information Accuracy. Digital Integrity refers to whether or not the data has been
placed and presented according to established rules for providing digital recognition
and digital relationships. Information Accuracy refers to whether or not that data
represents true information as found in the source records. The scope of this paper
is limited to techniques associated with Information Accuracy QA/QC procedures
that are to be applied against data product that has already cleared through all
Digital Integrity inspection procedures.
Information Accuracy QA/QC procedures could be established according to the
principles described in American Nationa/ Standard Sampling Procedures and
Tab/es for hspection by Attributes, Zf.4-f993, as prepared and maintained by the
American Society for Quality Control Standards Committee. This document “implies
a consensus of those substantially concerned with its scope and provisions, ...
intended as a guide to aid the manufacturer, the consumer, and the general public”
(ANS1/ASQC ZI .4-1993, title page) and is intended for use by any organization that
is able to apply its concepts.
The ANSt/ASQC standards provide a solid foundation for building a QA/QC
operation that grants the client the opportunity to actively monitor and control the
quality of their incoming conversion data, and lends objective authority to
procedures derived therefrom, affording the client even greater power in policing the
quality of their AM/FM/GIS solution’s data product.
Overview of ansuasqc-based random sampling concepts
Discussing AM/FM/GIS data in the context of ANS1/ASQC standards requires that
we define units of inspection.
For the purposes of this paper, we shall say that the data provider ships a set of
deliverable units (DU’S) to the AM/FM/GIS client (the Client). Generally a DU,
usefully defined, would be a collection of map areas that would contain an average
of 50 to 3000 “features”. Each feature could be a single object such as a segment
of uniquely identifiable gas pipe, a secondary power line, or a specific valve, etc.
361?Often, these features will have text attributes associated with them - it is acceptable
to consider these text attributes as part of their respective feature.
Hence, using these definitions, the data provider ships a set of DU’S to the
AM/FM/GIS client, with each of these DU’S containing several hundred features.
These features are subsequently examined and evaluated in determining whether
their respective DU should be accepted and incorporated as part of the AM/FM/GIS
data solution.
In order to be able to retain the authority of the ANS1/ASQC standards, it is
imperative that certain constraints be respected while developing a set of QA/QC
work flow procedures. There are four such constraints that are instrumental in
developing QA/QC work flow procedures such that those procedures retain the
authority of the ANS1/ASQC standards:
- the order in which the DU’S are to be inspected -it is important that the
order in which DU’S are inspected be random.
- how many features to inspect - determined according to the quantity of
features in each DU as well as the prescribed inspection level intensity,
- techniques for randomly identifying those features that are to be
inspected - a fair inspection requires randomly choosing a sample of
features that are to be examined.
- objective descriptions of the nature of each detected error - this objectivity
provides a means for reporting errors in a controlled QA/QC environment.
These four considerations are described as follows:
- ldentifying the Sequence Order for Inspecting DU’S
During Information Accuracy Inspection Procedures, it is important that the
order in which DU’S are inspected be random. This requirement stems from
the fact that it is necessary to track inspection trends such as too many failures
as well as positive trends of conversion excellence. These trends provide the
means for authorizing more relaxed or more stringent QA/QC operations.
As an example of how tracking inspection trends is important to the overall
inspection flow, the Responsible Authority (RA) can decide whether or not to
shift to a more relaxed level of inspection activity in cases where ten
consecutively inspected DU’S have passed inspection. The random ordering
of the DU’S assures that such trends would be fairly derived, with the
conversion vendor being unable to predict the order in which the DU’S would
be subject to inspection.
To prepare the Inspection Queue, prepare a list of DU’S that are to be
inspected such that the resulting list is a random ordering of the original list of
362?DU’S. This “randomized Inspection Queue shall hereafter serve as the official
Inspection Queue for this inspection cycle.
