Building a strong foundation for the future

Business transformation processes
Most business transformations processes cross traditional departmental mandates. Therefore, in order to avoid “islands of automation” of the past, “process-centric” approach to automation initiatives is recommended. Furthermore, there is a logical process when developing/deploying automated solutions (i.e. “one has to crawl before one can walk, run or fly”).

Figure 2 describes “common sense” building blocks required when developing an “automated/integrated working environment”.

Figure 2: Building blocks for an “automated working environment”

The Figure 2 above shows the activities that have to be addressed/resolved (i.e. landbase, network model, data migration/conversion) in a logical order, before or in parallel with process automation phase (i.e. “core” applications), followed by the development/deployment of “analytical” applications.

Data requirements
The methodology and issues related to data architecture has been, is being and will be discussed in many other papers and articles for example:

From tiles & layers to seamless spatial databases
From spatial features-centered to object-centered
From procedural programming to object-orientation
From proprietary spatial formats to RDBMS
Etc. etc.

In this paper, the authors wish to emphasize the importance of the following:

Data integrity (i.e. obtain/capture data from a source that is mandated and competent to provide specific data)
Data timelines (i.e. ensure as close to immediate access to all data for all
Avoid duplication of effort)
Ensure only critical data is captured

Since your Geospatially Enabled Asset Registry is in fact the “home” for all your plant (and related) data, it is recommended to create, and subsequently to maintain, an “Data Inventory Matrix”. This matrix would be a “living document” showing all your data in your corporate Asset Registry, including graphic symbology and attributes, for all data classifications (i.e. from Landbase to Administrative, Structures, Electrical, Streetlighting, Fiber-optics, etc.) This document would help you when prioritizing the development of your “analytical” applications since it is obvious that the application can not run without data. Similarly, it would readily show you which data elements are available and which new ones have to be captured before any given application could be functional.

Conclusion
Just a few logical, common sense issues have been covered in this paper. They should help you in the creation of a stronger foundation for your automated working environment, using geospatial and related technology. We should all attempt to reverse some of the findings by industry observers and analysts, for example:

42% of corporate information technology projects were abandoned before completion
75% of re-engineering projects fail to deliver the results they promise.
Etc. etc.

By defining business objective(s), realistic project planning, internal project coordination, participation by the right type of people, application of some old fashioned logic and common sense, we should be able to reverse some of the above stated statistics.