Information and Knowledge Management using ArcGIS ModelBuilder

Carolyn Begg and Tom Caira
The SMEs are of similar size but differ in their requirements for access to, and use of data. Company
A does not utilise e-business other than e-mail facilities and, as such, is very much internally
focussed in relation to its access to, and use of, data. Company B has made a significant investment
in e-business facilities and provides and utilises web services for both customer and supplier
interaction. As such, it recognises the need to have both an internal and an external focus in relation
to its access to, and use of, data.
Neither of these organisations overtly utilise any form of data governance. The aim of this research is
to explore the awareness of, and attitude to, data governance within each organisation and, given the
differing data focus between the organisations, to explore the potential benefits that could be provided
by the implementation of data governance and to highlight any barriers that might restrict these
organisations from capitalising on such. We use the data governance framework proposed by Khatri
& Brown due to its uncomplicated format as this is likely to engender a better level of understanding of
data governance concepts within the research organisations and hence provide for better elicitation of
valuable research data.
4.3 Research
Each organisation was asked to provide an explanation of the term ‘data governance’ and of the
decision domain terminology used in the Khatri framework as a means of establishing awareness and
understanding of the concepts. The authors then provided an explanation of the terminology and used
the questions contained in the framework to effect a discussion that revealed company attitudes to
data governance and engendered consideration of potential benefits and barriers.
When asked to provide an explanation of ‘data governance’ both companies referred to governance
by external entities such as government and regulatory bodies (e.g. Data Protection Act) and then,
almost as an afterthought, to the internal governance of company data, the management of client data
and the provision of effective data backup and security.
Both companies were then asked to provide an explanation of their understanding of the five ‘decision
domains’ that are contained in the framework. Neither company could offer any form of explanation
for the terms ‘data principles’ and ‘metadata’. Both companies identified ‘data quality’ as relating to
the accuracy and integrity of their electronic data. Company A’s understanding of the term ‘data
lifecycle’ matched the definition provided in the framework whereas company B could offer no
explanation for this terminology. The final decision domain, ‘data access’ provided differing
explanations with company A relating it to security and company B explaining it as the variety of ways
in which a dataset can be accessed.
After an explanation of the decision domain terminology was given, further comment was provided by
the companies, as follows in Table 1:
Table 1: Company attitude and perception of benefits and barriers
Decision
Company Attitude and Perception of Benefits and Barriers
Domain Company A Company B
Data
Principles
Data
Quality
Metadata
Value of data recognised in relation to
monitoring of production and repair processes
and for invoicing. Data not considered to have
value in relation to analytics. This was not
considered to be feasible in the existing
information system.
Data not 100% correct. Inconsistencies,
duplications and missing data corrected in a
reactive matter. Great deal of trust placed in
employees to maintain data accuracy. Small
size of workforce facilitates this. Company
does, on occasion, have to handle data that
does not fit within normal business processes
and management of this data can often be
problematic.
Data not perceived as an asset, because it is
not currently viewed as such by the business
world. Hence has no perceived ‘financial’
value to the enterprise, as opposed to
intangibles like goodwill. Value of data
recognised as an essential element of
operational management.
Data managed and maintained in a reasonably
accurate manner, in as much as this is
required to provide company services
effectively. Data quality perceived as a
reactive process driven by practical needs and
effected on an ‘as needs’ basis. Data quality
issues considered easier for SMEs and
particularly micro enterprises to address.
Both companies made similar comment in that company data is not documented and the
meaning of data is passed on to new employees through training and personal contact,
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