Page 44 - DCAP606_BUSINESS_INTELLIGENCE

Unit 3: Dimensional Data Warehouse

Notes
Consistent relating of information
An issue as simple as a name — the information that could be used to connect data events
to histories for individuals or other uniting objects — had no consistent method to
standardize or simplify naming conventions. Another example, Geographical Information
System (GIS) location information had an extravagant infrastructure that was constantly
changing. This made comparisons of data from two different time periods extremely
difficult.
Easy access to information

Often data warehouse technologies assume or demand a sophisticated understanding of
relational databases and statistical analysis. This prevents ordinary stakeholders from
using data effectively and with confidence. In some instances, the personnel responsible
for analysis lack the professional and technical skills to develop effective solutions. This
issue can stultify reporting to a few kinds of reports and variants that have been
programmed over time, and reduces data selection for the analyses to kind of magic
applied by clerical personnel responsible for generating reports.
Unleash management to formulate and uniformly apply policy and procedure

We found that management decisions and mandates could be hindered by an inability to
effectively capture, store, retrieve and analyse data.
In this particular instance, no management controls existed to analyse: source of low
quality; work rates; work effort to remediate (or even a concept of remediation);
effectiveness of procedures; effectiveness of work effort; etc.
Remediation is a good case in point. Management experienced difficulty with the concept
of remedying data transcription from past paper forms — even though the forms existed
in images that could be automatically routed. The perception was that quantity of data,
not quality, was the objective and that no one would ever attempt to fix data by verifying
it or comparing it to original documents.
Manage incoming data from non-integrated sources

Data from multiple, unrelated sources requires a plan to convert electronic data, manage
imaging and documents inputs, manage workflow and manage the analysis of data. In
this case, every interface required manual intervention. Since there was no system
awareness at the beginning of the capture process as to what was needed for analysis at the
end, it was very difficult to make rapid and time effective changes to accommodate changing
stakeholder needs.

Reproducible Reporting Results
We found that reporting of data was not reproducible and the reasons for differences in
reporting were not retrievable, undermining confidence in the data, analysis and reporting.
One may essentially summarize these objectives as quality challenges that require a basic
systems engineering approach for resolution.
Questions:
1. What were the challenges of lolopop automated data warehouse?

2. What were the data warehouse issues?

Source: http://www.lolopop.net/Lolopop.DWStudy.pdf

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