Page 43 - DCAP606_BUSINESS_INTELLIGENCE

Business Intelligence

Notes
the accuracy of the data against its original source of authority is imperative. Any such
system must also be able to: apply policy and procedure for comparing information from
multiple sources to select the most accurate source for a data element; correct data elements
as needed; and check inconsistencies amongst the data. It must accomplish this while
maintaining a complete data history of every element before and after every change with
attribution of the change to person, time and place. It must be possible to apply policy or
procedure within specific periods of time by processing date or event data to assure
comparability of data within a calendar or a processing time horizon. When data originates
from a source where different policies and procedures are applied, it must be possible to
reapply new policies and procedures. Where quality of transcription is low qualifying the
data through verification or sampling against original source documents and media is
required. Finally, it must be possible to recreate the exact state of all data at any date by
processing time horizon or by event horizon.
The analytical system applied to a data warehouse must be applicable to all data and
combinations of data. It must take into account whether sufficient data exists at the necessary
quality level to make conclusions at the desired significance level. Where possible it must
facilitate remediation of data from original primary source(s) of authority.
When new data is acquired from new sources, it must be possible to input and register the
data automatically. Processing must be flexible enough to process these new sources
according to their own unique requirements and yet consistently apply policy and
procedure so that data from new sources is comparable to existing data.

When decisions are made to change the way data is processed, edited, or how policy and
procedure is applied, it must be possible to exactly determine the point in time that this
change was made. It must be possible to apply old policies and procedures for comparison
to old analyses, and new policy and procedure for new analyses.

Defining Data Warehouse Issues
The Lolopop partners served as principals in a data warehouse effort with objectives that
are shared by most users of data warehouses. During business analysis and requirements
gathering phase, we found that high quality was cited as the number one objective. Many
other objectives were actually quality objectives, as well. Based on our experiences, Lolopop
defines the generalized objectives in order of importance as:
Quality information to Create data and/or combine with other data sources
In this case, only about one in eight events could be used for analysis across databases.
Stakeholders said that reporting of the same data from the same incoming information
varied wildly when re-reported at a later date or when it came from another organization’s
analysis of the same data. Frequently the data in computer databases was demonstrably
not contained in the original documents from which they were transcribed. Conflicting
applications of policy and procedure by departments with different objectives, prejudices
and perspectives were applied inconsistently without recording the changes or their
sources, leaving the data for any given event a slave to who last interpreted it.

Timely response to requests for data
Here, the data was processed in time period batches. In some instances, it could take up to
four years to finalize a data period. Organizations requiring data for analysis simply went
to the reporting source and got their own copies for analysis, entirely bypassing the
official data warehouse and analytical sources.

Contd....

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