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Proceedings of the Eighth International Conference on Information Quality (ICIQ-03)
8th International Conference on Information Quality (IQ-2002)
8th International Conference on Information Quality (IQ-2003)
Executive Summary/Abstract
Most IT systems cannot measure the accuracy of outputs
Does the system work and where is the evidence?
Are business decisions based on garbage?
Barclays Bank Case Study:
Small data quality flaws at the start of a project magnify into
inexplicable defects in end user outputs. Objectives cannot be
translated into measurable performance indicators and nobody
knows why.
Barclays1 used artificial intelligence to audit trail the history of
each data record at sub field, field and record level from the
source system to the warehouse. Audit trails made the workings
of the IT system transparent and end users were able to validate
output, identify errors and track back to fix them.
Using Artificial Intelligence to Benchmark
Organizational Data Flow Quality
Adrian McKeon
Infoshare Limited
amckeon@infoshare-is.com
1
McKeon, A. J. “Basel II Compliance: Can You Prove It?” A Barclays Bank plc Case Study, November 2002, www.infoshare-is.com
1
2
8th International Conference on Information Quality (IQ-2003)
8th International Conference on Information Quality (IQ-2003)
Which Comes First: Data or Technology?
Why Measuring Data Quality is Important
Data links policy to operations i.e. it translates
objectives into measurable performance indicators
Why measuring data quality is important
What happens when you can’t measure it
Output measurements reconcile silos of data from
disparate agencies and link it to objectives
Barclays Bank Case Study
Decisions made on fragmented or inaccurate data
are invariably bad making partnerships ineffective.
What did Barclays Achieve?
How did Barclays Achieve it?
Analysis tools are useless if they do not provide
accurate, measurable intelligence for action,
monitoring and evaluation
Conclusions
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