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SAS invites you to join data quality guru Larry English for an informative
discussion about best practices in data quality.
Many companies overlook the importance that data quality plays in
successful business intelligence initiatives. Organizations expect
to see a rapid return on their corporate investments, including data
integration efforts, business intelligence applications and enterprise
solutions. Yet, in the end, the results produced by these expensive
projects are only as good as the data that feeds them. Many fail to
deliver expected returns because of the poor data quality maintained
by their respective organizations.
This Webcast will help you develop strategies to tap into the power
of your data by integrating data quality standards into enterprise
intelligence initiatives from the outset. Learn about a proven process
that Larry English has developed for ensuring data quality in any organization.
Afterwards, you'll be able to:
- Profile, monitor and actively manage the quality of
enterprise data.
- Integrate and standardize data across multiple systems
and business units.
- Easily define data correction rules to reflect organizational
changes and cleanse data.
- Provide decision makers with information they can
trust.
More About Larry English
Larry P. English – President and Principal, InfoImpact
International Inc.
Information Management Authority Larry English is
an internationally recognized speaker, educator, author and consultant
in knowledge management and information quality improvement. He also
provides consulting and education in information stewardship, strategic
information visioning, information technology evaluation, information
resource management and data administration, data modeling and facilitation,
and value-centric application development methods. English has developed
the Total Information Quality Management (TIQM™) methodology applying
Kaizen® quality principles to information quality management.
He chairs Information
Quality Conferences around the world and is a co-founder of the
International Association for Information and Data Quality (IAIDQ).
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