News
 Turning
Data into Discovery
Many large enterprises are now embarking on data
governance programs, establishing data governance
councils, and appointing data stewards to provide a
single point of decision-making and responsibility.
Driven by compliance requirements and business
needs, understanding and mapping data has recently
come to the forefront.
Data governance is based on principles that data has
to be understood, secure, consistent, accessible,
and managed through people, processes and
procedures. Because you cannot govern what you do
not understand, understanding the data is the first,
necessary step in securing it, making it consistent
and accessible, and managing it. Unfortunately, it
is a difficult and painful process. After going
through all the pain to discover the business rules
governing a single field in a single application,
imagine trying to uncover the data rules and lineage
across hundreds, and even thousands, of
applications, and millions of fields!
Fortunately, there is hope. With all the focus on
the problems of data models, data lineage, and data
consistency, there is a new set of tools for data
relationship discovery and management available to
help fight the problem.
• New collaboration tools, like wikis, that provide
a searchable, editable forum, are ideal places to
capture the collective knowledge of the data in the
enterprise and allow analysts from different groups
to collaborate on defining business rules and
business terms.
• New discovery tools are available that focus on
the data analysis rather than metadata, help
discover the patterns and rules hidden in the data,
and help reverse-engineer the various rules and
identify exceptions. While these tools are not
"push-button," they would help discover the meaning
of the youthful driver flag in days, rather than
months.
• Finally, there are new tools available to validate
data consistency and manage remediation of data
inconsistencies that exist between distributed
systems.
While the problem of understanding and untangling
the current data mess is still formidable, armed
with these new tools, companies are finally
beginning to make progress. So, take heart! You can
have beautiful consistent data models, documented
data rules, and even clean data.
---Source: Excerpts from Alex Gorelik article “The
Analyst: Turning Data into Discovery” for DM News
October 16, 2007 issue.
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Melissa Data
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