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A matter of maturity
It's time for your data warehouse
to act as grown up as your business
by Scott Steinberg
EVERY
EXECUTIVE KNOWS a thriving commercial venture is
greater than the sum of its parts. But many don't yet comprehend
that even minor inconsistencies in interdepartmental analytical
capabilities can throw a monkey wrench into an otherwise well-oiled
machine.
Recognizing an industry trend, Teradata has
developed its patent-pending Data Warehouse Maturity Assessment
services, which maximize data warehousing value by aligning companies'
analytical capabilities with their business goals. These innovative
services are supported by the Teradata Solutions Methodology,
a proven suite of data warehousing processes, tools and best practices
developed at many of the world's most successful data warehouse
implementations.
"The maturity of a data warehouse must parallel
that of the enterprise it serves in order to effectively meet
the needs of business processes. Just as the sophistication of
a company matures, so too must the role of the data warehouse
supporting it," says Todd Higginson, manager, Teradata Professional
Services Marketing.
Teradata has developed a supporting scorecard
that defines the maturity stages of critical data warehousing
characteristics; among them are architectural governance, business
justification, data freshness and quality, security and training.
Companies must pay attention to all areas of the data warehouse,
including business processes and the technical components, to
gain the solution's full value.
Case in point: A major healthcare insurance
provider has created a Teradata Warehouse that provides integrated
information to applications such as customer analytics, billing,
pharmacy operations analysis and customer communications. The
complexity of the data warehouse, which is capable of managing
more than 600 users concurrently, has grown along with the business
and now enables better, faster decision making. "In this case,
data quality, data freshness, user concurrency and data volume
scalability were critical," explains Higginson. "If one area were
lacking, the data warehouse as a whole would not be successful."
Are your processes mature?
Within a given company, data variations between two business units
can occur simply from collecting it at different times or following
different data quality processes. From a business process perspective,
units/ departments may fund business intelligence projects in
an uncoordinated manner based on their specific agendas as opposed
to the prioritized goals of the enterprise.
While such technical and process variations
are inconspicuous on their own, attempting to use flawed data
and uncoordinated projects to carry out a corporate initiative
that crosses departmental boundaries can complicate and potentially
cripple the endeavor.
Teradata Professional Services consultants address
these issues by standardizing the maturity of all data warehousing
characteristics across a company while aligning a business's corporate
strategies with its data warehousing initiatives. Building upon
its experience in configuring business intelligence infrastructures
for clients worldwide, Teradata's approach to data warehouse maturity
is comprised of three distinct steps.
First, Teradata consultants determine the levels
of data warehousing maturity required to support business goals
and strategies. These levels must be consistent across the organization
as well as standardized for every dimension of the data-warehousing
environment, from data freshness to business justification processes.
Second, an assessment is performed to determine the existing maturity
level of critical data warehouse characteristics and establish
a prioritized development plan. Finally, data warehouse maturity
is standardized in a centralized environment to maintain consistency
of capabilities and processes.
Data Warehouse Maturity is not about spending
more money on business intelligence and data warehousing. In fact,
mature data warehouses cost millions of dollars per year less
than disparate, non-integrated analytical environments. They ultimately
achieve economies of scale that directly impact the value received
from a data warehouse investment.
"It's about gaining consistency across an organization,"
says Higginson. "We take the most desirable features of individual
data marts and consolidate them into one functioning data warehouse.
After all, it's easier to manage one 12-year-old than it is to
manage twelve one-year-olds." T
ILLUSTRATION
BY ERIC MUELLER
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