All ContentMaking Data Models Actionable with Data Integration
Making Data Models Actionable with Data Integration
Table of contents
Introduction
2
Case Study
4
Data Models: A Visual Way to Describe the Business
6
Data Integration and Data Models
9
The Data Modeling Bridge
11
Summary
12
About Donna Burbank
12
3
Data Integration
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Case Study
Financial Services Organization
Shortens Time-to-Production
from 9 to 3 months
The benefits of an integrated approach
with data models and data integration
techniques were recently demonstrated in
a large UK financial institution. Regulatory
and audit requirements were driving the
need for increased data governance and
audit trails. While the need for governance,
lineage, and accountability was clear, as
is the case with most organizations, there
was little appetite for increased spending
and overhead to meet these requirements.
Real Collaboration Between
Business & IT Teams
While various teams across the
organization were contributing pieces
of the audit requirements through
models, code, and documentation, there
was no single, consistent view of the
organization’s data assets and associated
data lineage. To address this challenge,
the organization decided to implement
a combined data modeling and data
integration solution. With this solution
in place, business users can contribute
key business rules and definitions via
data models, and developers are able
to consume these definitions directly in
the data integration solution to generate
runnable code. This provides the dual
benefit of not only assisting with audit
requirements, but with new feature
development as well.
Data Integration
Team and operational effectiveness has
been enhanced in the following ways:
• Business users not struggling to learn
technical code;
• Technical teams not wasting time
researching business requirements;
• Technical resources can be used for
the ‘heavy lifting’ tasks that require
a deep technical expertise, while
business users can create initial
designs via intuitive tools.
To support regulatory requirements,
rather than manually documenting data
lineage for regulatory reporting that
could take months of effort, this direct
integration provided more automated
data lineage and audit, improving accuracy
and reducing overall effort. Rather than
each change needing to be re-evaluated
by separate teams, changes were vetted
once, and automatically integrated with
the development environment.
Reducing Development Time
Development efforts for new features were
also made more efficient and accurate.
Since business users had a direct hand in
defining core business rules, requirements
were more closely aligned with business
operations, requiring fewer revisions at
implementation time. Since requirements
and development were directly integrated,
development time was significantly
improved. Notably, one development
cycle was reduced from nine months of
effort to just three months using this new,
integrated approach. The integrated data
modeling and data integration solution not
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4
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