CSU’s Data Architecture and
Governance
Nina Clemson
Enterprise Architecture Symposium, 2006
Where it all began
• Information architecture issue papers, 2000
– Reliability
– Complexity
– Scalability
• External architectural review and
recommendations
– Technical only
• Information Strategy and principles
– Aimed to educate the CSU community to accept the importance
of information
Constellar
• First middleware solution
– Point to point transfers via hub
• A limited success
– Enabled decoupling and improved reliability
– Technical limitations
• Revealed other dimensions to the information
architecture problem
– “Dirty” data
– “The chicken and the egg”, who’s the real owner
– Different perspectives
Data Architecture Project
• Top down review of our architecture, including
non-IT components, recommended
• Had to take a pragmatic approach
• Standardised enterprise objects mapped to
underlying sources
• Three streams
– Technical
– Integration
– Data analysis
DAP – data analysis
• Reverse engineered from existing sources
• Review of current data flows
– What to define
• Review of existing standards
– The design
• Leveraged other project work
– Some of the sources
• Examination and comparison of content
– Leverage “common knowledge”
– Revealed issues
Characteristics of a data standard
• Definitions
– Scope
• Ontology & taxonomy
– Relationships and classifications
• Authoritative Source
– Most correct source
– To the attribute level
– May change over the lifecycle
• Unique identifiers
– Shared or mappable
– Contributors, consumers and legacy
• Stakeholders
– Creator, system owner and others with a significant interest
Data Issues
• 40+ identified
• Categorised into five types
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–
–
–
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Competing sources of data
Currency and applicability
Inconsistent formats
Structural
Multiple sources of data
• What happens if you share data and don’t fix
these problems
Too many cooks
• Two systems store subject information
• One system creates subject information, the
other uses it for administration purposes
• Both systems contain active and inactive
subjects
• When queried for the current set of active
subjects, the results are completely different
• Question – if a new system arrives tomorrow
and wants subject data, which system is the best
source?
Data Governance – towards a solution?
• Storing data for the enterprise
• Possible to change, but is it worth it?
– What is the benefit?
– Departmental vs enterprise optimisation
• The cost of inaction
– The de facto standard
• This is where we are now
CSU Data Governance Board
• Membership
– Senior divisional managers
– Executive Director and Architecture staff
• Terms of Reference include:
– “The Data Governance Board has the responsibility of ensuring
the means by which data assets are defined, controlled, used
and communicated for the benefit of CSU”
• Prioritisation
– Project versus issue matrix
– Environmental scan
Lessons learned
• Data governance is hard
– This isn’t about technology, its about organisational change
• Where there is data sharing exists, there must also be
data governance
– No standard is a de facto standard
• Technology is not a substitute for management
– Garbage in garbage out, it’s a cliché but its true
• The content of a standard is not important, the
agreement is
• Standards are not cast in stone
– Things also change.
– Understanding is a collaborative and iterative process that occurs over time.
– Data governance is the process that manages this change
• Don’t underestimate the value of education