CSU’s Data Architecture and Governance

2. CSU’s Data Architecture and Governance Nina Clemson Enterprise Architecture Symposium, 2006

3. 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

4. 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

5. 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

6. 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

7. 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

8. Data Issues • 40+ identified • Categorised into five types • 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

9. 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?

10. 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

11. 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

12. 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