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 –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
Your consent to our cookies if you continue to use this website.