Corporate Systems and
Educational Purposes
JISC-CETIS 2010
#cetis10scs
Jim Everett
Reorganisation and Review
Aspirations
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Remove unnecessary steps
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Stamp out repetition, duplication and variation
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Single source data
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Simplify access to data
Squaring the Circle?
Review of Course and Class Approval
and Review
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General agreement that current arrangements
are not tenable
Process modelling
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Information flow not data structures
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Decision points not timings or criteria
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Highlight areas of ambiguity/complexity

Propose some innovations
Generalised model addressing the “common
variations”
BPMN diagram
Predicated on a data management solution
System’s architecture and technologies overview
Presentation Layer
User Interface Components, Presentation Logic
SharePoint UI, ASP.NET, jQuery, AJAX
Service Layer
Orchestration, Service Interfaces, Message Types
Windows Workflow Foundation, Web Services
Business Layer
Domain Model, Business Entities, Business Workflows
Domain-Driven-Design, Windows Workflow Foundation, .NET
Data Access Layer
O/R Mapping, Service Agents
NHibernate, SharePoint Object Model / Web Services Access Components
Data Stores
SQL Server, SharePoint Lists, Document Libraries
Interoperability Problems

The data structures are not the problem!
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There is a place to hold all the data (more or less)
The problems/bottlenecks/issues centre on
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Putting the information into the process

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On time
Complete
Compliant with requirements
Getting access to the information

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When needed
In a reusable form
Not buried in a mass of irrelevant data
Corporate v. Education?
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Academic Entrepreneurs v Academic Innovators
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Problem is too much entrepreneurialism

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Impose discipline on entrepreneurs
Problem is NOT too much innovation

Good policies, but not embraced

Training and resources, accessed by a few
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Harness corporate processes to provide
information to quality monitoring
Standards and Specifications
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Entirely driven by demands of external agencies
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HESA, UCAS, Hot Courses
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Diploma Supplements, Teachers' Profiles
Generally poor fit with internal data requirements
Often need to tailor data making whole
automated extraction problematic
No desire to pro-actively adopt external data
models, especially for internal applications
Reflections
Jim Everett
Development and Innovation Unit
Information Services Directorate
jim.everett@strath.ac.uk
@jeestirling
PiP: Principles in Patterns Project
http://www.principlesinpatterns.ac.uk