Macquarie Analytics
Using Palo to Tame the Excel Chaos: A Collaborative Approach to Student
Load Planning
Juan Laverde – Senior Analyst (Palo Admin)
Gary Lau – Senior Business Analyst (Lead Tech)
12 November 2012 – AAIR Conference, Terrigal
Macquarie Analytics
Overview of the Presentation
• Analytics and Memory
• History of Load Planning
• Case Study – Load Planning
• Datamodel by Palo
• Benefits, Challenges and Lessons
• What is Palo
• Live demonstration!
• Questions
Macquarie University at a glance
• Established in 1964
• 4 Faculty Structure
• 40,000 students ¼ International
• New Vice-Chancellor – Professor Bruce Dowton
• Train Station on Campus
• Located in a high technology industry corridor
• Macquarie University Hospital
• What do we do? • Storage and Retrieval of Un-Structured Records
• Management Information and Analysis
• Survey Management
• Statutory Reporting
• Connecting Education
and Industry
Analytics and Memory at MQ in 2012
Macquarie Analytics
Macquarie Analytics
Analytics and Memory at MQ in 2012
Datamart ( )
Datamodel ( )
Truth ( )
Macquarie Analytics
Case Study
• Student Load Planning – UAC Offer Rounds for Domestic CGS
– Rounds go from October to February
– 7 rounds in Q1 – 119 simulations in total
– 3 rounds in Q2 – 18 simulations in total
Load Planning Process in the Past
Faculty Executive Coursework Manager
HoD, Course Convenor
ROUND SIMULATIONS ATTACHMENTS CUMMULATIVE
Q1 Round 1 9 405 405
Q1 Round 3 9 405 810
Q1 Round 5 15 675 1,485
Q1 Round 7 28 1,260 2,745
Q1 Round 9 24 1,080 3,825
Q1 Round 11 23 1,035 4,860
Q1 Round 13 11 495 5,355
Q2 Round 1 2 90 5,445
Q2 Round 3 2 90 5,535
Q2 Round 5 14 630 6,165
A worst case scenario...
31GB in attachments
by the end!!!
• 45 emails/attachments per
simulation
Macquarie Analytics
Issues with the Load Planning Process
• Information Overload
• No Multi-User Access
• Many Stakeholders
• No Version Control
• Limited Reporting
• Planning or Reacting?
• Archiving / Historical Reporting
Macquarie Analytics
New Load Planning Process
Datamodel
Coursework Manager
Faculty
Executive
HoD, Course
Convenor
Consumers
IT Staff
Business
Analysis
Macquarie Analytics
Macquarie Analytics
Datamodel Features
• Access Historical Data
• Conduct What-if and Sensitivity Analysis
• Measure the Impact of Decisions
• Trend Analysis
• Revenue Estimates based on Load
• Predictive Model
• Web based platform
• Collaborative, Role Based Access
• Access via URL link
Datamodel Project
• Phase 1 – Project Specifications and tool shortlisting
• Phase 2 – Build
• Build Data Source
• Build Datamodel
• Phase 3 – Training and Handover
• Project duration: 9 months
• Project was live for 2012 Load Planning
Macquarie Analytics
Benefits, Lessons and Challenges
• Auditability
• Better Data Quality
• Secure, Multi-User Access
• Collaborative Environment
• Holistic view of the Process
• Progress and Historical Reports
• Time and Resource Efficiencies
Macquarie Analytics
Lessons Learnt
• Start early with the Server Build to avoid unexpected
delays.
• Build internal competencies for maintenance and
enhancements
• Ensure consensus on the specifications is reached
by all parties
• Have design skills In-House
Macquarie Analytics
Macquarie Analytics
What is Palo
• Multidimensional Online Analytical Processing (Cubes)
• Open Source Business Intelligence
• Web-based Front End
• Security – LDAPs
• Excel Interface
• In Memory
Dimensions
• Dimensions contain: elements / members /
coordinates (synonyms)
– Slicing and dicing
– Aggregation (roll-up)
– Attributes
Macquarie Analytics
Cubes
• Cubes: Allow users to analyse many different
dimensions – as opposed to a table.
• For Our Example:
– EFTSL Cube
– Actual EFTSL values for 2005-2012
– Rules to Forecast 2013 and 2014
– Planned EFTSL values for 2013-2014
Macquarie Analytics
Excel Plug-In for Palo
• For Power Users
– Load and Modify Data
– Query and Report
Macquarie Analytics
• Palo Functions
– Interact with Palo
– Add Elements: PALO.EADD
– Add Data to Cube PALO.SETDATA
Palo Web Front End
• Report Manager
• File Manager
• Publish Reports
• Distribute Reports
Macquarie Analytics
Live Demo
• 2013 – 2014 EFTSL Forecast by Liability and
Level of Study
• For Our Example:
– Dimensions
• Year, Liability Category, Level of Study, Scenario,
Measure
– EFTSL Forecast Cube
Macquarie Analytics
What has MQ planned for Palo?
• ABC
• Fee Setting
• UI for Databank Structure Bank
• UG Business Unit – Using Palo for Modelling
Macquarie Analytics
Macquarie Analytics
One machine can do the work
of fifty ordinary men.
No machine can do the work
of one extraordinary man.
“
” Elbert Hubbard,
American writer
Macquarie Analytics
Questions?
?
Macquarie Analytics
Contacts
• Web page www.mq.edu.au/analytics
• Juan Laverde
email – [email protected]
phone – (02) 9850 6946
• Gary Lau
email – [email protected]
phone – (02) 9850 7213
