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 ( )

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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

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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

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• 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 – juan.laverde@mq.edu.au

phone – (02) 9850 6946

• Gary Lau

email – gary.lau@mq.edu.au

phone – (02) 9850 7213