CRICOS 00111D

TOID 3069

Deriving value from data and analyticsPerspectives from Swinburne's journey

6th August 2019

• Centrally located team supporting

the whole university

• Deep technical expertise

• AWS & Tableau certified

Swinburne

Information Hub

(SIH)

Student One

Government

submissions

Executive reporting

Management reporting

Exploratory reporting

Dynamic ad-hoc

Student One - Operationalreporting

Governmentreporting

Ag

gre

gate

dTra

nsa

cti

on

al

An

aly

tical

Analytics University Wideanalytical models

Business Analytics

Executive and management reporting, student operational reporting, data-related submissions to government and

maturing analytics to support decisions.

Harnessing the capabilities of a unique team

• No data repository, leveraging replicated data files

• Requests unmanageable, unable to keep up

• Between a dumping ground and a graveyard

• Tech-specific skill set

Issues across the organisation

Issues within the business analytics team

• Multiple data views creating confusion and conflict

• No single point of reference for University

• Lack of trust in reporting produced

• Increasing need to be faster

In 2016 we commenced a data and analytics project to address concerns around the accessibility and reliability of

reporting from siloed data sources, and which sought to lay the foundations for analytics to provide decision support.

What was our starting point?

• Driven by speed and cost we ran a

PoC on AWS, demonstrating the

value of security, encryption and

performance

• AWS was chosen after taking into

account aspects such as cost,

reliability, security and data

sovereignty considerations

• AWS also provides the ability to

readily expand in terms of capacity

and services as business demand

requires, i.e. data lake, NLP, voice

navigation, AI, machine learning

• Business Analytics moves the data

from S3 to Redshift, modelling within

Redshift, building data sets and

dashboard development in Tableau

and managing/training usersAWS - ap-southeast-2 (Asia Pacific (Sydney))

VPC contents

VPC subnet

Swinburne data center

Internet gateway

virtual private gateway

routerVPN

connectioncustomer gateway

Amazon S3

client

Internal Sources

Internet gateway ESB

firewall

encrypted data

encrypted data

SSL Connection

SSL Connection

Amazon Redshift – Dev/

UAT

Amazon Redshift - Prod

Amazon EMR

To provision insightful information, we needed to develop a data repository and introduce tools to effectively manage the

data; current AWS architecture started as a POC and has grown into the enterprise management reporting platform.

Proof of concept to enterprise repository

• Picked the pieces of Data Governance that

we needed to drive trust and consistency in

the data

• Took on Data Standards and Data Quality

first. Simple form and publish approach –

wiki for access

• Great tool to drive business involvement

and for them to understand their role and

how that drives success

• Utilised a working group for business

representation, got volunteers to own data

sets and ran forums for updates and as

motivation discussions

• Simple and doable for a small team - now

embedding in whatever we build and

deliver to the business

A lack of data governance was at the core of the distrust of data and reporting; we chose to work within what we could

control by focusing exclusively on the data for the platform.

Data standards & data quality

• Re-launched and put

ourselves out there

• Created a brand –

DAPPER

• Deployed a host of

techniques to

engage including

journey wall, tours,

LAB, lunch & learn

and video

• Engaged Executives

first – 1:1 training,

buy in and quotes

For change to be effective the biggest hurdle is getting people to believe in and trust the data; so we focused on building

a brand that became synonymous with the single source of truth rather than invest in tools or focus on the technology.

Change the what, how and why?

Lead

Certification

Dapster

Dynamic

Designer

Enterprise

Dashboards

DownloadData sets

BYO data to SIH

Views FiltersSimple reports

Use Intro

Save as

Publishto folder

Web edit

Desktop

Contribute

Responsibility

Publish onDAPPER

Building

New

cap

abili

ty

New

leve

ls o

f ac

cess

& c

apab

ility

Building our data maturity and capability across the

University:

• Continue developing end user capabilities

• Adding new levels of accessibility

• Deal with potential risks associated with increased

data availability

• Understanding Swinburne’s Executive and Management

reporting needs

• Make more information accessible to users

174 reports, 800+ trained users with >15,000 report views annually with a continued focus on extending the accessibility

and functionality of the services and training provided.

DAPPER is utilised across the University…

Navigation –

Landing Page

Scorecards - Swinburne

on a Page

Analysis Dashboards –

Management Reporting

Filter Based Reports –

Exploratory Reports

Build Templates –

Web Edit Functionality

As data maturity has increased, we are extending the utility of what has been built by allowing selected users to download

data, to augment with their own data not available yet hosted and/or to publish their own content.

…with information consumed in many ways

Information that previously took enormous effort to manually update and email to participants is now discussed in

interactive online meetings by sharing filtered views that are refreshed daily, in a restricted and secure fashion.

Value of sharing insights interactively

• Committee meets regularly prior to intake periods to

evaluate marketing and recruitment activities

• Restricted access controls who can see commercially

sensitive data

• Online meetings, with users interacting with and

sharing filtered views refreshed daily in production

• Prior to DAPPER, spreadsheets created manually

across various teams each week and sent via email

This rich information is now available to all parts of the business for activities such as strategic planning and

benchmarking, marketing, product development and rationalisation decisions and forecasting future enrolments.

Value of data democratisation

• HEIMS reports

identify market

opportunities,

bringing together

comparative

enrolments

benchmarked

against Australian

universities

• No prior reporting

available - the

business sourced

information

manually c/o

publications and

websites with no

enrolment counts

Building out an enterprise reporting capability with robust data management and governance has enabled significant

improvements in both the efficiency to create reports and their timely delivery to those that benefit from the insights.

Value of delivery efficiency

• Previously took >1wk from raw QILT

data to Tableau reports; now takes

~30min to upload data that’s

available immediately

• Austrade data previously

significantly underutilised, was

delivered in Excel format making it

hard to work with

Foundational work and further modelling permits us to identify at-risk students based on >400 attributes, working with

academic and engagement teams to provide targeted intervention and support to improve student retention.

Attrition propensity modelling

StudentLife

Video &Echo 360

CRM(B2C)

Canvas LMS& Blackboard

Transition?

SurveysGOS, SES,

SFS, Checkin

Commute

Interventioncohort 1

Interventioncohort 2

Interventioncohort 3

Contact and record outcomes

Statistical appraisal of efficacy

Report outcomes & pass embed learnings into future interventions

Reinforcementlearning

Existing attrition propensity model Student-on-a-page (incl. attrition risk) in CRM Leverage Audience/Campaign in DXP

AND/OR campaigns direct from

CRM

Campaigns routed via

DXP

New data will greatly enrich modelling accuracy

• Increased accuracy through machine learning

• Shortened timelines; 2019 & 2020 views 15th April

• Greatly reduced staff input initially

• Provide 3 NSE scenarios; weighted YoY growth, YTD

run home extrapolation and ML models

• NSE & RSE linked, generating 5yr total EFTSL views

99.58%

99.41%

99.21%

99.58%

99.49%

DecisionTreeRegressor

RandomForestRegressor

GradientBoostingRegressor

ExtraTreeRegressor

Ensemble

• Initial view of EFTSL/SCH and gross load revenue

forecasts for all HE and VE units for both NSE and RSE

students for refinement by marketing, faculty & finance

• Mechanism to capture adjustments & visualise

commensurate impact, passing final EFTSL/SCH data

back for ingestion into GL and to facilities & timetabling

• Method and logic fully documented on Wiki, budget

versions stored for roll-back and comparison

The use of machine learning and systemised capture and visualisation of adjustments allows us to develop initial views of

student load forecasts earlier with greater accuracy and less manual intervention.

Forecasting and load planning

𝑚𝑖𝑛

𝑖

𝑛

𝑥𝑖ℎ𝑖𝑐𝑖𝑤𝑖

Constraint optimisation (cost function)

𝑥𝑖 is number of unallocated class instances

ℎ𝑖 is duration of unallocated class

𝑐𝑖 is hourly cost of sessional staff for the class activity

𝑤𝑖 is the number of weeks the class runs

ABC10001

Tutorial 1/01

Monday

10:30 AM

AKD60001

Tutorial 1/01

Wednesday

9:30 AM

XBC30020

Lab 1/03

Friday

9:30 AM

ABC10001

Lecture 1/01

Tuesday

2:30 PM

XBC30020

Lab 1/02

Thursday

12:30 PM

AKD10001

Lecture 1/01

Friday

2:30 PM

XBC30020

Lab 1/01

Monday

5:30 PM

ABC10001

Tutorial 1/02

Tuesday

4:30 PM

ABC10001

Tutorial 1/03

Wednesday

3:30 PM

ABC10001

Tutorial 1/04

Thursday

4:30 PM

XBC30020

Lecture /01

Monday

2:30 PM

AKD60001

Tutorial 1/02

Wednesday

4:30 PM

1

2

3

4

5

6

7

8

9

10

11

12

Timetable

10 45 61 23 7 8 911 12

XXX60009 – Tutorial1…

AHC20001 – Lab1AHC10004 – Tutorial1

ABC10001 – Tutorial1

222222 20 18

223024 28

Enrolment headcount : 104

Max class size : 32

Existing 5 instances per week

Proposed 4 instances per week

Duration : 2 hours

Teaching period :12 weeks

Potentially free up 24 delivery hours

45 61 2 37 89 1011 12

Using unit EFTSL forecasts to identify potentially over/under capacity unit instances and provide an initial Academic

Workload Model view maximising utilisation of allocable academic teaching hours.

Higher Education resource modelling

Bringing together forecast student load and initial views of resourcing to develop a view of margin as part of a broader

suite of metrics to appraise product performance.

Forecast labour delivery margin

• Unit enrolment

projections from

the forecast

(EFTSL and $)

• Optimised

allocation of

academic and

sessional

resources using

prior years actual

unit instance

profile

• Equals unit

delivery margin

(gross load

revenue less

directly attributable

labour)

• User-driven, with

adjustments to EFTSL

triggering recalculation

• Fast turn-around, with

E2E refresh ~7 minutes

• Removes bottlenecks,

with users able to load

adjustments and

access outputs directly

• Detailed visualisations

for each step

(fcst/budget, resourcing

and delivery margin)

• Ability to flex aspects

makes more dynamic

• Versions archived,

allowing roll-back

• Plan to link tightly to

facilities & timetabling

• EBA

• Research allocation

• Capability matrix

• Salaries & sessional rates

• Room/capacity

• Instances (planned)

• FOE rates & pricing

• NSE EFTSL @ nFOE

Linking load forecasting, resourcing & delivery margin for end-to-end scenario modelling capability to support faculty

planning, improving alignment between anticipated enrolments and scheduling, with refreshes processed in ~7 minutes.

HE resourcing budget initiative (HERBI)

1,450

925

2,375h

month

99,130h

Team

51,840h

year

Survey of 230 users before

we developed DAPPER

Extrapolating effort per annum

across 800+ Dapsters less the

total resources in our team

Data prep

Reporting

~6% month

Through the provision of a centralised, governed reporting and analytics service, even saving 50% of the time people

traditionally spent sourcing, preparing and reporting on disparate data provides significant value.

Realising the value; estimated to exceed $3m

Example only

Developed to uniformly support new product development, accreditation, KRA reporting, faculty planning & strategic

quarterly review requirements, bringing together >40 metrics from 12 internal and external data sources spanning ~8m rows.

Consolidated governed product datasets

• Consolidating data

provides a means to

appraise and

benchmark course

performance and

identify new product

opportunities

• Improves consistency,

with single view of

metrics across internal

& external sources

• Allow users to access

and utilise this

information self-

sufficiently to reduce

bottlenecks and

facilitate ad-hoc

exploration and

analytics

• Using data from our learning

management system (LMS)

we provide insights into

assignments due dates

across multiple units and the

weighting applied to each

assignment

• This provides an overview

schedule of student workload

& contribution to final grades

across the semester

• This view can be used to;

• Optimise academic

resourcing by identifying

peak demand

• Provide a better student

experience by

distributing assignments

more evenly

Assignment schedule planning and weighting analytics provides opportunities to optimise resourcing and balance

academic demands on students, improving the experience for both.

Canvas LMS reporting and learning analytics

FUTURE READY

LEARNERS

Founded at Swinburne in Apr’18 focused on FRLs, our new data consultancy works with talented placement

students to develop skills in demand…data management, visualisation & analytics.

The Data Experience · Giving students the EDGE

Learning by Doing Our Partners

ENGAGEMENT

o Business Consulting

o Change Management

o Training

PERSONAL

o Personal Development

o Personal Branding

o Communication

BUSINESS

o Direction and Ask

o Business Operations

o Data Governance

DESIGN

o Facilitation

o Visualisations

o Data Management

TECHNOLOGY

o AWS

o Tableau

o ThoughtSpot

Our 1st cohort exceeded expectations – mastering bootcamp, delivering Swinburne projects, achieving

industry-recognised certifications, undertaking paid industry engagements and receiving employment offers.

The Data Experience · Program overview

Bootcamp Internal Projects Internal ProjectsPaid Industry

Experience

Part-time

employment

Tableau

Certification

AWS

CertificationAttend Tech

Conference

Client

Presentations

“Working externally has enabled me to

develop and enhance the skills I have learnt

during my time in higher education“

“After a year of practical

work experience, I better

understand the direction I want my career to take.”

“Fast, fun learning.”

“A great way to

recognise the

importance of soft

skills and areas for

development.”

AWS CIC

or

Public

Sector

Challenges

ENGAGEMENT

PERSONAL

BUSINESS

DESIGN

TECHNOLOGY

AWS

Medical Insurance

CompanyEnergy

Company

CALIFORNIA

ARIZONA

BUSAN

MELBOURNE

MUNICHBERLIN

FRANCE

GLOBALLY CONNECTED

The Swinburne Data for Social Good Cloud Innovation Centre powered by Amazon Web Services is the 1st in the Southern

Hemisphere and 8th globally, using applied research to provide a digital transformation capability for Government and NFPs.

Swinburne Data for Social Good

• Matt Rudd

• Email mrudd@swin.edu.au

• +61 409 346 675

https://www.thedataexperience.com.au/

http://swinburne.edu.au/dataforsocialgoodcic/

Questions or more information