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DW/BI: Driving Institutional Adoption through Academic Processes

Togamau Te’o & Robert Wood

Introduction

About us

Why we’re here today?

Q&A end of session

Further discussions

Brief History (USQ DWBI)

Started in 2010

Built infrastructure early 2011

Student went live in late 2011

Complete HR in 2012

Facility, Research Publications 2013.

Finance currently underway.

Rest of Research in Q2 2015.

Back-end (DW)

Front-end (BI)

Self-Service BI

Continuous process improvement

Business platform optimisation

Greater return on investment

Deeper, more meaningful analytics

Data mining

Increase use beyond self-service

What’s next?

Focus on

Pervasive BI

Real-time analytics

Academic process – Grade Review

Real live application - GDA

Driving DW/BI adoption using academic process

What is Pervasive BI?

Definition

Goal

Costs & Benefits

Strategy

Definition

“Pervasive business intelligence results when organizational culture, business processes, and technologies are designed and implemented with

the goal of improving the strategic and operational decision-making capabilities…”

IDC (2008)

Definition: User Pyramid

(2%)

(4%)

(~90%)

Goal

To get the information to the front line

Costs & Benefits

Benefits

Data-driven culture

Single source of truth

Tactical decision support

Just-in-time insights

Empowering staff

Costs

Infrastructure capacity

Maintenance & support

Strategy

Increase use of DW/BI platform by using it as a source for certain transactional (near transactional) systems.

Identify the right processes

Get executive support

Get users involved (co-create)

Education/Training

DW and Academic Processes

Academic Process - Grade Distribution Analysis

Requirements

Understanding Audience Capability

Process Flow

Real (Near Real) time Analytics

How are we implementing this?

Advantages & Disadvantages

Latency – issues and complexities

Reliability, scalability and performance

Challenges

Technical

Interoperability

Useability

Education and Training

Solution

Approach

Model

ETL Flow

ETL Flow

Demo

Outcome

PIR

Issues

Improvement

Learnings

Q&A