Deloitte Analytics Data driven approach to a better business

Contents

Introduction

What is Data Analytics?

My CITS3200

Case study – Safety Analytics using a Self Organising Map

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Physical Review and Physical Review Letters from 1898, 1908, 1918.... to 1978.

1988 could not fit in the picture – it was unsafe to stack that many books up.

The Data Explosion

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My project

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Web interface Database Email client

Analysis

engine

Carpooling system – in conjunction with Parking Services (client).

Goal was to encourage carpooling to and from the university, to help relieve parking

space pressure.

• Allowed users to log in to a central system and input details

• Matched users together based on timing and location

• Emailed results of matching to users on a weekly basis

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If we had our time again..

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• Build a top down, end to end view of the project – right at the start

• Based on the above – identify any missing skill sets that need to be

considered

• Stress test the application to see if it works as intended in a ‘live’ situation

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Key learnings

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• How to define a scope

• How to translate a technical subject to someone who isn’t ‘in the know’

• How to manage a client’s expectations

• Attention to detail

• Sanitise your database inputs!

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Different ‘levels’ of delivery

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Tools

• Development

packages

• Coding

languages

• Skill Sets

Output

• Executable

program

• User interface

• Statistical

analyses

Impact

• Business

improvement

• Efficiency

• Clarity

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Client Sophistication

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Text

Text

What they

asked for

What they

want to do

Why they

want to do it

?

What they

need

What is different?

Organisational

Strategy

Data

Resourcing

Actions

Levers

Impact

The traditional way

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What is different?

Organisational

Strategy

Data

Impact

Resourcing

Actions

Levers

Granular data to strategy

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Measuring the size of the problem, but then what

0

2

4

6

8

10

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Feb-10 Mar-10 Apr-10 May-10 Jun-10 Jul-10 Aug-10 Sep-10 Oct-10 Nov-10 Dec-10 Jan-11

TR

IFR

Total Recordable Injury Frequency Rate | Dashboard

TRIFR (C) TRIFR (B) TRIFR (A) QTR Moving AVG

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Self Organising Maps (SOMs)

- Multifunction - Visual - No assumptions - Simultaneous - Proximity = similarity

Number of events I was involved in

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Two examples

Group A – Residentials having events on arrival

Group B – Older FIFO males hurt at end of roster

What makes this experienced

group of employees lose

concentration when they get back

on the job?

Number of events

Number of events

This group of employees in group A number ~40 and are part of a wider divisional

mining team of ~250. However, their behaviour is markedly different from the rest of

their team. Note that the results shown are much higher than one would generally see

in traditional reporting; These results are not averaged by the ‘safer’ behaviour of the

other ~210 employees.

Group A are almost eight times more likely to have suffered a safety event, and when

they do have an event, the impact is 240% more severe than average. These

employees are almost exclusively male, 20% older than average, unionised and

residential to the mine site.

Surprisingly, they tend to get hurt in the beginning of their roster (1st or the 2nd day),

generally through an object causing them harm. They have not completed a required

safety training unit.

Group B number barely 30 employees and are spread across two separate (much

larger) teams (same site and senior supervisor). Again, their behaviour is markedly

different from the rest of their respective teams. Group A and Group B are on the same

mine site.

These employees are exclusively male, 25% older than average, and mostly (two

thirds) FIFO based. They appear to have lost work interest or work satisfaction as

indicated by declining performance reviews.

Group B employees are six times more likely to have suffered a safety event, and

when they do have an event, the impact is almost 300 % more severe than average

(more severe than Group A). Their accidents are the very expensive kind. Injuries tend

to be sprains or soft tissue damage. In contrast to group A, these employees generally

get hurt on the 7th day of a 7 day roster – just before they roll off.

What makes this specific group of

employees dangerous at the end of

their shift? Are they complacent?

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Barriers to insight

Insight

Data quality

Security

Data mountain

Analytic skills

Experience

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Links

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• www.dataalchemists.com.au – Deloitte Analytics Perth blog

• www.deloitte.com, search ‘Analytics’ – Deloitte Analytics information

• www.iapa.org.au – Institute of Analytics Professionals Australia

• matambrose@deloitte.com.au– drop me an email if you have other

questions.

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About Deloitte

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About Deloitte Australia

In Australia, Deloitte has 12 offices and over 4,500 people and provides audit, tax, consulting, and

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