A NALYTICS F OR E NABLING B USINESS S TRATEGY P ROFESSOR C ATHAL B RUGHA, M.B.A, P H.D., FMII F...

ANALYTICS FOR ENABLING BUSINESS STRATEGY

PROFESSOR CATHAL BRUGHA, M.B.A, PH.D., FMII FOUNDER DIRECTOR OF THE ANALYTICS INSTITUTE PRESIDENT OF THE ANALYTICS SOCIETY OF IRELAND

WHO DO ANALYTICS? TECHI-ANALYSTS

DISTINCT: PARTNERS IN THE PRACTICE OF ANALYTICS

Assessing the Viability of using Open Source Toolsfor Marketing Analytics – Niamh Carroll and Paul Jones, 2008 -2009

Decision Support System for Credit-Scoring – Michael Wilson and Simon Shortt: 2009 -2010

Application of Network Analysis in Insurance Data Sets in order to highlight potentially Fraudulent Claims - Cliona Fleming and Colman Horgan, 2010-2011

Improving the Accuracy and Efficiency of Predictive Modelling in Classification and Regression Trees – Eoin Fitzpatrick and Ciarán Tobin: 2011-2012

Types of Analytics

Quant Approach

Applications

Qual Approach

DECISION ANALYTICS : THEORY BUILT ON PRACTICE

Development of a Muti-Criteria Decision Support System for Early Diagnosis of Dementia in the Elderly

A Multi-Criteria Approach to the Construction of Team Performance Indicators in Professional Rugby Union

Cultural Comparisons Between China and Ireland

Reform of the Culture in the Public Service

DEVELOPMENT OF A MUTI-CRITERIA DECISION SUPPORT SYSTEM FOR EARLY DIAGNOSIS OF

DEMENTIA IN THE ELDERLY

Apostolos Tsakmakis and Muhammad K Hafeez Dr. Radu Marinescu and Dr. Léa A Deleris -

IBM Ireland Research Laboratory Professor Mary McCarron –

Dean of the Faculty of Health Sciences, Dr Kate Irving - Nursing and Human Sciences

Dr Allys Guerandel - Integrating E-learning with Psychiatry teaching,

Dr. Aurelia Ciblis – UCD School of Medicine and Medical Science

Dr. Abdul Rauf - GP in Kilkenny (Software Test)

WHAT IS DEMENTIA?

Definition: Dementia is the term used to describe a collection of symptoms

caused due to the loss of cognition, behavioural changes and a decline in social activities in the elderly

Dementia of Alzheimer’s type (AD) Mostly in Elderly people age > 60 More than 50% of Dementia in elderly is of AD 41,000 dementia patients and number will be more than 100,000 by 2036

DIAGNOSIS OF DEMENTIA

American Psychiatric Association Diagnostic and Statistical Manual of Mental Disorders (DSM-IV)

The World Health Organization's International Classification of Diseases (ICD)

IMPORTANCE OF EARLY DIAGNOSIS

Misdiagnosed/late diagnosis by GPs (35% responsibility) Disease can be Managed Improved Quality of Life Less stress for Family members Fewer Patients requiring specialized help Fewer referrals to Memory Clinics

PSYCHOMOTOR ACTIVITIES

Language disturbance

Sleep Disturbances

Memory Impairment

Orientation

Personal Care

Attention/Thinking/Conciousness

Social Activities/hobbies

Mood/Behaviour

Physical Exam

Patient’s Medical History

Instrumental Activities of daily living

Symptoms (Criteria) for Dementia

Depression Delirium

DEMENTIA DIAGNOSIS A MULTI-CRITERIA DECISION-MAKING (MCDM) PROBLEM

Multiple Diagnostic Criteria

Conflicting Alternatives

MOST COMMON DISEASES WITH THE SAME SYMPTOMS

STRUCTURING THE CRITERIA TREE

Self Others World

Physical Psychological Social

Self Psychomotor

activities

Physical Exam

Memory

Impairment

Personal care

IADL

Others Medical History Language

Disturbance

Mood/Behaviour

World Sleep patterns Orientation

Attention/thinking/

consciousness

Social Activities

IADL: Instrumental Activities of Daily Living

Convince me they have Dementia: how they relate to Self, Others, the World (structure)

DIAGNOSTIC GUIDELINES

Criteria Delirium (DEL)Depression

(DEP)Dementia (DEM)

Psychomotor

Activities

Mild Moderate Severe

Retardation:

Decreased

motivation

Agitation:

agitated

depression

Apraxia: Psychomotor

changes

characteristically

occurring late in the

illness

Hypoactive

(retardation)

Noticeable

disturbance

in

Movement

Large

disturbance in

movement

No

movement.

Hyperactive

(agitation)

Slightly

restless,

occasional

little risk

behaviour

Prolonged

little risk

behaviour,

considerable

intense

reactions

More

restlessness,

no control in

psychomotor

activity,

highly

intense

reactions

CRITERIA WEIGHTS AND SCORING OF ALTERNATIVES Imprecise Weights User is not able to provide a specific importance to one symptom over another.

Verbal Scale

Direct-interactive Structured-Criteria (DISC ) System

Utility Scoring (DISCUS) System

Relative Intensity Measurement (DISCRIM) System

DECISION SUPPORT SYSTEM (DSS) REQUIREMENTS

User friendly interface Efficient scoring process Accessibility to historical data Data protection Flexibility Integration with other DSS

MCDM IMPLEMENTATION SOFTWARE

Imprecise input requirements Additional software requirements (EXCEL) User friendliness Generic approach

PROTOTYPE SOFTWARE Stand-alone Java based application Software Implementation for MCDM model Handles Imprecise Inputs Data Protection Provides functionality for further analysis

CONTRIBUTION TO BUSINESS & SOCIETY

Early Diagnosis can reduce the cost of Dementia care (approx €442/person in late stages).

The quality of life of the Person with Dementia can be improved through therapies in the early stages.

Reduced number of misdiagnosed patient referrals to Memory Clinics

Improved quality of life for Dementia patients Correct early clinical diagnosis with no Dementia can

reduce extra cost (MRI, Brain scans etc.)

ACADEMIC CONTRIBUTION

Structuring DSM IV using MCDM Imprecise inputs for MCDM model Structured criteria combined with diagnostic guidelines

matrix can be used for improvement of dementia training for GPs

A MULTI-CRITERIA APPROACH TO THE CONSTRUCTION OF TEAM PERFORMANCE INDICATORS IN PROFESSIONAL

RUGBY UNION

Alan Freeman and Declan Treanor

Opta Sports

CURRENT RUGBY UNION & TEAM PERFORMANCE METRICS

• Current methods used to objectively depict team performance leave it up to the expert user to make sense of them, using technical and qualitative analysis

• High Level information flow looks like this:

Match Event Data

Performance Metrics

Expert User Input

Post Match Analysis

QUESTION: RUGBY UNION & TEAM PERFORMANCE METRICS

• Can we use Business Analytics to introduce expert user knowledge earlier in the process so as to produce a team metric that offers a more meaningful description of performance?

Match Event Data

Performance Metrics

Expert User Input

Post Match Analysis

INTRODUCTION: RUGBY UNION & TEAM PERFORMANCE METRICS

• Can we use Business Analytics to introduce expert user knowledge earlier in the process so as to produce a team metric that offers a more meaningful description of performance?

Match Event Data

Expert User Input

Performance Metrics

Post Match Analysis

RUGBY UNION & TEAM PERFORMANCE METRICS

• Hot Performance Indicators constructed using existing Multi Criteria Decision Making tools

• Expert Users brought through steps so as to be convinced of good performance.

• Actions need to be executed well technically in any given context (depending on opposition) and improve the team’s situation , e.g. gives some advantage – field position or on the score board

• Can be used to analyse comparative team performance by highlighting imbalances within underlying adjusting structure

• Also, provide a basis for match outcome prediction, using a simple Time Series Forecasting method

RESEARCH QUESTION & SUCCESS CRITERIA

• Can the factors that contribute to team performance in Rugby Union be considered to follow an underlying adjusting structure?

• Using this underlying structure, can a new team performance metric be created that will lend itself well to comparative analysis of teams and match outcome prediction?

• Success = Successful proof of concept

• Derived initial criteria relating to possession, set-pieces, distribution, general execution (Hughes and Bartlett, 2002)

• Criteria: possession, set-pieces, distribution, general execution Came from soccer – experts extended them to eight for rugby

• We consider the sub-criteria for each of the eight criteria, for example Tackles

• Sought to convince expert users by looking at different aspects.

• Ensure technique, appropriate to opposition and gives team a game advantage

• Example for Execution>>Tackles• Able to identify

relevantMatch Events forconstruction of HPI metrics

Should be convincing:Technicallyin the particular Contextin the actual Situation

• Base Scores• 83 match event

outcomes given scores, positive & negative outcomes

• Score Modifiers• Positional clusters

(James et al. (2005))

• Quality of Opposition ((Taylor et al., 2008))

• Location on Pitch• Home Advantage /

Away Disadvantage(Nevill et al., 2007))

• Base Scores• 83 match event

outcomes given scores (positive & negative outcomes

• Score Modifiers• Positional clusters

(James et al. (2005))

• Quality of Opposition ((Taylor et al., 2008))

• Location on Pitch• Home Advantage /

Away Disadvantage(Nevill et al., 2007))

METHODOLOGY: DATA & SOFTWARE USED• Data Provided by Opta

Sports

• 893 Extensible Markup Language (XML) Files (2008 – 2011 data)

• Only used data between2009 – 2011 – data quality issues

• XML included fixture data, player data and match event data

• Software used: MySQL, Java

Snippet of XML provided

ENTITY RELATIONSHIP DIAGRAM

BUSINESS CONTRIBUTION• Metric shows

difference in performance / form over time.

• Example, Celtic League 2011

• Munster, Leinster finished top, Aironi finished bottom. See HPI over season

• Can be used to highlight strengths / weaknesses viz a viz opposition teams

BUSINESS CONTRIBUTION• Well structured teams

should show balance among the factors contributing to their performance

• Methodology highlights imbalance

• Metrics form basis for prediction of match outcomes (win or loss)

• Useful for coaches, sports management, bookmakers

SUCCESS CRITERIA• Can a new team

performance metric be created that will lend itself well to comparative analysis of teams and match outcome prediction?

Competition # Fixtures # Correct % Correct

Heineken 2010 75 54 72.0%

Heineken 2011 78 60 76.9%

Heineken 2012 72 53 73.6%

Magners 2010 81 50 61.7%

Magners 2011 134 106 79.1%

Rabo 2012 112 73 65.2%

Overall 552 396 71.7%

Competition # Fixtures # Correct % Correct

Heineken 2010 63 44 69.8%

Heineken 2011 66 38 57.6%

Heineken 2012 60 38 63.3%

Magners 2010 75 41 54.7%

Magners 2011 128 80 62.5%

Rabo 2012 106 66 62.3%

Overall 498 307 61.6%

Actual HPI vs Match Outcome

Predicted HPI vs Match OutcomeView the Imbalance – Top vs Bottom of League

• Academic Contribution – Novel application of MCDM

• Business Contribution – new ways to analyse comparative performance and predict future performance

• Learning: How to deal with Professionals / Organisation outside our normal comfort zone (usually IT / Finance).

• Successful in terms of research questions. Married quantitative with qualitative approach

• Further research• Refinement of scores and modifiers• Improve scoring methodology (e.g. team rankings,

referees)• Expand scope – better forecasting method (e.g. Artificial

Intelligence

BUSINESS CONTRIBUTION

• Metric shows difference in performance / form over time.

• Example, Celtic League 2011

• Munster, Leinster finished top, Aironi finished bottom. See HPI over season

• Can be used to highlight strengths / weakness viz a viz opposition teams

ACADEMIC CONTRIBUTION• Novel application

of business analytics to a real world problem

• Viewed sport as an Adjusting Process and showed link to existing structures

• Practise based approach to evaluating team performance

• Usable in other contexts (different sports, different decision problems)

SUBJECTIVE COMPARISONS BETWEEN CHINA AND IRELAND ©CATHAL M BRUGHA

Introverted Development - Committing Phases

Extroverted Development - Convincing Stages

Tech – Self India - Ireland

Contextual – Others - China

Situational – World - U.S.

x x x xx x x

x x x

x x x xx x x

x x x

x x x x x x x

x x x

Somatic – Need – Thinking – Fear Analysis – Abduce

1. Physical / Intuiting /

Affection

2. Political / Recognizing /Comradeship

3. Economic / Believing /Partnership

Psychic – Prefer – Feeling – AnxietyDesign – Deduce

6. Emotional / Trusting /EmpathyFinding

5. Cultural / Learning /Friendship

Filling

4. Social / Sensing /SexualFitting

Pneumatic – Value – Knowing – Resent Implement – Adduce

7. Artistic / Experiencing /Collaboration

8. Religious / Understanding /

Communion

9. Mystical / Realising /

Charity

Introverted Development - Committing Phases

Extroverted Development - Convincing Stages

Individual Self - Technical

Representative Others - Contextual

Corporate – World - Situational

Reforming the Culture in the Public Service, which came from 19th Century Britain that protected corporate entities, and feared individuals and representative groups

No individuals with given roles other than ministers and some

ombudsmen, Governor of Central Bank, regulators, etc.

Some external boards: little internal lateral or vertical coordination.

Little oversight of authorities. Councils little

power over officials

c. 400 state authorities, mainly unconnected, de

facto independent.Can postpone, ignore, sideline suggestions, pass the “hot potato”

Somatic – Need – Thinking – Fear Analysis - Abduce

1. Physical / Intuiting /

Survey

2. Political / Recognizing /

Study

3. Economic / Believing /

Define

Psychic – Prefer – Feeling – AnxietyDesign – Deduce

6. Emotional / Trusting /AcquireFinding

5. Cultural / Learning /

DesignFilling

4. Social / Sensing /

SelectFitting

Pneumatic – Value – Knowing – Resent Implement – Adduce

7. Artistic / Experiencing /

Construct

8. Religious / Understanding /

Deliver

9. Mystical / Realising /Maintain

ANALYTICS FOR ENABLING BUSINESS STRATEGY

PROFESSOR CATHAL BRUGHA, M.B.A, PH.D., FMII FOUNDER DIRECTOR OF THE ANALYTICS INSTITUTE PRESIDENT OF THE ANALYTICS SOCIETY OF IRELAND

WHO DO ANALYTICS? TECHI-ANALYSTS

ANALYTICS HAS MANY APPLICATIONS

A NALYTICS F OR E NABLING B USINESS S TRATEGY P ROFESSOR C ATHAL B RUGHA, M.B.A, P H.D., FMII F...

Documents

Transcript of A NALYTICS F OR E NABLING B USINESS S TRATEGY P ROFESSOR C ATHAL B RUGHA, M.B.A, P H.D., FMII F...