It's All Analytics!: The Foundations of Al, Big Data and Data Science Landscape for Professionals in Healthcare, Business, and Government

308 Pages·2020·5.61 MB·english

by  Scott Burk

It’s All Analytics!
The Foundations of AI, Big Data, and
Data Science Landscape for Professionals
in Healthcare, Business, and Government

It’s All Analytics!
!
It s All Analytics
’
The Foundations of AI, Big Data, and
Data Science Landscape for Professionals
The Foundations of AI, Big Data, and
in Healthcare, Business, and Government
Data Science Landscape for Professionals
in Healthcare, Business, and Government
Scott Burk, Ph.D.
GaSryco Dt.t MBuinrekr, ,P Phh.D.D..
Gary D. Miner, Ph.D.
First edition published 2020
by CRC Press
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ISBN: 978-0-367-49379-0 (pbk)
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Contents
Foreword Number One ...........................................................................xv
Foreword Number Two .........................................................................xvii
Foreword Number Three .......................................................................xix
Preface ......................................................................................................xxi
Endorsements .......................................................................................xxvii
Authors ...................................................................................................xxxi
1 You Need This Book ....................................................................1
Preamble ......................................................................................................1
The Hip, the Hype, the Fears, the Intrigue, and the Reality: ....................2
Hype, Fear, and Intrigue No 1: ...............................................................2
Hype, Fear, and Intrigue No 2: ...............................................................2
Hype, Fear, and Intrigue No 3: ...............................................................3
Professionals Need This Book ....................................................................6
Introduction .............................................................................................6
Technology Keeps Raging, but We Need More Than Technology
to Be Successful ......................................................................................6
Data and Analytics Explosion ...............................................................10
A Bright Side of the Revolution ................................................................14
Where Is Someone to Turn for Information? .......................................17
The Problem, Too Many Self-Interests: The Need for an Objective
View .......................................................................................................25
There Are Many Other Professional Stories That Are Concerned
about Whether Analytics Is Important; Here Are a Few More
Examples ...............................................................................................29
What This Book Is Not: ............................................................................33
Why This Book? ........................................................................................33
Sure, Business, but Why Healthcare, Public Policy, and Business? .........34
v
vi ◾ C ontents
How This Book Is Organized ..................................................................39
References .................................................................................................41
Resources for the Avid Learner .................................................................44
2 Building a Successful Program .................................................45
Preamble ....................................................................................................45
The Hip, the Hype, the Fears, the Intrigue, and the Reality ...................45
The Hype ...............................................................................................45
Reality ....................................................................................................45
The Hype ...............................................................................................46
Reality ....................................................................................................46
The Hype ...............................................................................................46
Reality ....................................................................................................46
Introduction ...............................................................................................46
Culture and Organization – Gaps and Limitations ..................................47
Gaps in Analytics Programs .................................................................48
Characterizing Common Problems .......................................................51
Don’t Confuse Organizational Gaps for Project Gaps .............................55
Justifying a Data-Driven Organization .....................................................56
Motivations ................................................................................................56
Critical Business Events ........................................................................57
Analytics as a Winning Strategy ...............................................................57
Part I – New Programs and Technologies ............................................57
Part II – More Traditional Methods of Justifcation ..............................58
Positive Return of Investment ...............................................................58
Scale .......................................................................................................59
Productivity ...........................................................................................59
Reliability ...............................................................................................59
Sustainability .........................................................................................60
Designing the Organization for Program Success ...................................61
Motivation / Communication and Commitment ......................................62
Establish Clear Business Outcomes ......................................................62
Organization Structure and Design ..........................................................63
The Organization and Its Goals – Alignment ......................................63
Organizational Structure ...........................................................................64
Centralized Analytics ................................................................................64
Decentralized or Embedded Analytics .....................................................66
Multidisciplinary Roles for Analytics ........................................................67
Data Scientists .......................................................................................68
Contents ◾ vii
Data Engineers ......................................................................................68
Citizen Data Scientists ...........................................................................68
Developers .............................................................................................69
Business Experts ...................................................................................69
Business Leaders ...................................................................................69
Project Managers ...................................................................................69
Analytics Oversight Committee (AOC) and Governance Committee
(Board Report) ..........................................................................................71
Postscript ...................................................................................................71
References .................................................................................................72
Resources for the Avid Learner .................................................................72
3 Some Fundamentals – Process, Data, and Models .....................75
Preamble ....................................................................................................75
The Hip, the Hype, the Fears, the Intrigue, and the Reality ...................75
The Hype ...............................................................................................75
Reality ....................................................................................................76
Introduction ...............................................................................................76
Framework for Analytics – Some Fundamentals .....................................76
Processes Drive Data ................................................................................77
Models, Methods, and Algorithms ............................................................80
Models, Models, Models ........................................................................80
Statistical Models .......................................................................................81
Rules of Thumb, Heuristic Models ...........................................................82
A Note on Cognition .................................................................................83
Algorithms, Algorithms, Algorithms .........................................................84
Distinction between Methods That Generate Models .............................85
There Is No Free Lunch ............................................................................86
A Process Methodology for Analytics ......................................................89
CRISP-DM: The Six Phases: ..................................................................90
Last Considerations ...................................................................................92
Data Architecture ..................................................................................92
Analytics Architecture ...........................................................................92
Postscript ...................................................................................................93
References .................................................................................................93
Resources for the Avid Learner .................................................................94
4 It’s All Analytics! .......................................................................95
Preamble ....................................................................................................95
Overview of Analytics – It’s All Analytics ...............................................95
viii ◾ C ontents
Analytics of Every Form and Analytics Everywhere ...............................98
Introduction ...........................................................................................98
Analytics Mega List ...............................................................................98
Breaking it Down, Categorizing Analytics .............................................100
Introduction .........................................................................................100
Gartner’s Classifcation ........................................................................100
Descriptive Analytics ...........................................................................101
Diagnostic Analytics ............................................................................102
Predictive Analytics .............................................................................103
Prescriptive Analytics ..........................................................................104
Process Optimization ..........................................................................105
Some Additional Thoughts on Classifying Analytics .........................106
Fundamentals of Analytics – Data Basics ..............................................107
Introduction .........................................................................................107
Four Scales of Measurement ...............................................................107
Data Formats .......................................................................................108
Data Stores...........................................................................................109
Provisioning Data for Analytics ..........................................................109
Data Sourcing ......................................................................................111
Data Quality Assessment and Remediation .......................................111
Integrate and Repeat ........................................................................... 114
Exploratory Data Analysis (EDA)........................................................ 115
Data Transformations ..........................................................................116
Data Reduction ....................................................................................116
Postscript ................................................................................................. 117
References ............................................................................................... 117
Resources for the Avid Learner ...............................................................118
5 What Are Business Intelligence (BI) and Visual BI? ...............119
Preamble .................................................................................................. 119
Introduction ............................................................................................. 119
Background and Chronology .................................................................122
Basic (Digital) Reporting .....................................................................122
A View inside the Data Warehouse and Interactive BI .....................123
Beyond the Data Warehouse and Enhanced Interactive Visual BI
and More .............................................................................................125
Business Activity Monitoring an Alert-Based BI, Version 4.0 ............125
Strengths and Weaknesses of BI ............................................................126
Transparency and Single Version of the Truth ...................................126
Contents ◾ ix
Summary .................................................................................................135
Postscript .................................................................................................136
References ...............................................................................................136
Resources for the Avid Learner ...............................................................137
6 What Are Machine Learning and Data Mining? .......................139
Preamble ..................................................................................................139
Overview of Machine Learning and Data Mining .................................139
Is There a Difference? .........................................................................139
A (Brief) Historical Perspective of Data Mining and Machine
Learning ...............................................................................................140
What Types of Analytics Are Covered by Machine Learning? ..............143
An Overview of Problem Types and Common Ground ....................144
The BIG Three! ...................................................................................144
Regression ............................................................................................144
Classifcation ........................................................................................ 145
Natural Language Processing (NLP) ................................................... 145
Some (of Many) Additional Problem Classes .....................................146
Association, Rules and Recommender Systems .................................147
Clustering .............................................................................................148
Some Comments on Model Types ......................................................148
Some Popular Machine Learning Algorithm Classes .........................149
Trees 1.0: Classifcation and Regression Trees or Partition Trees .....150
Trees 2.0: Advanced Trees: Boosted Trees and Random Forests,
for Classifcation and Regression .................................................... 151
Regression Model Trees and Cubist Models ................................... 151
Logistic and Constrained/Penalized (LASSO, Ridge, Elastic Net)
Regression ........................................................................................ 152
Multivariate Adaptive Regression Splines ....................................... 153
Support Vector Machines (SVMs) .................................................... 153
Neural Networks in 1000 Flavors ................................................... 153
K-Means and Other Clustering Algorithms ....................................154
Directed Acyclic Graph Analytics (Optimization, Social
Networks) ........................................................................................154
Association Rules ............................................................................. 155
AutoML (Automated Machine Learning) ........................................ 155
Transparency and Processing Time of Algorithms ................................156
Model Use and Deployment ...................................................................156
Major Components of the Machine Learning Process ...........................156