The document discusses the relationship between analytics, big data, and system dynamics. It begins by providing background on the growth of analytics and big data. It then discusses relationship problems between analytics and operations research. The main part of the document introduces the Dianoetic Management Paradigm to describe the evolution of management thinking and related technologies over time. It also describes categories of analytics from descriptive to predictive to prescriptive. Finally, it discusses implications of big data for system dynamics, including opportunities around high-volume, unstructured, and streaming data as well as related technologies.

2. Structure
1. Background
2. Relationship Problems
3. The Dianoetic
Management Paradigm
4. Categories of Analytics
5. Implications for System
Dynamics
2 Big Data, Analytics & System Dynamics – April 2014

3. Competing on Analytics
3 Big Data, Analytics & System Dynamics – April 2014

4. 190,000shortage of analytics specialists in the US
alone (Manyika et al, 2010)
$225,000starting salaries for data scientists
(Loizos, 2013)
$300p/h
hourly rate to hire data scientists
via Kaggle (Granville, 2013)
1. Why Analytics?
Big Data, Analytics & System Dynamics – April 2014
$105,000,000,000size of the business analytics market in 2010 (IBM, 2010)
83%“of c-suite executives agree the importance of
using information effectively has never been
greater” (SAS, 2009)
4

5. 1. Why Big Data?
3,000,000,000,000
1,200,000,000,000,000
0
200,000,000,000,000
400,000,000,000,000
600,000,000,000,000
800,000,000,000,000
1,000,000,000,000,000
1,200,000,000,000,000
How Much Data is There in the World?
2010
1997
Sources: Lesk (1997) and Gow (2010)
Big Data, Analytics & System Dynamics – April 20145

6. 1. Analytics & Operational Research?
Big Data, Analytics & System Dynamics – April 2014
The Analytics Network
www.theorsociety.com/
Pages/SpecialInterest/
AnalyticsNetwork.aspx
6

7. 1. Big Data & System Dynamics?
Big Data, Analytics & System Dynamics – April 20147

8. 1. The Red Pill or the Blue Pill?
Big Data, Analytics & System Dynamics – April 20148

9. 2. Relationship Problems
Big Data, Analytics & System Dynamics – April 2014
≈Analytics OR/MS
Analytics
OR/MS Analytics
OR/MS
OR/MSAnalytics
≠Analytics OR/MS
6% 7%
28% 29% 30%
Source: Liberatore and Luo (2011)
9

10. 2. Relationship Problems
Big Data, Analytics & System Dynamics – April 2014
vs. vs.
10

11. 3. The Dianoetic Management Paradigm
Big Data, Analytics & System Dynamics – April 201411

12. 3. The Dianoetic Management Paradigm
Big Data, Analytics & System Dynamics – April 201412
System
Dynamics

13. 3. The Dianoetic Management Paradigm
Big Data, Analytics & System Dynamics – April 2014
Scientific Management (1910-1945)
Technology
c1913 The Ford Model 1 began production using
its influential assembly lines
1914 The end of The Technological Revolution
1941 The first digital computer, Z1, released
Quantitative Methods
1935 Publication of Fisher’s The Design of
Experiments
1938 First discussions of ‘OR’ (Kirby, 2003 p 71)
1939 Development of cluster analysis
Decision Making
1912 The principles of Gestalt visual perception
devised (Wagemans et al, 2012)
1921 Launch of the Cambridge Psychological
Laboratory designed to distribute the
results of studies amongst industry
The Scientific Method (1945-1960s)
13

14. 3. The Dianoetic Management Paradigm
Big Data, Analytics & System Dynamics – April 2014
Management Info Systems (1960s-1970s) Decision Support Systems (1970s-1980s)
Technology
c1963 The development of microchips
1964 Release of the IBM System/360
c1970 E. F. Cobb conceptualises the first
relational databases (Date, 2000)
Quantitative Methods
c1963 Geography’s Quantitative Revolution
demonstrating the growth of quantitative
methods in academia (Burton, 1963)
1964 The first UK master’s degree in OR/MS
Decision Making
1962 The Myers Briggs Type Indicator published,
used to understand decision maker types
c1962 Behavioural science grows in influence,
particularly in consumer research
c1969 First study into computer-aided decision
making (Ferguson and Jones, 1969)
Technology
c1972 Personal computers are popularised in
businesses (Ceruzzi, 1999, pp 207-241)
c1972 TCP / IP internet protocols introduced
1973 IBM 3660 Supermarket System released
introducing barcode scanners
Quantitative Methods
c1975 ‘S’ statistical language and Matlab are
launched. SPSS and SAS grow in
popularity (Wegman et al, 1997)
1979 Development of the ID3 decision tree
algorithm (the predecessor of C4.5)
Decision Making
1979 Research into decision making needs of
CEOs leads to the design of Executive
Information Systems (Rockart, 1979)
1981 Development of soft systems methodology
14

15. 3. The Dianoetic Management Paradigm
Big Data, Analytics & System Dynamics – April 2014
Business Intelligence (1980s-1990s) Analytics (2000 – Present Day)
Technology
1988 The conceptualisation of data warehouse
architecture Devlin and Murphy, 1988)
1989 Launch of the world-wide-web
Quantitative Methods
c1988 The first significant research into agent
based modelling (Samuelson, 2000)
1989 Piatesky-Sharpio introduces the term ‘data
mining’ (He, 2009)
c1996 General Electric introduces Six Sigma to its
operations (Henderson and Evans, 2000)
Decision Making
1992 Development of balanced scorecards
(Kaplan and Norton, 1992)
2000 Popularisation of business dashboards
(Marcus, 2006)
Technology
2004 Google’s Dean and Ghemawat publish a
paper detailing MapReduce, the big data
programming paradigm
2004 Launch of Facebook (Twitter in 2006)
2007 Development of NoSQL databases
Quantitative Methods
2001 The release of the Natural Language
Toolkit, helping popularise text mining
2008 Anderson’s The End of Theory published
2010 The first Kaggle competition
Decision Making
2005 eBay buy shopping.com, highlighting the
importance of recommendation agents
2013 Tableau, the data visualisation software,
valued at $2bil after two days on the
Stock Exchange (Cook, 2013)
15

16. 3. The Dianoetic Management Paradigm
Big Data, Analytics & System Dynamics – April 2014
The Isolationist Approach
vs.
The Faddist Approach
16 Source: Mortenson, Doherty, Robinson (Forthcoming)

17. 4. Categories of Analytics
Big Data, Analytics & System Dynamics – April 2014
Source: Blackett, 2012
17

18. 4. Categories of Analytics
Big Data, Analytics & System Dynamics – April 201418

19. 4. Categories of Analytics
Big Data, Analytics & System Dynamics – April 2014
Descriptive Analytics
Predictive Analytics Prescriptive Analytics
Statistical and data modelling techniques designed to describe past
events and answer “what happened”?
Data mining and machine
learning techniques used to
predict future events and answer
“what will happen next”?
OR/MS, mathematical and
statistical models used to prescribe
future actions and answer “what
should we do next”?
Technological Strategic
Lower Risk Decisions Higher Risk Decisions
Discovery Analytics Decision Analytics
Advanced Discovery
Analytics
Reporting & alerts
Market research
ERP & information systems
Basic historical analysis
Performance metrics
Stakeholder consultation
Advanced visualisation
Real time insights
Automated learning models
Advanced Decision
Analytics
Optimisation
Problem structuring
Modelling & simulation
Advanced
19

20. 4. Categories of Analytics
Big Data, Analytics & System Dynamics – April 201420
Discovery Analytics Decision Analytics
Describe and summarise the data
and business context
Describe and summarise the
problem situation and/or system
Build models than can make
predictions about unseen data
(holdout or future data)
Build models than can predict
how the system would respond to
different stimuli or conditions
Prescribe future actions based
upon the model
Recommend
Prescribe future actions based
upon the model
Recommend

21. 5. Implications for System Dynamics
Big Data, Analytics & System Dynamics – April 201421

22. 5. Implications for System Dynamics
Big Data, Analytics & System Dynamics – April 201422
High
volume
data
Unstructured
data Streaming &
real-time data
Big data
architecture
(e.g. Hadoop)
Data
visualisation
Decision
automation