system dynamics, analytics & big data (16th conference of the uk chapter of the system dynamics...

Analysing Analytics:Evolution or Emperor's New Clothes?

System Dynamics Society, April 2014Michael Mortenson, Neil F. Doherty & Stewart Robinson

Big Data, Analytics & System Dynamics:

The Red Pill or the Blue Pill?

Structure1. 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

Competing on Analytics

3 Big Data, Analytics & System Dynamics – April 2014

190,000shortage of analytics specialists in the US

alone (Manyika et al, 2010)

$225,000starting salaries for data scientists

(Loizos, 2013)

$300p/hhourly 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

1. Why Big Data?

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

3,000,000,000,000

1,200,000,000,000,000

How Much Data is There in the World?

40,000%

2010

1997

Sources: Lesk (1997) and Gow (2010) Big Data, Analytics & System Dynamics – April 20145

1. Analytics & Operational Research?


The Analytics Networkwww.theorsociety.com/Pages/SpecialInterest/AnalyticsNetwork.aspx

6

http://www.theorsociety.com/Pages/SpecialInterest/AnalyticsNetwork.aspx





http://www.theorsociety.com/Pages/SpecialInterest/AnalyticsNetwork_magazine.aspx

http://viewer.zmags.com/publication/6af8b2ed

http://meetings.informs.org/analytics2014

http://meetings.informs.org/bigdata2014

1. Big Data & System Dynamics?


1. The Red Pill or the Blue Pill?




≈Analytics OR/MS

Analytics

OR/MS Analytics

OR/MSOR/MSAnalytics

≠Analytics OR/MS

6% 7%

28% 29% 30%Source: Liberatore and Luo (2011)9



vs. vs.

10



System Dynamics



Scientific Management (1910-1945)

Technology1945 Design of the von Neumann Architecture the computer structures still used today1952 The UNIVAC computer predicts the US presidential election1957 FORTRAN programming language devised

Quantitative Methods1947 Linear programming developedc1947 OR/MS methods used to help rebuild UK

industry (Kirby, 2003, pp 190-105)

Decision Making1946 Formation of the Ergonomics Society1947 Simon’s Administrative Behavior published c1959 Judy Clap leads the development of the

first graphical user interface (Grer, 2002)

Technologyc1913 The Ford Model 1 began production using its influential assembly lines1914 The end of The Technological Revolution1941 The first digital computer, Z1, released

Quantitative Methods1935 Publication of Fisher’s The Design of

Experiments1938 First discussions of ‘OR’ (Kirby, 2003 p 71)1939 Development of cluster analysis

Decision Making1912 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



Management Info Systems (1960s-1970s) Decision Support Systems (1970s-1980s)

Technologyc1963 The development of microchips1964 Release of the IBM System/360c1970 E. F. Cobb conceptualises the first relational databases (Date, 2000)

Quantitative Methodsc1963 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 Making1962 The Myers Briggs Type Indicator published,

used to understand decision maker typesc1962 Behavioural science grows in influence,

particularly in consumer researchc1969 First study into computer-aided decision

making (Ferguson and Jones, 1969)

Technologyc1972 Personal computers are popularised in businesses (Ceruzzi, 1999, pp 207-241)c1972 TCP / IP internet protocols introduced1973 IBM 3660 Supermarket System released introducing barcode scanners

Quantitative Methodsc1975 ‘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 Making1979 Research into decision making needs of

CEOs leads to the design of Executive Information Systems (Rockart, 1979)

1981 Development of soft systems methodology

14



Business Intelligence (1980s-1990s) Analytics (2000 – Present Day)Technology1988 The conceptualisation of data warehouse architecture Devlin and Murphy, 1988)1989 Launch of the world-wide-web

Quantitative Methodsc1988 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 Making1992 Development of balanced scorecards

(Kaplan and Norton, 1992)2000 Popularisation of business dashboards

(Marcus, 2006)

Technology2004 Google’s Dean and Ghemawat publish a paper detailing MapReduce, the big data programming paradigm2004 Launch of Facebook (Twitter in 2006)2007 Development of NoSQL databases

Quantitative Methods2001 The release of the Natural Language

Toolkit, helping popularise text mining2008 Anderson’s The End of Theory published2010 The first Kaggle competition

Decision Making2005 eBay buy shopping.com, highlighting the

importance of recommendation agents2013 Tableau, the data visualisation software,

valued at $2bil after two days on the Stock Exchange (Cook, 2013)

15



The Isolationist Approach

vs.The Faddist Approach

16 Source: Mortenson, Doherty, Robinson (Forthcoming)


Big Data, Analytics & System Dynamics – April 2014Source: Blackett, 201217



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 & alertsMarket research

ERP & information systems

Basic historical analysisPerformance metrics

Stakeholder consultation

Advanced visualisationReal time insights

Automated learning models

Advanced Decision Analytics

OptimisationProblem structuring

Modelling & simulation

Advanced

19



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

RecommendPrescribe future actions based

upon the model

Recommend



High volume

data

Unstructured data Streaming &

real-time data

Big data architecture

(e.g. Hadoop)

Data visualisation

Decision automation

system dynamics, analytics & big data (16th conference of the uk chapter of the system dynamics...

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