Published on April 5, 2014
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
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/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
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
1. Analytics & Operational Research? Big Data, Analytics & System Dynamics – April 2014 The Analytics Network www.theorsociety.com/ Pages/SpecialInterest/ AnalyticsNetwork.aspx 6
1. Big Data & System Dynamics? Big Data, Analytics & System Dynamics – April 20147
1. The Red Pill or the Blue Pill? Big Data, Analytics & System Dynamics – April 20148
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
2. Relationship Problems Big Data, Analytics & System Dynamics – April 2014 vs. vs. 10
3. The Dianoetic Management Paradigm Big Data, Analytics & System Dynamics – April 201411
3. The Dianoetic Management Paradigm Big Data, Analytics & System Dynamics – April 201412 System Dynamics
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
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
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
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)
4. Categories of Analytics Big Data, Analytics & System Dynamics – April 2014 Source: Blackett, 2012 17
4. Categories of Analytics Big Data, Analytics & System Dynamics – April 201418
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
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
5. Implications for System Dynamics Big Data, Analytics & System Dynamics – April 201421
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
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