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YHORG Presentation 23 February 2016

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YHORG Presentation 23 February 2016

  1. 1. Hull University Business School Connected Thinking! OR and Data Science: a match made in heaven? Dr. Giles A. Hindle Professor Richard Vidgen YHORG: 23 February 2016
  2. 2. Agenda • What is OR? • How is the world of the OR practitioner changing? • What is data science? • Are OR and data science the same? • The contribution of soft OR – a business analytics methodology • Putting the pieces together • Discussion
  3. 3. What is OR? “The discipline of applying advanced analytical methods to help make better decisions” (INFORMS and ORS) NOTE: The definition is very broad. Could apply to OR, Soft OR Data Science, Business Analytics, Business Analyst, Market Research.
  4. 4. The OR Approach Operation research leads to a model of the operation analysis leading to solutions used to conduct applied to library of techniques: Inventory Control, Linear Programming, Optimisation, Queuing Theory, Simulation, Forecasting, etc. Overlaps: Industrial Engineering Operations Management
  5. 5. Examples of OR in Practice • Scheduling: of aircrews and the fleet for airlines, of vehicles in supply chains, of orders in a factory and of operating theatres in a hospital. • Facility planning: computer simulations of airports for the rapid and safe processing of travellers, improving appointments systems for medical practice. • Forecasting: identifying possible future developments in telecommunications, deciding how much capacity is needed in a holiday business. • Yield management: setting the prices of airline seats and hotel rooms to reflect changing demand and the risk of no shows. • Credit scoring: deciding which customers offer the best prospects for credit. • Marketing: evaluating the value of sale promotions, developing customer profiles and computing the life-time value of a customer. • Defence and peace keeping: finding ways to deploy troops rapidly.
  6. 6. Typical of OR models Used • Descriptive models • Predictive models (Forecasting) • Optimising Models (Linear Programming, Heuristics) • Experimental Models (Simulation, System Dynamics)
  7. 7. What’s happening in the world? • Data is getting bigger
  8. 8. What’s happening in the world? • The cloud
  9. 9. What’s happening in the world? • The Internet of Things
  10. 10. What’s happening in the world? • Ubiquity
  11. 11. Data science: types of model • Descriptive – e.g., what are the characteristics of customers who churn? • Predictive – e.g., which customers are most likely to churn? • Prescriptive – e.g., what action should we take for customers at high risk of churn?
  12. 12. http://www.slideshare.net/datasciencelondon/big-data-sorry-data-science-what-does-a-data-scientist-do What is a data scientist?
  13. 13. Some of the techniques data scientists use • Classification • Clustering • Association rules • Decision trees • Regression • Genetic algorithms • Neural networks and support vector machines • Machine learning • Natural language processing • Sentiment analysis • Artificial intelligence • Time series analysis • Simulations • Social network analysis
  14. 14. Technologies for data analysis: usage rates King, J., & R. Magoulas (2013). Data Science Salary Survey. O’Reilly Media. R and Python programming languages come above Excel Enterprise products bottom of the heap
  15. 15. (a) (b) Figure 1: (a) the data scientist (Conway, 2011); (b) business analytics (Robinson, 2014)
  16. 16. Are OR and data science practitioners the same types of people? “… data science is not merely statistics, because when statisticians finish theorizing the perfect model, few could read a tab-delimited file into R if their job depended on it.” (Mike Driscoll, quoted by O’Neill and Schutt, 2014, p. 7). Or, put another way, Josh Wills’ definition: “data scientist (noun): Person who is better at statistics than any software engineer and better at software engineering than any statistician”.
  17. 17. Soft OR and Problem Structuring Methods (PSMs) • PSMs offer “a way of representing the situation...that will enable participants to clarify their predicaments, converge on a potentially actionable mutual problem or issue within it, and agree commitments that will at least partially resolve it” (Mingers and Rosenhead 2004). • Soft Systems Methodology (SSM) used by the IS community to help appreciate the context of and need for IT • SSM helps define the business model which defines business valued within a situation
  18. 18. Business Logic: Value driven by Business Model Business Model Analytical Work
  19. 19. Business Analytics Methodology
  20. 20. Business Analytics Methodology
  21. 21. Business Analytics Methodology
  22. 22. Business Analytics Methodology
  23. 23. Business Analytics Methodology
  24. 24. Business Analytics Methodology
  25. 25. Food bank app – multiple technologies • To get the base data on food bank usage • SQL • To scrape Census data and get Google maps travel times • Python • To prepare data, create predictive models, and create data for visualization • R • To render the geospatial visualization as an interactive app for the user • D3 • topoJSON • GDAL • OpenStreetMap • Javascript
  26. 26. Putting it together Soft OR Hard OR Data science Organisational context and sources of value Business analytics methodology Analytics outputs (desc, pred, prescrip) 1. OR practitioners need IT skills (also need to know where to start and where to stop with IT) 2. OR practitioners need to work with different data types (e.g., text, video) 3. OR practitioners need to step out of specialist departmental niche (go beyond the engineering mind set) 4. Analytics is embedded in the organization – not a separate function (prescriptive as well as predictive analytics) 5. Analytics is about organizational transformation – culture change is needed to become data-driven1. Get IT skills 5. Cultural change 4. Embedded, prescriptive 2. Work with heterogeneous, open, and messy data 3. Get out of comfort zone
  27. 27. Questions and discussion

Notas do Editor

  • Systematic: that which is characterised by order and planning.
    Analysis: the separation of a whole into its constituent parts for individual study.
  • Data scientists needs more skills than programming or stats and must be inquisitive.
  • Many techniques are available.
    The most common is classification used for recommendation systems as on Amazon.
  • New technologies to handle getting data in (e.g., real-time feeds), storage, and retrieval.
    Tools for the data analyst involve statistics, programming, maths, visualization.