Chief Analytics Officer, Canada 2016 – Alfred Essa – Predictive, prescriptive and experimental analytics for the smart enterprise

1. Alfred Essa Vice President, R&D and Analytics McGraw-Hill Education
 @malpaso [email protected] Entrepreneurial 
 Analytics
2. Mindset
3. Toolkit
4. “How can I innovate and be a rockstar
 in Analytics?”
5. “How can I innovate 
 on a miserly budget?”
6. Analytics Innovation for Misers
7. Live Demo
8. Alfred Essa • McGraw-Hill Education, VP, R&D and Analytics • Desire2Learn, Director of Innovation & Analytics Strategy • MIT Sloan School of Management, CIO • Minnesota State Colleges and Universities, Assoc Vice Chancellor and Deputy CIO • dotLRN Open Source Project, Founder
9. McGraw-Hill Education Learning Sciences Company ::::
10. Intelligent, personalized, adaptive software based on AI and Advanced Analytics
11. Innovation
12. – Samuel Johnson “Depend upon it, sir, when a man knows he is to be hanged in a fortnight, it concentrates his mind wonderfully.”
13. Innovation = Speed
14. Innovation = 
 Time to Market
15. Time to Market Be Like Bolt!
16. Stage I Stage II Stage III research product validation product development Innovation Pipeline
17. Cycle from idea to product 
 < 12 months
18. Cadence: Major release every quarter
19. # 1: 
 Create an Analytics 
 Innovation Pipeline
20. Analytics
21. Analytics Levels informationvalue insight ReportingData Access what happened? what is happening? past, present Optimization Strategy what do i want to happen? desired future Risk 
 Forecasting Predictive
 Modeling what will happen? future
22. How am I doing? How will I do? How can I do better?
23. # 2: 
 Analytics is Actionable Insights (Optimization, Simple Visualizations), not Dashboards and Operational Reports
24. Don’t drown in the mud of operational reports!
25. Data
26. – Dan Ariely “Big data is like teenage sex: everyone talks about it, no one really knows how to do it; everyone thinks everyone else is doing it, so everyone claims they are doing it…”
27. Big Data is overrated
28. Think Small Data
29. “If a man sees a fly, he aims at it”
30. – Sunstein & Thaler “Small and apparently insignificant details can have major impact on people’s behavior.”
31. Some Nudges • Increase retirement savings by opt-out vs opt-in: 
 
 90% vs 20% for new employees • Decrease energy use by providing information on consumption compared to neighbors:
 
 above average users decreased their use immediately • Organ donation by opt-out vs opt-in
32. Nudges
33. Behavioral Economics
34. Behavioral Economics
35. # 3: 
 Use the Power of Nudge Analytics
36. Teams
37. Analytics Army
38. Special Operations
39. # 4: 
 Create a Small Team (“special operations”): Crack the hardest problems
40. Architecture
41. “Type a quote here.” Traditional Business Intelligence Architecture
42. Lambda Architecture
43. # 5: 
 Use an Open Source stack in your research environment (e.g. Apache Kafka, Spark, Python, PostGRES)
44. Apache Spark • Apache Spark is a fast and general engine for large-scale data processing • Can run programs up to 100x faster than Hadoop MapReduce in memory, or 10x faster on disk • Ideal for parallelizing algorithms
45. Discovery
46. Discovery: Grammar of Science
47. Sir Francis Bacon 1561-1626 Lord Keeper, and afterwards Lord Chancellor of England
48. Questions NATURE Experiments Language Data Iterate Predictions! Interpret Natural Laws Hypotheses
49. Questions DomainExploratory Data Analysis Models Programming, Computation Data Iterate Confirmatory Data Analysis Predictions! Visualizations
50. Exploratory Data Analysis
51. John Tukey 1915-2000 Father of Modern Data Science
52. Exploratory vs Confirmatory Data Analysis
53. “Exploratory data analysis is detective work.” “Exploratory data analysis can never be the whole story, but nothing else can serve as the foundation stone.”
54. “There is no excuse for failing to plot and look (if you have graph paper).” “Graphs are friendly. Graphs force us to note the unexpected, nothing could be more important…There is no more reason to expect one graph to “tell all” than to expect one number to do the same ”
55. Coined the term “software” and also the term “bit”
56. • “The first thing to do when you get a set of data for analysis is not to run through a fancy algorithm. Make some graphs, some plots. Look at the data.” • “And we need to remember that, even with big data, you should look at it first before you jump in with an analysis.”
57. “Is there a tool that enables this iterative workflow and which can be shared with other researchers and within and across teams?”
58. Demo
59. # 6: 
 For Discovery start with Exploratory Data Analysis using Jupyter, R, and Scala
60. Thank You! @malpaso [email protected] You Tube: Awesome Data Science Linkedin: Alfred Essa

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