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2015 Society of Actuaries Life/Annuity Symposium Presentation

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2015 Society of Actuaries Life/Annuity Symposium Presentation

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2015 Society of Actuaries Life/Annuity Symposium Presentation

  1. 1. What’s the Big Deal about Big Data…for Actuaries? Neil Raden Founder, Hired Brains Research Twitter: @NeilRaden Blog: http://hiredbrains.wordpress.com Website: http://www.hiredbrains.com Mail: nraden@hiredbrains.com LinkedIn: http://www.linkedin.com/in/neilraden
  2. 2. Neil Raden Neil Raden is the founder and Principal Analyst at Hired Brains Research LLC, , a provider of consulting and implementation services to many Global 2000 companies since 1985, providing research and advisory services focusing on Big Data, Analytics, Decision Management and Business Intelligence. He began his career as a Property & Casualty actuary with AIG in New York before moving into predictive analytics services, software engineering, and systems integration with experience in delivering environments for decision making. He is the co-author of the book “Smart (Enough) Systems: How to Deliver Competitive Advantage by Automating Hidden Decisions,” 2007, Prentice Hall. His blogs appear at InformationWeek, SmartDataCollective and http://hiredbrains.wordpress.com. He is a regular contributor to Forbes, LinkedIn Groups, Focus, Quora and eBizQ and was also an early Wikipedia editor and administrator in areas of technology, health care and mathematics. EMAIL: nraden@hiredbrains.com TWITTER USERNAME: @neilraden LINKEDIN PROFILE: http://www.linkedin.com/in/neilraden Copyright 2015 Neil Raden and Hired Brains Research LLC 2
  3. 3. Willie Sutton: Infamous Bank Robber Q: Willie, why do you rob banks? A: Because that’s where the money is
  4. 4. 4 1950 1960 1970 1980 1990 2000 Batch Reporting CICS/OLTP C/S OLTP Y2K/ERP 4GL/PC/SS DW/BI Convergence Convergence is Here 2010 Operational BI Composite Apps BPM Semantics Decision Automation History of the Rift Between Operational and Analytical Processing Copyright 2015 Neil Raden and Hired Brains Research LLC
  5. 5. Big Is Relative This Pace Isn’t New, Just Magnitude Copyright 2015 Neil Raden and Hired Brains Research LLC 5 Though Volume is interesting, it isn’t what distinguishes Big Data
  6. 6. Moore’s Law Copyright 2015 Neil Raden and Hired Brains Research LLC 6
  7. 7. Different Way to Visualize It Copyright 2015 Neil Raden and Hired Brains Research LLC 7
  8. 8. No More Managing from Scarcity Copyright 2015 Neil Raden and Hired Brains Research LLC 8
  9. 9. Data Warehouse and Hadoop Data Warehouse Hadoop Characteristics Use Cases Characteristics • High performance analytics and complex joins • High concurrency • SQL (ANSI and ACID compliant) • Advanced workload mgmt. • High Availability • Data Governance • Emerging Late Binding • Fine Grain Security • One-stop support • Fast Data Landing and Refinment • Processing Flexibility • Emerging SQL/SQL-like interfaces • Batch-oriented processing • Low workload concurrency • Multi-structured and file based data • Late Binding • Open Source Community • Low $/TB • Long-Term Raw Data Storage • ETL • Reporting • Deep Analytics Copyright 2015 Neil Raden and Hired Brains Research LLC 9
  10. 10. Even Big Data Doesn’t Speak for Itself Copyright 2015 Neil Raden and Hired Brains Research LLC 10 • Incomplete • Behaviors under- represented • Anonymizing disasters • Single source of data inadequate • Harmonization Not a crystal ball
  11. 11. How Operational Intelligence Expands Current Technology Copyright 2015 Neil Raden and Hired Brains Research LLC 11
  12. 12. Compare This with a Hadoop Copyright 2015 Neil Raden and Hired Brains Research LLC 12
  13. 13. The combination of some data and an aching desire for an answer does not ensure that a reasonable answer can be extracted from a given body of data. John Tukey Copyright 2015 Neil Raden and Hired Brains Research LLC 13
  14. 14. Decisions: A Miracle Happens? Copyright 2015 Neil Raden and Hired Brains Research LLC 14 40 years with decision support and BI. Are we making better decisions Will Data Science Lead Us to Better Decision Processes? Getting to a culture of decision making requires you to have real, solid wins using analytics to make people care from top to bottom.
  15. 15. What Is Data Science? • Discovering what we don’t know from data • Getting predictive and/or actionable insight • Development of data products that have clear business value • Providing value to the organization through sharing and learning • Using techniques like storytelling and metaphor to explain concepts • Building confidence in decisions
  16. 16. Do You Know This Number? Copyright 2015 Neil Raden and Hired Brains Research LLC 16 2.718281828459... Why is this important
  17. 17. Euler Gave Us the Tools Copyright 2015 Neil Raden and Hired Brains Research LLC 17 Contribution Example Graph Theory Graph & Ontology Databases Infinitesimal Calculus Everything Topology Topological Data Analysis Number Theory Encryption Nothing we do in Big Data would be possible without Euler
  18. 18. But Euler Got One Thing Wrong Copyright 2015 Neil Raden and Hired Brains Research LLC 18 • Tobias Mayer • A contemporary of Euler • Famous for his observations of the libration of the moon • TONS of observations • Figured out how to group them Famous quote: Because these observation were derived from nine times as many observations, one can therefore conclude that they are nine times more accurate”
  19. 19. Euler Not a Data Scientist Copyright 2015 Neil Raden and Hired Brains Research LLC 19 Euler:“By the combination of two or more equations, the errors of the combinations and the calculations multiply themselves.” The greatest mathematician of all time pre-dated the concept of statistical error
  20. 20. One Way to Become a Data Scientist: Mugging injury turns man into math genius A brutal beating outside a club left college dropout Jason Padgett with brain damage. But the furniture store worker discovered he could draw diagrams, turning mathematical formulae into stunning works of art Don’t try this at home Copyright 2015 Neil Raden and Hired Brains Research LLC 20
  21. 21. Why Does This Matter? Copyright 2015 Neil Raden and Hired Brains Research LLC 21 Because Data Science is not the realm of the most brilliant mathematicians It’s for people who know how to do it and who have the correct training and tools to do it themselves
  22. 22. The Data Scientist • Term invented by Yahoo • Super-tech, super-quant • Business expert too • Orientation: Search and Web • We used to call them quants • Few and far between • How do you find/train them? • Hint: like actuaries Copyright 2015 Neil Raden and Hired Brains Research LLC 22
  23. 23. Copyright 2015 Neil Raden and Hired Brains Research LLC 23 Chief Actuary of GeoSpatial Analytics and Modeling Chief Analytic Officer Chief Analytics & Algorithms Officer Chief Analytics Officer Chief Credit & Analytics Officer Chief Data and Analytics Officer Chief Research & Analytics Officer Chief Scientist, Global Head of Analytics Chief Scientist, VP of Analytics Chief Technology Officer, Enterprise Information Management & Analytics Client Director, Business Analytics Director - Advanced Analytics Director - Analytic Science Director – Analytics Delivery Director - BI & Analytics Director - Fraud Analytics & R&D Director - Predictive Analytics Director (Analytics and Creative Strategy) Director (Marketing Analytics) Director : Digital Analytics Director Analytics Strategy, JMP Director Marketing Analytics Director of Advanced Analytics Director of Analytic Consulting, Product/Data Loyalty Analytics Director of Analytic Solutions Director of Analytics Director of Analytics (consultant) Director of Data Analytics and Advertising Platforms Director of Digital Analytics and Customer Insight Director of Health Analytics Director of Innovation, Big Data Analytics Director of Product, Analytics Director of Risk Analytics and Policy Director of Science & Analytics for Enterprise Marketing Management (EMM) Director of Web Analytics and Optimization Director, Advanced Analytics Director, Advanced Analytics, HumanaOne Director, Advanced Strategic Analytics Director, Analytic Science Director, Analytic Strategy Director, Analytical Services Director, Analytics Director, Big Data Analytics and Segmentation Director, Business Analytics Director, Business Analytics & Decision Management Strategy Director, Business Intelligence & Analytics, Pogo Director, Business Intelligence and Analytics Director, Business Planning & Analytics Director, Center for Business Analytics, Stern School of Business Director, Clinical Analytics Director, Customer Analytics Director, Customer Analytics & Pricing Director, Customer Insights and Business Analytics Director, Data Analytics Director, Data Science & Analytics Practice Director, Data Warehousing & Analytics Director, Database Marketing & Analytics (Marketing) Director, DVD BI and Analytics Director, Gamification Analytics Platform, Information Analytics & Innovation Director, Global Digital Marketing Analytics Director, Group Analytics Director, Head of Forensic Data Analytics Director, Marketing Analytics Director, Marketing Analytics for Bing Product Group Director, Oracle Database Advanced Analytics Director, Predictive Analytic Applications Director, Reporting/Analytics Director, Risk & Analytics Director, Risk and Business Analytics Director, Statistical Modeling and Analytics Director, Statistics and Project Analytics / Senior Analytic Consultant Director, Strategic Analytics Director, Web Analytics Director/Head of Analytics Director/Principal, Analytics This Is Getting Ridiculous
  24. 24. Here Comes the “Citizen” Data Scientist • Gartner • Davenport: “Light Quants” • The truth: Training individuals to use stat/ML icons is pointless • How do you organize for it? Copyright 2015 Neil Raden and Hired Brains Research LLC 24
  25. 25. Stat Tools Can Be Dangerous Copyright 2015 Neil Raden and Hired Brains Research LLC 25 • Tests are not the event • Tests are flawed Tests detect things that don’t exist • Tests give test probabilities not the real probabilities • False positives skew results • People prefer natural numbers • Even Science is a test
  26. 26. Anscombe’s Quartet Copyright 2015 Neil Raden and Hired Brains Research LLC 26
  27. 27. Spurious Correlation Copyright 2015 Neil Raden and Hired Brains Research LLC 27
  28. 28. Texas Sharpshooter Fallacy Copyright 2015 Neil Raden and Hired Brains Research LLC 28
  29. 29. • It’s not just about knowing and using quantitative models • You have to understand the meaning of the data Copyright 2015 Neil Raden and Hired Brains Research LLC 29
  30. 30. Definition vs. Meaning -Neil Armstrong -Apollo 11 -July 20, 1969 -Tranquility Base, Moon, 90210 -First human to step on another planet -End of the “space race” -Healthcare diagnostics & therapeutics -Microelectronics -Conspiracy theories: where are the stars? Definition Meaning
  31. 31. Deriving Meaning from Text Not Easy “Katy Perry and Russell Brand are now officially husband and wife.” She doesn’t look like a husband… But neither does he, actually.
  32. 32. Big Data Analytics Economics • Human resources to exploit opportunities are expensive • When demand exceeds supply, suppliers use “allocation” • 60,000 – 120,000 unfilled data scientist jobs in US Data scientists “allocated” to most critical (economically lucrative) efforts, and their time is limited to those tasks that most completely leverage their unique skills Copyright 2013 Neil Raden and Hired Brains Research LLC 32
  33. 33. Copyright 2015 Neil Raden and Hired Brains Research LLC 33 Types of Analytics Data Mining X X X X X X X X X X X X X X X X X X X X X X XX X X X X X X X X X X X X X X X X X X XX X X X X X X X X X X X X X X X X X X X X X X X X X Who are my best/worst customers? How do I turn my data into rules for better decisions? Predictive Analytics How are those customers likely to behave in the future? How do they react to the myriad ways I can “touch” them? Optimization How do make the best possible decisions given my constraints? Knowledge - Description Action - Prescription Business Intelligence How do I use data to learn about my customers? What has been happening in my business?
  34. 34. Copyright 2015 Neil Raden and Hired Brains Research LLC 34 Impact May Take Time to Play Out
  35. 35. Types of Analysis and Roles Descriptive Title Quantitative Sophistication/Numeracy Sample Roles Type I Quantitative R&D PhD or equivalent Creation of theory, development of algorithms. Academic /research. Work in business/government for very specialized roles Type II Data Scientist or Quantitative Analyst Advanced Math/Stat, not necessarily PhD Internal expert in statistical and mathematical modelling and development, with solid business domain knowledge. Type III Operational Analytics Good business domain, background in statistics optional Running and managing analytical models. Strong skills in and/or project management of analytical systems implementation Type IV Business Intelligence/ Discovery Data and numbers oriented, but no special advanced statistical skills Reporting, dashboard, OLAP and visualization, some design, posterior analysis of results from quantitative methods. Spreadsheets, “business discovery tools” Copyright 2015 Neil Raden and Hired Brains Research LLC 35 Analytic Types
  36. 36. Types of Analysis Descriptive Title Quantitative Sophistication/Numeracy Sample Roles Type I Quantitative R&D PhD or equivalent Creation of theory, development of algorithms. Academic /research. Work in business/government for very specialized roles Type II Data Scientist or Quantitative Analyst Advanced Math/Stat, not necessarily PhD Internal expert in statistical and mathematical modelling and development, with solid business domain knowledge. Type III Operational Analytics Good business domain, background in statistics optional Running and managing analytical models. Strong skills in and/or project management of analytical systems implementation Type IV Business Intelligence/ Discovery Data and numbers oriented, but no special advanced statistical skills Reporting, dashboard, OLAP and visualization, some design, posterior analysis of results from quantitative methods. Spreadsheets, “business discovery tools” Copyright 2015 Neil Raden and Hired Brains Research LLC 36 Analytic Types Type V Better BI/Viz/Disco Training/Mentoring/Apps Training/Mentoring/Apps 3rd Party Services Type Shifting
  37. 37. A Typical Day • Basic data manipulations to wrangle data and fit a variety of standard models -40% • Translate a business problem into the design of a data analysis strategy - 5% • Graphically explore data to motivate modeling choices and improvements– 10% • Interpret and critically examine standard model output – 5% • Test the performance of models on holdout data - 10% • Go to meetings – 30% Copyright 2015 Neil Raden and Hired Brains Research LLC 37 70% is not Data Scientist work
  38. 38. Type Shifting Copyright 2015 Neil Raden and Hired Brains Research LLC 38 • As much as 80% of “Data Scientist” work can be done by others • Data gathering, cleansing, profiling, parsing and loading • Data and process stewardship • Platform availability • Providing organizational and market domain expertise • Creation of presentation material
  39. 39. Analytics is hard and takes resources Analytics takes effort to create and assimilate Focus analytics on key leverage points of business UPS focuses on where the package is Marriott focuses on yield management If you try to do everything, won’t do anything well. Copyright 2015 Neil Raden and Hired Brains Research LLC 39 Analytics Is Hard
  40. 40. A Final Thought About Analytics The challenge of analytics is communication and creating a shared understanding. It’s about focusing on high impact areas, moving forward one step at a time, being skeptical, being creative, searching for the truth. Any company can “Compete on Analytics.” But not like this Copyright 2015 Neil Raden and Hired Brains Research LLC 40 StockMarket Returns for the “Competing on Analytics” Cohort -80% -40% 0% 40% 80% 120% Amazon Marriott Honda Intel Novartis Wal-Mart UPS Verizon P&G Progressive CapitalOne Yahoo Dell Barclays Average Stock Market Return
  41. 41. Five Things to Remember • Data is an “asset,” people make it valuable • Your data scientists may well be a team • Communication, insight and reason more important than math • You have lurking data scientists in your firm • Start with what matters, build confidence Copyright 2015 Neil Raden and Hired Brains Research LLC 41
  42. 42. Thank You Copyright 2015 Neil Raden and Hired Brains Research LLC 42 Neil Raden Founder, Hired Brains Research Twitter: NeilRaden Blog: http://hiredbrains.wordpress.com Website: http://www.hiredbrains.com Mail: nraden@hiredbrains.com LinkedIn: http://www.linkedin.com/in/neilraden
  43. 43. Apparently Life Insurance Is Ahead of All Other Industries in Big Data* Copyright 2015 Neil Raden and Hired Brains Research LLC 43 2/3’s of Life companies deployed big data analytics < 5 years ago 33% claim full-scale operations for > a decade ½ using big data analytics for six or more functions including: • marketing initiatives, • sales lead generation, • underwriting, • claims/fraud detection and prevention * http://www.lifehealthpro.com/2014/12/02/two-thirds-of-life-insurers-use-big-data-analytics
  44. 44. Hadoop: Not a Database • Relational database keeps proprietary data, parsing and query optimizers bound • NoSQL can break this apart • Cost/GB in cloud of data lake attractive • Separating compute from data supports it Copyright 2015 Neil Raden and Hired Brains Research LLC 44

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