Advanced Business Analytics for Actuaries - Canadian Institute of Actuaries June 2013
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Presentation given at the Canadian Institute of Actuaries Annual Meeting in June 2013. Covers the direction business intelligence is moving in for insurance.
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Advanced Business Analytics for Actuaries - Canadian Institute of Actuaries June 2013
- 1. Advanced Business Analytics for Actuaries CIA Annual Meeting June 2012 Session 12 Kevin Pledge kevinpledge@gmail.com 416 949 8920
- 2. What does analytics mean to you?
- 3. What does analytics mean to you? Business Intelligence
- 4. What does analytics mean to you?
- 5. What does analytics mean to you? or something else?
- 7. Business Intelligence Business intelligence (BI) is a set of theories, methodologies, processes, architectures, and technologies that transform raw data into meaningful and useful information for business purposes. BI can handle large amounts of information to help identify and develop new opportunities. Making use of new opportunities and implementing an effective strategy can provide a competitive market advantage and long-term stability. BI technologies provide historical, current and predictive views of business operations. Common functions of business intelligence technologies are reporting, online analytical processing, analytics, data mining, process mining, complex event processing, business performance management, benchmarking, text mining, predictive analytics and prescriptive analytics. Though the term business intelligence is sometimes a synonym for competitive intelligence (because they both support decision making), BI uses technologies, processes, and applications to analyze mostly internal, structured data and business processes while competitive intelligence gathers, analyzes and disseminates information with a topical focus on company competitors. If understood broadly, business intelligence can include the subset of competitive intelligence
- 8. Business Intelligence In a nutshell… structuring your internal data for reporting
- 9. Data Warehouse Extraction, Transformation and Loading (ETL) Metadata (data about data) Online Analytical Processing (OLAP) Source Systems End User (there are other forms) Typical BI StructureTypical BI Architecture
- 10. ‘single version of the truth’
- 12. Business Intelligence you don’t know what you don’t know there may not be a single version of the truth retrospective changes messy if possible at all designed seriatim aggregations hard to keep up to date
- 13. but
- 15. Big Data Big data is a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications. The challenges include capture, curation, storage, search, sharing, transfer, analysis, and visualization. The trend to larger data sets is due to the additional information derivable from analysis of a single large set of related data, as compared to separate smaller sets with the same total amount of data, allowing correlations to be found to "spot business trends, determine quality of research, prevent diseases, link legal citations, combat crime, and determine real-time roadway traffic conditions. As of 2012, limits on the size of data sets that are feasible to process in a reasonable amount of time were on the order of exabytes of data.[ Scientists regularly encounter limitations due to large data sets in many areas, including meteorology, genomics, connectomics, complex physics simulations, and biological and environmental research. The limitations also affect Internet search, finance and business informatics. Data sets grow in size in part because they are increasingly being gathered by ubiquitous information-sensing mobile devices, aerial sensory technologies (remote sensing), software logs, cameras, microphones, radio- frequency identification readers, and wireless sensor networks. The world's technological per-capita capacity to store information has roughly doubled every 40 months since the 1980s; as of 2012, every day 2.5 quintillion (2.5×1018) bytes of data were created. The challenge for large enterprises is determining who should own big data initiatives that straddle the entire organization. Big data is difficult to work with using most relational database management systems and desktop statistics and visualization packages, requiring instead "massively parallel software running on tens, hundreds, or even thousands of servers”. What is considered "big data" varies depending on the capabilities of the organization managing the set, and on the capabilities of the applications that are traditionally used to process and analyze the data set in its domain. "For some organizations, facing hundreds of gigabytes of data for the first time may trigger a need to reconsider data management options. For others, it may take tens or hundreds of terabytes before data size becomes a significant consideration."
- 16. Big Data Unstructured Data (including external) Marketing Lingo
- 17. but Big Data is here … and it’s big US$28bn in 2012 US$34bn in 2013
- 18. it’s not really about the platform it’s about the analysis
- 19. Descriptive Title Quantitative Sophistication/Numeracy Sample Roles Data Scientist or Quantitative Analyst Advanced Math/Stat Internal expert in statistical and mathematical modelling and development, with solid business domain knowledge. Business Intelligence / Operational Analytics Good business domain, background in statistics optional Running and managing analytical models. Application of traditional methods such as experience studies. Business Intelligence/ Reporting 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” Analytic Types Types of Analysis Type V
- 20. Data Scientist Job Description • Passion for “playing” with tons of data and supporting scientific experiments to validate the performance of algorithms • Advanced degree in Statistics or related area • Experience with traditional as well as modern statistical learning techniques, including: Support Vector Machines; Regularization Techniques; Boosting, Random Forests, and other Ensemble Methods. • Strong computer science skills with high-level languages, such as R, Python, Perl, Ruby, Scala or similar scripting languages. • Experience with Hadoop and working with multi-terabyte systems. • Extensive hands on experience working with very large data sets, including statistical analyses, data visualization, data mining, and data cleansing/transformation. • Business expertise and entrepreneurial inclination to discover novel opportunities for applying analytical techniques to business/scientific problems across the company. • Good communication ability
- 21. Data Scientist Statistical skills Computer scientist Business expert Communication skills How do you train a data scientist? Neil Raden “Like Actuaries”
- 22. Is there an opportunity for actuaries here? If so what is it?
- 23. SOA initiative Working group White paper and recommendation Opportunities Statistical skills Computer skills How to deliver this
- 24. Opportunities Actuaries already have: Most the statistical skills Some computer skills Business expertise Communication skills Significant job growth in analytics is predicted Reputation for actuaries in analytics can be enhanced However… we have competition
- 25. U.S. Department of Labor occupations forecasted for growth in analytics Job Titles Expected Growth by 2018 Total # Expected Projected Median Income Top 10% Income Librarians 8% 172,400 $52,530 $81,130 Accountants/Auditors 22% 1,570,000 $59,430 $102,380 Statisticians 13% 25,500 $72,610 $117,190 Ops Research Analysts 22% 76,900 $69,000 $118,130 Management Analysts 24% 925,200 $73,570 $133,850 Actuaries 21% 23,900 $84,810 >$160,780
- 26. Opportunities Current employers/Current roles Current employers/New roles New employers/New roles
- 27. “Required” Skills/Techniques Traditional Statistical Techniques Ordinary Least Squares Logistic Regression Generalized Linear Model Time Series Methods That Group/Organize Trees/Clustering Prep for Analysis Model Validation
- 28. Advanced Business Analytics – Computer Skills Excel R SAS Matlab Statistica SPSS S-Plus Stata Emblem
- 29. How do they compare? – Social Networks Followers (from blog by Robert Muenchen)
- 30. How do they compare? – Discussion Forum Posts (from blog by Robert Muenchen)
Notas del editor
- Conceived by Dan Bricklin, refined by Bob Frankston, developed by their company Software Arts,[1] and distributed by Personal Software in 1979 (later named VisiCorp) for the Apple II computer, it propelled the Apple from being a hobbyist's toy to a useful tool for business,[3] two years before the introduction of the IBM PC.1-2-3 was released on January 26, 1983 and immediately overtook Visicalc in sales.Microsoft released the first version of Excel for the Macintosh on September 30, 1985, and the first Windows version was 2.05 (to synchronize with the Macintosh version 2.2) in November 1987. Lotus was slow to bring 1-2-3 to Windows and by 1988 Excel had started to outsell 1-2-3On May 14, IBM quietly announced the end of the road for 1-2-3, along with Lotus Organizer and the Lotus SmartSuite office suite. Lotus 1-2-3's day is done.
- Conceived by Dan Bricklin, refined by Bob Frankston, developed by their company Software Arts,[1] and distributed by Personal Software in 1979 (later named VisiCorp) for the Apple II computer, it propelled the Apple from being a hobbyist's toy to a useful tool for business,[3] two years before the introduction of the IBM PC.1-2-3 was released on January 26, 1983 and immediately overtook Visicalc in sales.Microsoft released the first version of Excel for the Macintosh on September 30, 1985, and the first Windows version was 2.05 (to synchronize with the Macintosh version 2.2) in November 1987. Lotus was slow to bring 1-2-3 to Windows and by 1988 Excel had started to outsell 1-2-3On May 14, IBM quietly announced the end of the road for 1-2-3, along with Lotus Organizer and the Lotus SmartSuite office suite. Lotus 1-2-3's day is done.
- Conceived by Dan Bricklin, refined by Bob Frankston, developed by their company Software Arts,[1] and distributed by Personal Software in 1979 (later named VisiCorp) for the Apple II computer, it propelled the Apple from being a hobbyist's toy to a useful tool for business,[3] two years before the introduction of the IBM PC.1-2-3 was released on January 26, 1983 and immediately overtook Visicalc in sales.Microsoft released the first version of Excel for the Macintosh on September 30, 1985, and the first Windows version was 2.05 (to synchronize with the Macintosh version 2.2) in November 1987. Lotus was slow to bring 1-2-3 to Windows and by 1988 Excel had started to outsell 1-2-3On May 14, IBM quietly announced the end of the road for 1-2-3, along with Lotus Organizer and the Lotus SmartSuite office suite. Lotus 1-2-3's day is done.
- Conceived by Dan Bricklin, refined by Bob Frankston, developed by their company Software Arts,[1] and distributed by Personal Software in 1979 (later named VisiCorp) for the Apple II computer, it propelled the Apple from being a hobbyist's toy to a useful tool for business,[3] two years before the introduction of the IBM PC.1-2-3 was released on January 26, 1983 and immediately overtook Visicalc in sales.Microsoft released the first version of Excel for the Macintosh on September 30, 1985, and the first Windows version was 2.05 (to synchronize with the Macintosh version 2.2) in November 1987. Lotus was slow to bring 1-2-3 to Windows and by 1988 Excel had started to outsell 1-2-3On May 14, IBM quietly announced the end of the road for 1-2-3, along with Lotus Organizer and the Lotus SmartSuite office suite. Lotus 1-2-3's day is done.
- Conceived by Dan Bricklin, refined by Bob Frankston, developed by their company Software Arts,[1] and distributed by Personal Software in 1979 (later named VisiCorp) for the Apple II computer, it propelled the Apple from being a hobbyist's toy to a useful tool for business,[3] two years before the introduction of the IBM PC.1-2-3 was released on January 26, 1983 and immediately overtook Visicalc in sales.Microsoft released the first version of Excel for the Macintosh on September 30, 1985, and the first Windows version was 2.05 (to synchronize with the Macintosh version 2.2) in November 1987. Lotus was slow to bring 1-2-3 to Windows and by 1988 Excel had started to outsell 1-2-3On May 14, IBM quietly announced the end of the road for 1-2-3, along with Lotus Organizer and the Lotus SmartSuite office suite. Lotus 1-2-3's day is done.
- Alternatives
- This is an oxymoron, but an early driver of BI was to establish a single set of data that would enable analytics.
- can’t handle stochastic modelsInflexibleand it’s still just internal data(and just reporting)
- Conceived by Dan Bricklin, refined by Bob Frankston, developed by their company Software Arts,[1] and distributed by Personal Software in 1979 (later named VisiCorp) for the Apple II computer, it propelled the Apple from being a hobbyist's toy to a useful tool for business,[3] two years before the introduction of the IBM PC.1-2-3 was released on January 26, 1983 and immediately overtook Visicalc in sales.Microsoft released the first version of Excel for the Macintosh on September 30, 1985, and the first Windows version was 2.05 (to synchronize with the Macintosh version 2.2) in November 1987. Lotus was slow to bring 1-2-3 to Windows and by 1988 Excel had started to outsell 1-2-3On May 14, IBM quietly announced the end of the road for 1-2-3, along with Lotus Organizer and the Lotus SmartSuite office suite. Lotus 1-2-3's day is done.
- Data from GartnerBig data this year will account for US$28 billion of IT spending worldwide, which will increase to US$34 billion in 2013, according to Gartner.In a report released Wednesday, the market research firm said much of 2012 expenditure will be in adapting traditional tools to address issues related to the big data phenomenon such as machine data, social data, and the large variety and velocity of data. In contrast, only US$4.3 billion will be focused on new big data functionalities.Specifically, social network and content analysis had the most impact on big data budgets, and projected to account for 45 percent of new IT spending each year, Gartner said. Application infrastructure and middleware would account for 10 percent of yearly spend.Big data is not a distinct, standalone market, said Mark Beyer, research vice president at Gartner. Rather it represents an industry-wide market force addressed in products, practices and solution delivery, he explained.In 2011, big data was the new driver in almost every category of IT spending. Through to 2018, however, big data requirements will gradually evolve from being a differentiator to "table stakes" in information management, Beyer said.Elaborating, he said by 2020 big data features and functionalities will be non-differentiating and routinely expected from traditional enterprise vendors as part of their product offerings.By the end of 2015, Gartner said it expects leading organizations to begin using their big data knowledge in "an almost embedded form in their architectures and practices". And around the start of 2018, the distinction--and advantage--new big data products had over traditional offerings that provide additional functions to handle big data, will decrease.Skills, tools and practices leading companies acquired over the years to handle big data would eventually become routine flexibility, it added.Beyer said: "Because big data's effects are pervasive, big data will evolve to become a standardized requirement in leading information architectural practices, forcing older practices and technologies into early obsolescence."In other words, big data will end up as "just data" once again by 2020, and approaches toward architecture, infrastructure, hardware, and software that do not adapt to this "new normal" will be retired, he said.
- How do they move up? Type Shifting: How Analysts can slip up to higher types and what organizations need to do to facilitate itThe go-to BI analysts are ready to move up to Type IIIType III analysts can be trained to be Type IIInternal training and mentoringExternal certification programs such as one being offered by the Society of Actuaries
- Term invented by YahooWho is this data scientist. There is some confusion over the term. Some define it as just skilled in statistics' and programming, others include ability to communicate with the business as a result of having domain expertise. For the most part, the “data scientist” will probably be more a collaborative group than an individual. Exception: actuaries, who have always been data scientists, but many rise to senior position in the business. Domain expertise a prerequisite for fellowship. The training of actuaries is a good model for data scientists. The value in big data is analytics. Because, as we said, The data doesn’t speak for itself. But lets take a closer look at analyticsUsed to be called them quantsFew and far between
- Team Members: Lisa Tourville (Chair) Joan Barrett Guillaume Briere-Giroux Jack Bruner Kara Clark Ian Duncan Kim DwornickAlice Kroll John Lloyd David McleroyKevin Pledge Jacques RiouxChris Stehno
- Significant job growth in analytics is predicted, including management and leadership roles.
- Meg – why we see opportunities for actuaries…The US Department of Labor predicts healthy growth in occupations that work in analytics. In addition, McKinsey (May 2011) predicts 1.5 million managers and advanced analysts needed by 2018. A recent review of open actuarial positions with a recruiter showed 15% of all openings had a preference for job seekers with proficiencies in one or more aspects of advanced business analytics, most often predictive modeling.But a survey (Fall 2011) of 55 life insurance companies by the SOA indicated life insurance companies were not using predictive modeling techniques in any widespread way but planned to in the near future…40% considering using predictive modeling to enhance sales and marketing50% considering using for underwriting1 company currently using predictive modeling for claimsKey Points:Actuaries are included in the job titles associated with job growth in analytics. There are many job titles associated with this growth (IT jobs, too).According to this chart, actuaries tend to be more highly compensated than our fellow professionals. Our stories from actual hiring managers conflict on this. Some employers pay non actuaries at the same rate. Some employers say they are hiring economists and “ninja librarians” because they cost less.
- Actuaries as leaders, not the people running the models.
- Many other skills and techniques were identified as part of our study but these made the top of the list. (Others – Survival/failure Time Analysis, Factor Analysis,
- Technical sites like stack overflow
- R is a free software environment for statistical computing and graphics. It compiles and runs on a wide variety of UNIX platforms, Windows and MacOS.