analytics for actuaries cia
DESCRIPTION
Presentation given on Business Analytics for Actuaries at the Canadian Institute of Actuaries Annual Meeting June 2013TRANSCRIPT
Advanced Business Analytics for Actuaries
CIA Annual Meeting June 2013Session 12
Kevin [email protected] 949 8920
What does analytics mean to you?
What does analytics mean to you?
Business Intelligence
BI
What does analytics mean to you?
What does analytics mean to you?
or something else?
Business Intelligence
BI
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
Business Intelligence
In a nutshell…
structuring your internal datafor reporting
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
‘single version of the truth’
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
but
Big DataBig 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."
Big Data
Unstructured Data (including external)
Marketing Lingo
but Big Data is here
… and it’s big
US$28bn in 2012US$34bn in 2013
it’s not really about the platform
it’s about the analysis
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/ ReportingData 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
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
Data Scientist
Statistical skillsComputer scientistBusiness expertCommunication skills
How do you train a data scientist?
Neil Raden“Like Actuaries”
Is there an opportunity for actuaries here?
If so what is it?
SOA initiative
Working group
White paper and recommendation
Opportunities
Statistical skills
Computer skills
How to deliver this
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
U.S. Department of Laboroccupations 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
Opportunities
Current employers/Current roles
Current employers/New roles
New employers/New roles
“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
Advanced Business Analytics – Computer Skills
Excel R SASMatlabStatisticaSPSSS-PlusStataEmblem
How do they compare? – Social Networks Followers(from blog by Robert Muenchen)
How do they compare? – Discussion Forum Posts(from blog by Robert Muenchen)
r-project.orgcoursera.org