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?

6.
Business Intelligence

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’

11.
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

12.
but

13.
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."

14.
Big Data
Unstructured Data (including external)
Marketing Lingo

15.
but Big Data is here
… and it’s big
US$28bn in 2012
US$34bn in 2013

16.
it’s not really about the platform
it’s about the analysis

17.
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

18.
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

19.
Data Scientist
Statistical skills
Computer scientist
Business expert
Communication skills
How do you train a data scientist?
Neil Raden
“Like Actuaries”

20.
Is there an opportunity for actuaries here?
If so what is it?

21.
SOA initiative
Working group
White paper and
recommendation
Opportunities
Statistical skills
Computer skills
How to deliver this

22.
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

23.
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

24.
Opportunities
Current employers/Current roles
Current employers/New roles
New employers/New roles

25.
“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

26.
Advanced Business Analytics – Computer Skills
Excel R
SAS
Matlab
Statistica
SPSS
S-Plus
Stata
Emblem

27.
How do they compare? – Social Networks Followers
(from blog by Robert Muenchen)

28.
How do they compare? – Discussion Forum Posts
(from blog by Robert Muenchen)

29.
r-project.org
coursera.org