2015 Society of Actuaries Life/Annuity Symposium Presentation

What’s the Big Deal about Big Data…for Actuaries?

Neil Raden

Founder, Hired Brains ResearchTwitter: @NeilRaden

Blog: http://hiredbrains.wordpress.com

Website: http://www.hiredbrains.com

Mail: [email protected]

LinkedIn: http://www.linkedin.com/in/neilraden

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 atInformationWeek, 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: [email protected] USERNAME: @neilradenLINKEDIN PROFILE: http://www.linkedin.com/in/neilraden

Copyright 2015 Neil Raden and Hired Brains Research LLC

2

http://www.informationweek.com/authors/6567

http://hiredbrains.wordpress.com

mailto:[email protected]

http://www.linkedin.com/in/neilraden

Willie Sutton: Infamous Bank RobberQ: Willie, why do you rob banks?

A: Because that’s where the money is

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


Big Is RelativeThis Pace Isn’t New, Just Magnitude


5

Though Volume is interesting, it isn’t what distinguishes Big Data

Moore’s Law


6

Different Way to Visualize It


7

No More Managing from Scarcity


8

Data Warehouse and HadoopData 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


9

Even Big Data Doesn’t Speak for Itself


10

• Incomplete

• Behaviors under-

represented

• Anonymizing

disasters

• Single source of

data inadequate

• Harmonization

Not a crystal ball

How Operational Intelligence Expands Current Technology


11

Compare This with a Hadoop


12

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


13

Decisions: A Miracle Happens?


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.

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

Do You Know This Number?


16

2.718281828459...

Why is this important

Euler Gave Us the Tools


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

But Euler Got One Thing Wrong


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”

Euler Not a Data Scientist


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

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


20

Why Does This Matter?


21

Because Data Science is not the realm of the most brilliantmathematicians

It’s for people who know how to do it and who have the correct training and tools to do it themselves

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


22


23

Chief Actuary of GeoSpatial Analytics and ModelingChief Analytic OfficerChief Analytics & Algorithms OfficerChief Analytics OfficerChief Credit & Analytics OfficerChief Data and Analytics OfficerChief Research & Analytics OfficerChief Scientist, Global Head of AnalyticsChief Scientist, VP of AnalyticsChief Technology Officer, Enterprise Information Management & AnalyticsClient Director, Business AnalyticsDirector - Advanced AnalyticsDirector - Analytic ScienceDirector – Analytics DeliveryDirector - BI & AnalyticsDirector - Fraud Analytics & R&DDirector - Predictive AnalyticsDirector (Analytics and Creative Strategy)Director (Marketing Analytics)Director : Digital AnalyticsDirector Analytics Strategy, JMPDirector Marketing AnalyticsDirector of Advanced AnalyticsDirector of Analytic Consulting, Product/Data Loyalty AnalyticsDirector of Analytic SolutionsDirector of AnalyticsDirector of Analytics (consultant)Director of Data Analytics and Advertising PlatformsDirector of Digital Analytics and Customer InsightDirector of Health AnalyticsDirector of Innovation, Big Data AnalyticsDirector of Product, AnalyticsDirector of Risk Analytics and PolicyDirector of Science & Analytics for Enterprise Marketing Management (EMM)Director of Web Analytics and OptimizationDirector, Advanced AnalyticsDirector, Advanced Analytics, HumanaOneDirector, Advanced Strategic Analytics

Director, Analytic ScienceDirector, Analytic StrategyDirector, Analytical ServicesDirector, AnalyticsDirector, Big Data Analytics and SegmentationDirector, Business AnalyticsDirector, Business Analytics & Decision Management StrategyDirector, Business Intelligence & Analytics, PogoDirector, Business Intelligence and AnalyticsDirector, Business Planning & AnalyticsDirector, Center for Business Analytics, Stern School of BusinessDirector, Clinical AnalyticsDirector, Customer AnalyticsDirector, Customer Analytics & PricingDirector, Customer Insights and Business AnalyticsDirector, Data AnalyticsDirector, Data Science & Analytics PracticeDirector, Data Warehousing & AnalyticsDirector, Database Marketing & Analytics (Marketing)Director, DVD BI and AnalyticsDirector, Gamification Analytics Platform, Information Analytics & InnovationDirector, Global Digital Marketing AnalyticsDirector, Group AnalyticsDirector, Head of Forensic Data AnalyticsDirector, Marketing AnalyticsDirector, Marketing Analytics for Bing Product GroupDirector, Oracle Database Advanced AnalyticsDirector, Predictive Analytic ApplicationsDirector, Reporting/AnalyticsDirector, Risk & AnalyticsDirector, Risk and Business AnalyticsDirector, Statistical Modeling and AnalyticsDirector, Statistics and Project Analytics / Senior Analytic ConsultantDirector, Strategic AnalyticsDirector, Web AnalyticsDirector/Head of AnalyticsDirector/Principal, Analytics

This Is Getting Ridiculous

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?


24

Stat Tools Can Be Dangerous


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

Anscombe’s Quartet


26

Spurious Correlation


27

Texas Sharpshooter Fallacy


28

• It’s not just about knowing and using quantitative models

• You have to understand the meaning of the data


29

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

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.

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


32

33


33

Types of AnalyticsData Mining

X

X

X

X

X

X

X X

X

X

X

X

X

X

X

XX

X

X

X

X

X

XX X

X

X X

X

XX

X

X

X

X

X

X

X

X

X

X

X

XX X X

X X

X

XX

X

X

X

X

X X

X

X

X

X

XX

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

Impact May Take Time to Play Out

Types of Analysis and RolesDescriptive Title Quantitative

Sophistication/NumeracySample 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”


35

Analytic Types

Types of AnalysisDescriptive Title Quantitative

Sophistication/NumeracySample 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”


36

Analytic Types

Type VBetter BI/Viz/Disco

Training/Mentoring/Apps

Training/Mentoring/Apps

3rd Party Services

Type Shifting

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%


37

70% is not Data Scientist work

Type Shifting


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

Analytics is hard and takes resourcesAnalytics 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.


39

Analytics Is Hard

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


40

Stock Market Returns for the “Competing on Analytics” Cohort

-80%

-40%

0%

40%

80%

120%

Am

azo

n

Mar

rio

tt

Ho

nd

a

Inte

l

No

vart

is

Wal

-Ma

rt

UP

S

Ve

rizo

n

P &

G

Pro

gre

ssiv

e

Cap

ital

On

e

Yah

oo

De

ll

Bar

clay

s

Average Stock Market Return

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


41

Thank You


42

Neil Raden

Founder, Hired Brains ResearchTwitter: NeilRaden

Blog: http://hiredbrains.wordpress.com

Website: http://www.hiredbrains.com

Mail: [email protected]

LinkedIn: http://www.linkedin.com/in/neilraden

Apparently Life Insurance Is Ahead of All Other Industries in Big Data*


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

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


44

Data & Analytics

2015 Society of Actuaries Life/Annuity Symposium Presentation