big data and role of information professional

SLA – IEBD Webcast

Big Data and Role of the Information

Professional

Pat Saporito, CPCU, Sr. Director, Global COE for Analytics

[email protected] Twitter: @patsaporito

mailto:[email protected]

© 2012 SAP AG. All rights reserved. 2 Confidential

Agenda

Big Data & Disruption

Potential Business Value & Challenges

Emerging Roles & Stakeholders

Culture & Change Management

Information Professionals Role


Internet of Things is disrupting all industries

1 billion Facebook

users

4 billion YouTube views

per day

Data

doubles every 18

months

15

billion web-enabled

devices

5 billion in emerging

middle class


Drive Better

Profit Margins

New

Strategies and

Business Models

Operational

Efficiencies

Value

Velocity

Volume Variety

Mobile

CRM Data

Planning

Opportunities Transactions

Customer

Sales Order

Things

Instant Messages

Demand

Inventory

Big Data Matters: 5 Vs Potential to Provide Transformational Business Value

Veracity


Data (Big & Small) - Its Uses

1:1 Marketing

Amazon.com,

Gamification/ Game Design

Angry Birds, CastleVille

Group/Collective Buying

• Groupon, Living Social

Social Networking

• Facebook, Linked In

Inbound Marketing

Retargeting, remarketing

Travelocity

Location Based Marketing

• Google Places

Video

• YouTube, Hulu

User Generated Content

• Facebook, Linked In, Twitter

Mobile Technologies

• Smartphones, iPads/tablets

http://www.google.com/url?sa=t&rct=j&q=youtube&source=web&cd=&cad=rja&sqi=2&ved=0CHoQoRBQAQ&url=https://plus.google.com/115229808208707341778?socpid=1&socfid=web:infocard:default:image&ei=JHWeUPmADc-C0QG0vID4Bw&usg=AFQjCNE1odz61EeE0_ZjFLUYKOBaoswzKg

http://en.wikipedia.org/wiki/File:Amazon.com-Logo.svg

http://www.google.com/url?sa=t&rct=j&q=linkedin&source=web&cd=&cad=rja&sqi=2&ved=0CIcBEKEQUAE&url=https://plus.google.com/107350354213838732087?socpid=1&socfid=web:infocard:default:image&ei=CnaeULGtB8iB0QHs7oHgAg&usg=AFQjCNFzgGthuO_MAIIcfikYdztQb0i1vw

http://www.google.com/aclk?sa=L&ai=CsqslP3aeUI_dF6f76QHxk4DoBKaR8a8CjuiMij--h5fGogEIBRABIJmo2R4oBVDGssmmAmDJ_p6M0KTkD8gBB6oEJk_QgIfFe2MncVusvtM42-NeSZp8OqxxKlPTYXBjgGpxNBFIm6n1gAWQTroFEwjbgb3-3sSzAhWJcDQKHWsBAHPABQXKBQCgBibgEr3XoPTcrJOGeg&ei=P3aeUJvmEYnh0QHrgoCYBw&sig=AOD64_1nah2Z_vmnodOrSCJk86bPjE98OQ&ctype=5&sqi=2&ved=0CD8Qwg8&adurl=http://clk.atdmt.com/CNT/go/410971126/direct/01/?cagpspn=pla

http://reviews.cnet.com/ipad/


All that Glitters is not Gold

All data is not equal

• Validate data sources

• Validate its fit for purpose

• ID alternative data sources


Selected 3rd Party Data Categories & Sources

Sources: • Acxiom

• AM Best

• AMA

• American Housing Survey

• American Tort Reform Foundation

• Bureau of Labor Statistics

• Cap Index

• Carfax

• Census Point

• Choicepoint

• Corporate Research Board

• Directory of US Hospitals

• Dun & Bradstreet

• EASI Analytics

• Equifax

• ESRI

• Experian

• Insurance Institute for Highway Safety

• Internal Revenue Service

• State Licensing Data (Attys, CPAs, MDs,

etc.)

• Martindale/Hubble Attorney Listing

• MRI Purchasing Propensities

• NFIRS – National Fire Reporting

• NHTSA

• OSHA

• US Census

• US Geological Surveys

• Warranties

Categories


The Mother Load – Data.Gov http://catalog.data.gov

134,000 data sets


Organizations need to mature their analytics to attain

business value

Raw

Data

Cleaned

Data

Standard

Reports

Ad Hoc

Reports &

OLAP

Agile

Visualization

Predictive

Modeling

Optimization

What happened?

Why did it happen?

What will happen?

What is

the best that

could happen?

Us

er

En

ga

ge

me

nt

Maturity of Analytics Capabilities

Self Service BI

Generic

Predictive Analysis

Co

lle

cti

ve

In

sig

ht


Turning new signals into business value

Proactive

Health/Wellness

& Risk

Management

360O Customer View

360O Provider View

Extraordinary

Policy/Contract &

Claims Service

Customer and

Producer

Sentiment

Underwriting &

Pricing,

Real Time

Predictive Risk

Management

Fraud

Detection, Real

Time

Telematics /

Usage Based

Insurance

Insider

Threats

Risk Mitigation,

Real-time

Asset Optimization Distribution

Management

:-)


“World Class Analytics”

Often Described, Rarely Achieved


Big Data Challenges

Staffing

and

Skills

Data Quality/

Governance

Cost

Uncertainty

on Value of

Big Data

Tools &

Technogies

Connecting people to

information, and applying

analytics


Nucleus Research, Gartner, Fortune Magazine

Analytic Use Will Skyrocket: 2020 vs. 2014… yet, we’re not using the data we already have

10%

75%

Use Analytics

Today

Need

Analytics

by 2020

Ability to manage and

consume all data is

getting harder

Not utilizing all

the information

out there

Bottom Line: Not leveraging the

power of “collective insight”

Missing new

insights

IT is not agile enough

and the business wants

to get involved

=


Many New Titles & Roles

Data Diva

Data Savant

Data Super Hero

Chief Analytics Officer

Analytics

Cave Man

Not everyone is a Data

Scientist… but more

people need analytics in

their jobs.


Analytic Value Chain

Many Different Types of Users


How Information Professionals Can Help

• Content: Identify external data sources

• Information Governance: Validate data quality, suitability

• Access: Help develop text mining taxonomies

• Tools: Help evaluate tools

• Research: Develop a bibliography on analytics, big data, analytic

leaders, analytic competitors, analytics educational programs.

• Advocacy: Work with BI Competency Centers.


Insurance Analytics Evolution

Where are you today? Where do you want to be?

Pricing & Underwriting

Traditional Class Rated

Portfolio Analysis Household Analysis, Tier Rating Plans

Risk Based Pricing, Ad-hoc or On Demand Rate Reviews

Data Poor Quality, Silo’d, Inaccessible Data

Data Assembled Across Product Lines/Historical

Consistent Enterprise View Knowledge/ Data Mining

Atomic Detail Data Wisdom/ Predictive

Product Development

One Product Fits All

Unbundled Coverages Cafeteria/ Menu Approach

Customer & Profitability Driven

Marketing

Product Value Customer Segment Value

Customer Lifetime Value

Dynamic Value Management

Accounting & Finance

Unit focused claims mgmt.

Integrated, but reactive claims mgmt.

Driver based historical claims mgmt.

Driver based predictive claims mgmt.

Metrics Silo’d, Functional, Lagging Metrics

SBU-Strategic Objective linked, historical drivers

Strategic & Cross-SBU objective linked, predictive drivers

Integrated predictive models & metrics

Claims

Traditional Planning & Budgeting

Driver Based Planning & Budgeting

Integrated Planning Predictive Planning

Less Advanced More Advanced


Information Culture

Connecting People to Data

Use information as a strategic

asset in decisions

Build and tell fact-based

stories

Maximize business

performance with effective use

of information (apply the

analytics)

“

”

The stone age was

marked by man's

clever use of crude

tools; the

information age, to

date, has been

marked by man's

crude use of clever

tools.

Anon


Practical Guidance

Applied Insurance Analytics

Free download of Chapter 1 (Overview)

http://www.ftpress.com/content/images/9780133760361/samplepages/9780133760361.pdf


Free desktop visualization tool

SAP Lumira http://saplumira.com/


Be ready for

continuous

disruption

Create an

Information

driven

culture

Intelligence

and Analytics

are universal,

"Big Data“

isn't

We all emit

data, lots of it!

Data needs to

be front and

Center, no

matter how

big or small

Analytics is at the core of an intelligent business

Shifting to an Enterprise Analytics Mindset

Analytics is not just for power users - it's for everyone


Next Steps

• Volunteer for Analytics projects

• Expand your peer network especially with:

• Chief Analytics Officer; let them know your value-add

• BI Competency Center

• Enlarge your user base

• Role: Data scientists

• Function: Actuarial, marketing, claims,

• Expand your skills

• Learn about big data

• Try new tools – especially new visualization and text mining tools

• Lead by Example

• Use infographics in fulfilling/presenting info requests


Plan

Explore

Enrich Explain

Monitor

Design

Govern DATA

PEOPLE

Analyst

IT

Developer

Decision

Maker

Visualize Exploration &

Visualization

Predict Advanced

Analytics

Operational Strategic

Become a Trusted Data Advisor Help incorporate analytics into your company’s DNA

Engage Enterprise-

wide BI

Act

Data

Advisor

Thank You!

Pat Saporito, CPCU

Sr. Director, BI

Global COE for Analytics

[email protected]

(201) 681-9671

Twitter: @Pat.Saporito

LinkedIn: www.linkedin/in/patriciasaporito

SAP Collaboration Network

http://scn.sap.com/

SAP Decision Factor Blog

http://www.the-decisionfactor.com/home/

mailto:[email protected]

http://www.linkedin/in/patriciasaporito

http://scn.sap.com/

http://scn.sap.com/

https://twitter.com/PatSaporito

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





Analytics Bibliography: Books

Analytics at Work: Smarter Decisions, Better Results. Thomas H. Davenport, Jeanne G. Harris, Robert

Morison. Harvard Business School Publishing. 2010.

Applied Insurance Analytics: A Framework for Driving More Value from Data Assets, Technologies

and Tools. Patricia Saporito. Pearson FT Press, 2014.

Big Data: A Revolution That Will Transform How we Live, Work and Think. Viktor Mayer-Schönberger and

Kenneth Cukier. Houghton Mifflin Harcourt, 2013.

Big Data@Work. Dispelling the Myths, Uncovering the Opportunities. Tom Davenport. Harvard Business

School Publishing, 2014.

Business Intelligence in Plain Language: A practical guide to Data Mining and Business Analytics. Jeremy

Kolb. Applied Data Labs, Inc. 2012.

Business Intelligence Competency Centers: A Team Approach to Maximizing Competitive Advantage.

Gloria J. Miller, Stephanie V. Gerlach and Dagmar Brautigam. John A. Wiley & Sons. 2006

Mining the Talk: Unlocking the Business Value in Unstructured Information. Scott Spangler and Jeffrey

Kreulen. IBM Press/Pearson, plc. 2008.

Predictive Analytics: The Power to Predict who Will Click, Buy, Lie or Die. Eric Siegel. John Wiley & Sons.

2013.

The Visual Display of Quantitative Information. Edward Tufte. 2001. (A classic reference work; the original

“bible” of visualization. Also see: Envisioning Information and Visual Explanations, by Tufte.


Analytics Bibliography: Trade & Professional Assns.

International Institute for Analytics (IIA). www.iianalytics.com

An independent research firm co-founded Jack Phillips and Research Director Thomas H.

Davenport. Works with organizations to build strong and competitive analytics programs.

INFORMS (Institute for Operations Research & Management Sciences)

www.informs.org

Professional organization for cross industry operations research and management

professionals. Sponsors the CAP (Certified Analytic Professional) professional designation.

TDWI (The Data Warehouse Institute) www.tdwi.org

A leading educational and research organization for BI and Data Warehousing. TDWI

produces an annual BI Benchmark Report.

http://www.iianalytics.com/

http://www.informs.org/

http://www.tdwi.org/


Analytics Bibliography: Articles, Studies, White Papers

Benchmarking Analytic Talent. Talent Analytics Corp. December 2012. A research study on analytics

professionals.

Big Data: The next frontier for innovation, competition, and productivity. May 2011. McKinsey

Research Institute. One of the key studies on Big Data.

Business Intelligence and Performance Management; Key Initiative Overview. Gartner Group. 2013.

(Research Brief)

Data and Analytics in Insurance: P&C Insurer Strategic Priorities and Operational Plans for 2014 and

Beyond. Mark Breading and Denise Garth. June 2014. Strategy Meets Action.

The Data-Driven Organization. Marcia W. Blenko, Michael C. Mankins, Paul Rogers. Harvard

Business Review. June 2010.

Disruptive Technologies: Advances that will transform life, business, and the global economy. May

2013. McKinsey Research Institute. Insights into Machine to Machine (M2M), Internet of Things (IoT),

and other technologies.

The way forward. Insurance in an age of customer intimacy and Internet of Things. Economist

Intelligence Unit; sponsored by SAP. June 2014. Global survey of P&C and Life insurance executives

on the future of insurance. Key findings include important role of data and analytics.

big data and role of information professional

Data & Analytics