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The Casualty ty Actu tuarial Society ty is committe tted to to adhering str trictl tly to to th the lette tter } and spirit t of th the anti titr trust t laws. Seminars conducte ted under th the auspices of th the CAS are designed solely to to provide a forum for th the expression of various points ts of view on to topics described in th the programs or agendas for such meeti tings. Under no circumsta tances shall CAS seminars be used as a means for competi ting } companies or firms to to reach any understa tanding – – expressed or implied – – th that t restr tricts ts competi titi tion or in any way impairs th the ability ty of members to to exercise independent t business judgment t regarding matte tters affecti ting competi titi tion. It t is th the responsibility ty of all seminar parti ticipants ts to to be aware of anti titr trust t } regulati tions, to to prevent t any writte tten or verbal discussions th that t appear to to violate te th these laws, and to to adhere in every respect t to to th the CAS anti titr trust t compliance policy. policy.
} Background Information } Data } Uses } Strategies and Plans } Summary } Open Discussion/Questions 3
} Art Cadorine, ISO } Pete Marotta, ISO } Tracy Spadola, Teradata 4
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1922: NY Y law requires insurers to to file premiums 1343: first formal policy written in Italy } } and loss experience in conformance with th approved 1494: double entry bookkeeping established } classificati tions 1726: Sun Fire Office established } 1923: NY Y requires Casualty ty Ex Experience Ex Exhibit t } 1736: Union Fire (Ben Franklin) established } 1944: South East Underwriters case } 1779: standard marine policy established } 1945: McCarran Ferguson enacte ted by Congress } 1792: states started to impose limitations on company } 1948: sta tate tes pass regulati tions/laws regarding } activities and investments sta tati tisti tical plans, rate tes and rules 1824: NY established a tax on premiums } 1949: Insurance Ex Expense Ex Exhibit t intr troduced } 1828: Annual Sta tate tement t concept t is create ted in NY Y with th 1950: NAIC adopts ts multi ti-line blank } } 13 cate tegories of questi tions 1967: ACORD formed to create standardized } 1850: A & H coverage in US } operational forms 1851: states start insurance company examinations 1969: Schedule P changed to calendar/accident } } 1853: NY Y annual report t expanded requires more data ta year basis } 1971: ISO formed from several national insurance 1871: Lloyds established } } service organizations 1871: Nati tional Conventi tion of Insurance Commissioners } (NCIC) – – fire and marine blank 1983: Insurance Value Added Network (IVANS); } first batch processing via IVANS 1873: MA adopts first standard fire policy } 1995: EU directive on data protection 1898: auto liability coverage } } 1996: HIPAA and FCRA passed by Congress 1899: auto collision coverage } } 1996: Solvency II in EU } 1902: auto property damage coverage } 1966: Graham-Leach-Bliley passed by Congress } 1911: first workers ’ comp policy } 2002: Sarbanes-Oxley passed by Congress } 1911: NCIC model reserve law } 2003: CA data breach law } 1922: Nati tional Council on Compensati tion Insurance } esta tablished 6
} Michael Lesk ( Network World , October 28, 2003)– ◦ including sounds and images there are thousands of petabytes of information ◦ T.K. Landauer – “ How much do people remember? ” , Cognitive Science , Oct/Dec 1986: the human brain holds 200 MB of information 7
1,000 bytes OR 103bytes Kilobyte (KB) 2 Kilobytes: A Typewritten page. 100 Kilobytes: A low-resolution photograph. 1,000,000 bytes OR 10 6 bytes Megabyte (MB) 1 Megabyte: A small novel OR a 3.5 inch floppy disk. 2 Megabytes: A high-resolution photograph. 5 Megabytes: The complete works of Shakespeare. 10 Megabytes: A minute of high-fidelity sound. 100 Megabytes: 1 meter of shelved books. 500 Megabytes: A CD-ROM. 1,000,000,000 bytes OR 10 9 bytes Gigabyte (GB) 1 Gigabyte: a pickup truck filled with books. 20 Gigabytes: A good collection of the works of Beethoven. 100 Gigabytes: A library floor of academic journals. 1,000,000,000,000 bytes OR 10 12 bytes Terabyte (TB) 1 Terabyte: 50000 trees made into paper and printed. 2 Terabytes: An academic research library. 10 Terabytes: The print collections of the U.S. Library of Congress. 400 Terabytes: National Climactic Data Center (NOAA) database. 1,000,000,000,000,000 bytes OR 10 15 bytes Petabyte (PB) 1 Petabyte: 3 years of EOS data (2001). 2 Petabytes: All U.S. academic research libraries. 20 Petabytes: Production of hard-disk drives in 1995. 200 Petabytes: All printed material. 1,000,000,000,000,000,000 bytes OR 10 18 bytes Exabyte (EB) 2 Exabytes: Total volume of information generated in 1999. 5 Exabytes: All words ever spoken by human beings. 1,000 EBs Zettabyte (ZB) 8
} University of California, San Diego ◦ June 2008 announced a 3 year study to quantify the amounts and kinds of information being produced worldwide ◦ The “ How Much Information? ” study will be completed by a multi-disciplinary, multi- university faculty team supported by corporate and foundation sponsorship ◦ http://giic.ucsd.edu 9
} University of California, Berkeley ◦ “ How Much Information 2003? ” , senior researchers Peter Lyman and Hal R. Varian ◦ Print, film, magnetic and optical: 5 exabytes in 2002 92% on magnetic media Doubles every 3 years/quadruples in 2 years ◦ Telephone, radio, TV, Internet: 18 exabytes in 2002 ◦ http://www2.sims.berkeley.edu/research/projects/how- much-info-2003 / 10
1999- 1999-2000 2000 % C % Chan ange e Sto torage 2002 2002 2002 2002 1999- 1999-200 200 Lower Low er Up Upper er Medium Mediu Terabyte tes Terabyte tes 0 Upper 0 Upper Esti Es timate te Esti Es timate tes Upper Up er Low Lower er Esti Es timate te Esti Es timate te Es Esti timate te Paper 1,634 327 1,200 240 36% Film 420,254 76,69 431,690 58,209 -3% Magnetic 5187130 3,416,230 2,779,760 2,073,760 87% Optical 103 51 81 29 28% TOTAL: 5,609,121 3,416,281 3,212,731 2,132,238 74.5% 11
Mediu Medium 2002 Terabyte tes Radio 3,488 Television 68,955 Telephone 17,300,000 Internet 532,897 TOTAL 17,905,340 12
How much data ta is in your organizati tion – – and how has th this changed from th the 20th th to to th the 21st t centu tury? Is th there any one single area th that t eith ther oversees data ta at t th the ente terprise level or is charged with th coordinati ting and documenti ting data ta acti tiviti ties ente terprise-wide? 13
From insurance data managers to enterprise data managers, managing many types of data including insurance data 14
Where in th the organizati tion should th the data ta management t functi tion be housed? Do Do you see th this changing in th the 21st t centu tury? 15
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} Climate change (NAIC survey) } Premium leakage } Fraud } Loss control } Predictive models } ERM 17
What t new problems will data ta managers be asked to to address? 18
} Optical/graphical: ◦ Satellite imagery, maps, photos ◦ Video: risk assessment, claims handling } Voice: call centers, scripts, fraud detection } Cellular/telephonic: ◦ Embedding cellular connectivity ◦ RFIDs ◦ Telematics, EDRs ◦ Claims reporting via cell phone apps ◦ Loss control } Internet ◦ For standard insurance functions ◦ Social media: communication and mining 19
} Privacy: in sync with Chief Privacy Officer } C-level: in sync with business strategies } Quality ◦ Broader application to new uses of data, ◦ More emphasis on external and an enterprise view } Standards implementation 20
As data ta privacy and confidenti tiality ty receive broader mandate tes – – what t role is th the data ta manager playing in regards to to prote tecte ted data ta? And what t roles should/will data ta ma mana nage ger ’ s be playing in th the futu ture? 21
} GIS } GPS } Traffic } Weather } Health/Medical } Pharma } Risk components 22
The granularity ty of data ta – – desired and available - has exploded in th the past t few decades decades. How has th this explosion impacte ted data ta management? t? Again what t is th the impact t on data ta management t best t practi tices? 23
Wh What at ’ s next t in data ta? 24
} Metadata repositories, data dictionaries, MDM, } ETL } Data profiling, audits and controls } Data and text mining } Entity resolution } Visualization } Longitudinal functionality } Encryption 25
What t new to tools will data ta managers be u be usin ing? 26
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