Avoiding the Trough: Harnessing Big Data to Drive BI Maturity

Transcription

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2 Avoiding the Trough: Harnessing Big Data to Drive BI Maturity Session 572

3 Introductions John Johansen SVP, Global Lead BI, Data & Analytics Majesco Tony Simpkins Manager, Enterprise Data Warehousing Grange Insurance Mike Ferber CIO and Director of HR ICAT Managers Jason Lichtenthal, CIO Pure Underwriters

4 Session Description / Objectives Big Data is everywhere. Do you know what you can and should you do with it? How mature is your BI strategy? How well aligned is your company s functional areas to the various BI performance stages? Do you know how to best leverage technology to get the most out of your Big Data? There are so many different aspects to consider, as well as internal factors like understanding your company culture and how they embrace new technologies that it can be overwhelming. In order to understand what Big Data can do for you, it is important gauge your BI analytics capabilities and the impact of Big Data within your organization. During this session, an overview of the BI Maturity Landscape and BI Performance Stages will be discussed to help you assess your company s maturity, as well as your company s ability to quickly respond to the ever-changing market demands for data

5 Session Description / Objectives Highlight the role analytics can play in your organization and the characteristics of the BI Maturity Landscape. Describe the different stages/levels of BI Performance and how to align the levels across different functional areas. Discuss why we are at a unique point in time for big data solutions.

6 Wait, what trough? Source: Gartner

7 BI Maturity Models: TDWI

8 BI Maturity Models: Edgewater

9 BI Maturity Models: Gartner

10 Our Favorite Model: MIT / IBM

11 Killer Applications across Functional Areas

12 Organizations need to focus on Key Skills

13 Why now? The board is talking about it Pragmatism and good expectation setting will break the fall Offerings are maturing Ecosystems are built out It doesn t cost a lot to put a toe in the water

14 What Functional Applications? Ingestion Actuarial Large less-structured data store

15 Avoiding the Trough: Harnessing Big Data to Drive BI Maturity Mike Ferber, ICAT

16 Introduction to ICAT Executive Summary ICAT is a leading underwriting manager of U.S. SME property cat risks (hurricane and earthquake) at Lloyd s. ICAT presently delivers ~$220M of profitable, cost-efficient, highly segmented and well controlled SME property business to Lloyd s and other underwriters. ICAT s Syndicate 4242 has one of the best records at Lloyd s over the past six years. ICAT can produce a greater volume of well-selected, properly priced premium than can be absorbed by s.4242 within its geographic concentration limits. ICAT has the production capacity to expand its strategic partnerships and deliver a superior portfolio of risks to selected underwriters with compatible risk-taking appetites. ICAT provides integrated, real-time analytics, underwriting and financial information to MGA clients with risk appetites that are complementary to those of s.4242 ICAT provides each MGA client with hands-on control of a custom-designed portfolio that fits its overall aggregate requirements and risk management controls

17 ICAT Overview ICAT OVERVIEW

18 Business Units Segmented business units provide targeted underwriting approach based on account size. PBU MMBU: M1 MMBU: M2 HBU Product Insured values generally below $10m. Full Limits. Average TIV $1.7m; Average premium $6k. Insured values $10m-$100m. Target full limits, offer primaries also. Average TIV $21m; Average total premium $43k. Insured values generally above $100m. Target primary layers. Homeowners product, full limits. Admitted or non-admitted depending upon market. Underwriting Philosophy ICAT s core business that has the greatest benefit of insulation from market rating cycles. This segment is more exposed to rating cycles (due to the larger insured values) and requires ICAT to selectively identify market opportunities. This segment has very high renewal retention and is less impacted by market conditions. Distribution Direct to retail and wholesale brokers. US based wholesalers only. US based retailers only. Platform Highly automated underwriting and business process. Underwriting applications are completed on-line. An automated underwriting box defines acceptable risk parameters. Eligible risks run through rating engine to deliver real time quotes. TIV flags prompt referrals to underwriters for exposure management and price control. Not automated, all accounts are underwritten by ICAT personnel. Data is collected via mandated data entry forms. All risks are modeled pre-binding. Narrowly defined and controlled underwriting box : all business conducted within intranet system for transparency and control. Highly automated underwriting and business process. Underwriting applications are completed on-line. An automated underwriting box defines acceptable risk parameters. Eligible risks run through rating engine to deliver real time quotes. TIV flags prompt referrals to underwriters for exposure management and price control.

19 Underwriting Fundamentals

20 Underwriting Fundamentals ICAT is built around five core underwriting fundamentals Risk Selection and Pricing Risk Inspection Aggregate (Exposure) Management Catastrophe Modeling Claims Management

21 Risk Selection and Pricing Collection of comprehensive information to permit robust underwriting. A complete and comprehensive set of underwriting data is required from agents and brokers to receive a quote from ICAT. Lower limit exposures (generally $10m) are underwritten via a fully automated platform and larger exposures are underwritten by ICAT s personnel using market expertise and similarly detailed pricing metrics. Catastrophe and Fire / AOP components are rated separately to maintain integrity of underwriting Catastrophe Catastrophe exposures are priced to achieve targeted expected loss ratios based on modeled annual average loss. Each MMBU policy is modeled individually prior to quoting including COPE information, coverage, and secondary characteristics for each building. Fire/AOP Fire/AOP exposures are priced to achieve a target expected loss ratio based on ISO loss costs and other industry data sources, modified for other risk characteristics of the account (fire protection, security rating, loss experience and deductibles, etc.). Risk factors used to determine risk eligibility and pricing include but are not limited to: Occupancy Age Underwriting Criteria Construction type Distance to water Roof shape and age Structural integrity and parking type Soil type and liquefaction Fire protection and security Loss history

22 Aggregate Exposure Management Aggregate management is ICAT s principle risk management tool. ICAT manages aggregate catastrophe exposures at both the regional and local level on a real-time, daily basis. Spread of risk: Blueprints Blueprints segment the US into 121 microzones that isolate homogenous risk and market environment characteristics. Business written is measured against Nationwide and Regional Blueprints to ensure sound spread of risk and to mitigate concentrations. Each microzone has a target and maximum insured limit. As limits are approached, ICAT s systems provide warning flags and access can be restricted on an almost real-time basis. Spread of risk: local concentrations ICAT controls risk concentration at the neighborhood level through the use of its Local Concentration of Risk tool. This helps to mitigate loss black spots resulting from a catastrophe event, e.g. localized exceptional quake losses or tornados spawned from hurricanes. As each building insured is assigned a limit and all buildings are geocoded, ICAT is able to monitor local risk concentrations and maintain aggregate caps for 0.25 mile and 1.0 mile radius circles throughout the country.

23 Data and Reporting At ICAT ICAT has a massive infrastructure of Policy Admin systems, claims, inspections, risk modeling, rating systems and other potential sources of data ICAT s data infrastructure has grown organically, but seeks to compress data from multiple sources into centralized data repositories ICAT leverages: Cognos Informatica ipartners Sales Force Custom SQL Reporting Excel, Excel, Excel.

24 Big Data and Predictive Analytics At ICAT The HYPE: Everyone has heard about Big Data and the promise that it will show you new correlations about your data and your business Predictive Analytics: Have yet to point out a new correlation between Risk Quality and already known Risk Characteristics (e.g., Distance to Coast, Roof Shape, Construction etc ) Data Architecture: Big Data analytics projects try to be something different from your normal requirements, architecture, build, deploy reporting projects. Our experience so far, is there isn t a big difference between Big Data projects and legacy Data Architecture projects. The WIN: ICAT has been able to utilize it s own experience in claims and CAT activity to develop our own proprietary overlay on the stock RMS models. ICAT believes that our data can be mined effectively to show correlation between our risk experience and expected damageability. ICAT has developed it s own model called IMAM (ICAT Model Adjustment Methodology) IMAM has allowed ICAT to buy more economical re-insurance and to set Blueprints with Lloyds that allow us to write more aggregate. The FUTURE: ICAT is hopeful that tools will become easier to deploy, leverage able by business users and provide richer correlations than those that currently exist.

25 Avoiding the Trough: Harnessing Big Data to Drive BI Maturity Tony Simpkins, Grange Insurance

26 Tony Simpkins Bio Started Enterprise Data Warehouse and BI Program in 2002 Oversees day-to-day activities of team to support data needs of enterprise Key member of Buckeye DAMA chapter board Lives in Columbus, OH Bachelor s degree in Business Administration Associate s degree in Computer Science

27 Grange Overview $1.3 billion annual premium volume, $2 billion assets Personal, Commercial, Life Distribute through independent agents only 13 States (Mainly Midwest and some southern states) Headquarters is located in Columbus, Ohio

28 Where my Data Journey Started Started development of the Enterprise DW in 2002 Premium and Loss Data Marts sourced from mainframe. Goals: Replace individual line of business extracts with single version of the truth Gain greater insights by having transaction level data. Make data available to non-power users through enterprise reporting tool Team: 4 person internal team Occasional external help as funding was available Team still supported other applications

29 The Road Enterprise Premium & Loss released December 2002 Continue to add new data to warehouse as time permitted. Roadmap #1 (2008) Solid Design Infrastructure needs improved Not enough business involvement Resource Limitations inhibit teams delivery Governance & Self-service concerns Reorganization #1 ( ) Business Began taking more ownership Separated Data Integration from Reporting Centralized Reporting Developed BI/DW Mission, Strategy and Guiding Principals

30 The Road Continues Roadmap #2 (2013) Shore Up Data Foundation Implement Federated Reporting Team Expand Self-Service BI Develop Enterprise Data Governance Invest in people (Proposed 15 new FTE across the enterprise) Current State Completing V2 of Premium & Loss Creating Enterprise Data Mart for Quote Analysis Implemented Federated Reporting Team with Help of Business Hired Enterprise Data Governance Manager & Governance Analyst Hired additional ETL, DBA, QA, BI and BA resources (7 total) Developing New 5 year roadmap

31 Future Roadmap Strategies Complete Committed projects (V2 Premium, Quote, Others) Ensure Adequate Support for Existing Data Assets Better Leverage Existing Data Assets Support of Transformation Projects Establish Big Data Platform Telematics Customer Sentiment Call Center Analytics) Over 80% of Executives interviewed thought we needed a Big Data Platform

32 Avoiding the Trough: Harnessing Big Data to Drive BI Maturity Jason Lichtenthal, PURE

33 About PURE Insurance

34 My Prior Data Experience

35 PURE in 2009 (~3 Years Old)

36 IT Maturity Can IT can assist with the level of data maturity within an organization?

37 Data Maturity What does it take to be mature?

38 Using Data as an Asset and for Competitive Advantage Data as an Asset Where we have invested our time Eagle Eye Analytics Buckets for segmentation and categorization Using data to help with subjective analysis Acceptable risk parameters PURE Toolset SQL Server/SSIS IBM Cognos Salesforce Eagle Eye Analytics Access/Excel Custom SQL reporting

39 Barriers to Exceptional Analytics We're facing a talent gap Resources capable of understanding and testing data Business users that understand the structure of data We spend more time gathering data than analyzing it Need appropriate tools in place to allow for self-service We don't (yet) see the value and importance of data visualization We have really smart executives that don't (yet) see that others aren't as quick at understanding numbers

40 Possibilities for the Future Build new, better, and more profitable products Support for better decision making Provide exceptional service experience to members Improve productivity/efficiency

41 Q & A

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