Demystifying business intelligence

CEO and Co-Founder, Insight Decision Solutions Specializes in BI for insurance and has overseen many BI projects Used BI for RPEC mortality study Former Chair of the Technology Section Speaks without an accent

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BI system installed in-house Background Case studies BI as a business project

Actuarial role and benefits from BI

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Typical reasons given for BI: Consolidate Data Make information accessible Enhance with analytics Integration Enterprise view of business

Concerns: Security Breadth of users Project risk and cost

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Benefits often intangible

Against: Reporting available from other

systems Local data extracts available Would create additional version

of data Users comfortable with existing

tools

For: Integration of data Operational data not structured

for analysis Maximize potential from

existing systems

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By definition the Insurance Industry is one of the largest users of data

Early failures Notable successes BI in other industries

1997 – IBM, Oracle, Microsoft launch BI products

1990 – Cognos launch first desktop BI tool

Niche vendors, fragmented tool sets

Emergence of mainstream, web based products

Data Warehouse / OLAP Server

Presentation Server

UsersMetadata

Data Store

Integrated Systemse.g. valuation system

ETL

ETL

Source systems

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What triggers projects? Challenges and lessons learnt

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Senior management want better information Actuaries needed improved analytics Improved data management

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CEO / Board requests Format suitable for (non-technical) audience Verifiable and integrated – need to get everyone on the same page Need to be able to answers questions not yet anticipated Budget sometime easier to obtain

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Improved Financial Reporting• GL drill-down by policy and product dimensions

• Integrated financial planning

• Source of Earnings

Experience studies

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Valuation extract management• Valuation data transformations – consistent

• Reproducibility of extracts

Shared data source

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Health Insurer writes $1bn premium annually Rate increases subject to regulator approval may be 6% p.a. Value of accelerating approval by one month Additional Revenue = $1bn x 0.06 x 1/12 = $5m p.a.

(modifications: not all business receives increase, may not be 6%, one time catch up, etc)

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Opportunity spotted by COO• Loss ratio by agent

• Not credible, but effective

Handling success – report explosion, staff role change Concern of data availability to all areas

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Personal Information, access to sensitive financial information

• Modify query based on login credentials

• Design in structure of DBs to restrict access Laptops / transportable data

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Many non-tangible benefits discovered during the project Would have done differently:

• Definitions / less pre-planning

• Accessibility

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Business functions cannot be an after thought Success does not come from a data mapping exercise Business leadership is critical for success Application model needs to be designed upfront

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Unique features of insurance need to be recognized in the design• Sale is the start of a relationship

• Data is complex Life Health – temporal, P&C large number of attributes

Goal of moving logic upstream• Reduces work

• Avoid inconsistencies

• Proves system

Data Warehouse / OLAP Server

Presentation Server

UsersMetadata

Data Store

Integrated Systemse.g. valuation system

ETL

ETL

Source systems

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Goal Move application logic into the system managed by metadata

Policy Fact Table

Extract Date (FK)Product Code (FK)Jurisdiction (FK)Policy IdDate of BirthIssue AgeIssue Date (FK)Sum AssuredAnnual PremiumReserve

Time Dimension (1)

Extract Date (PK)YearQuarterMonth

Product Code Dimension

Product Code (PK)Product NameProduct DescriptionProduct TypeProduct FundProduct Group

Jurisdiction Dimension

Jurisdiction (PK)State or provinceSales AreaCountry

Time Dimension (2)

Issue Date (PK)Issue Year BandYearQuarterMonth

Policy Fact Table

Extract Date (FK)Product Code (FK)Jurisdiction (FK)Policy IdDate of BirthIssue AgeIssue Date (FK)Sum AssuredAnnual PremiumReserve

Time Dimension (1)

Extract Date (PK)YearQuarterMonth

Product Code Dimension

Product Code (PK)Product NameProduct DescriptionProduct Type (FK)

Jurisdiction Dimension

Jurisdiction (PK)State or provinceSales AreaCountry

Time Dimension (2)

Issue Date (PK)Issue Year bandYearQuarterMonth

Product Type Dimension

Product Type (PK)Product FundProduct Group

Need to embed calculated functions Need to accommodate unique insurance features

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Don’t be intimidated by jargon• OLAP (online Analytical Processing), ROLAP, HOLAP, MOLAP,

DOLAP, etc

• Star schema, normalization,

• Data warehouse, data marts, decision support systems

Insurance DW is complex not large

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BI has been successful in many companies, but despite this business case can be hard to justify

Build logic into the data structure not reports Insurance is complex and requires business expertise

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