Data Make information accessible Enhance with analytics Integration Enterprise view of business Concerns: Security Breadth of users Project risk and cost 4 Benefits often intangible
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 5
one of the largest users of data Early failures Notable successes BI in other industries 1980’s 1990’s 2000’s 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
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 10
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) 13
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 17
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
Store Integrated Systems e.g. valuation system ETL ETL Source systems 19 APPLICATION LOGIC Goal Move application logic into the system managed by metadata
(FK) Jurisdiction (FK) Policy Id Date of Birth Issue Age Issue Date (FK) Sum Assured Annual Premium Reserve Time Dimension (1) Extract Date (PK) Year Quarter Month Product Code Dimension Product Code (PK) Product Name Product Description Product Type Product Fund Product Group Jurisdiction Dimension Jurisdiction (PK) State or province Sales Area Country Time Dimension (2) Issue Date (PK) Issue Year Band Year Quarter Month
(FK) Jurisdiction (FK) Policy Id Date of Birth Issue Age Issue Date (FK) Sum Assured Annual Premium Reserve Time Dimension (1) Extract Date (PK) Year Quarter Month Product Code Dimension Product Code (PK) Product Name Product Description Product Type (FK) Jurisdiction Dimension Jurisdiction (PK) State or province Sales Area Country Time Dimension (2) Issue Date (PK) Issue Year band Year Quarter Month Product Type Dimension Product Type (PK) Product Fund Product Group
(online Analytical Processing), ROLAP, HOLAP, MOLAP, DOLAP, etc • Star schema, normalization, • Data warehouse, data marts, decision support systems Insurance DW is complex not large 23
despite this business case can be hard to justify Build logic into the data structure not reports Insurance is complex and requires business expertise 24