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Mid Continent Grou p

Mid Continent Grou p. Insurance Performance Foundation. Presenter. Chris Reaves is the Project Lead on the IBI Data Warehouse Project and DBA supervisor. He holds a Bachelor’s degree in Business Information Technology from Rogers State University and is a native of Oklahoma.

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Mid Continent Grou p

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  1. Mid Continent Group Insurance Performance Foundation

  2. Presenter • Chris Reaves is the Project Lead on the IBI Data Warehouse Project and DBA supervisor. He holds a Bachelor’s degree in Business Information Technology from Rogers State University and is a native of Oklahoma

  3. Mid Continent Group • Mid Continent Group Founded in 1956 and in 1983 became an indirect subsidiary of American Financial Group and consists of: • Mid Continent Casualty which is licensed in 34 states, with written premium in 43 states • Mid-Continent Assurance which is licensed in eight states has the same focus as Casualty. • Oklahoma Surety which is licensed in six states also has the same focus as Casualty. • Mid-Continent Excess and Surplus Insurance Company,  focused on non-admitted business. Eligible in 19 states

  4. Data Warehouse Solution • Partnered with IBI we have been working on our Enterprise Data Warehouse for a little under three years. IBI offered a reporting application, with numerous industry reports including ETL engine to move data off our Legacy systems into a SQL Data Warehouse.

  5. Where did all the dirty data come from? • The need of products going to market in a timely manner. • This required programs to be developed based on quick turnaround, often data validation was not included • Different Business Departments with different requirements. (Often shortcuts) • We did not realize the extent of the problem

  6. What effects on our Data Warehouse • Missing transactions • How hard It is to get counts with no transactions • Missing data required for filtering • Policy Holder City and State • Duplicate Policy Holder Names • Invalid Changes to Data • Policy and Claims status changes • Attributes that changed that could effect drill downs • Drill down on comparisons of Policies and Claims

  7. Profile – Zip Code and City

  8. Profile – Policy Holder Name

  9. Profile - Duplicates

  10. Policy Holder Data Quality Flow • We decided to focus on two items • Standardizing and Removing Duplicate Policy Holders • Find the “Golden Record”, and link all transactions to the following Policy Holder Information • Cleansing Zip Code and City Information • Created zip code validation module to search for City and State matches based on fuzzy matching logic to look for misspelling of City Names and Invalid Zip Code and City matches.

  11. Demo of Policy Holder Flows • Two Flows were created • Policy Holder Cleansing • Policy Holder Matching

  12. Demo of Zip Code Validation Module • Fuzzy Logic Matching • Real life example of issue found and the fix

  13. Demo of Issue Tracker • Demo of workflow driven issue program to allow business units to cleanse data using approval driven workflows • The ability to use the same Zip Code Validation Module to offer proposals to the data custodians. • Match and Merge functionality.

  14. Questions? Chris Reaves wreaves@mcg-ins.com

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