Accounts Master Clean-up


for a Specialty Pharma Manufacturer

Type
Pharma Manufacturer
Duration
4 Weeks
Size
10,000+ Employees

Background

  • The Specialty Business Unit of a top Pharma company had issues with the account masters
  • Mismatch of IDs and Addresses, created incorrect and duplicate master records
  • While the business required data for 9,000+ accounts only, the total number of master records were over 400,000
  • Internal and external data sources could not be integrated due to lack of a reliable master
  • The business required data for 9,000+ accounts only - but total master records were over 400,000. As a result, internal and external data sources could not be integrated due to lack of a reliable master.

    Solution

  • DataZymes employed a consultative and phased approach to enrich the masters, improve accuracy and reliability of records

  • Step 1: Identify Standards and Assess Current State
    The first step was to identify standard identifiers to match accounts. Key identifiers selected – DEA, HIN, NPI and DDD. The current match percentage of accounts was less than 30%

  • Step 2: Improve match percentage through IDs
    Improve match percentage using universe of key identifiers and enriching the current masters. This process improved the match percentage to 43%

  • Step 3: Automated fuzzy matching
    Implement a fuzzy logic algorithm to match accounts based on name, address, zip and suite number. Implementation of this phase involved iterative control relaxation of codes to improve match percentage. The results were validated programmatically. This step improved the match percentage to 78%

  • Step 4: Manual Data Stewardship
    The next step involved manual stewardship of the fallout records to improve match and merge. The MDM rules were updated based on inputs from the manual resolution team to improve match percentage in future data loads. This process improved the match percentage to 96%. A continuing data stewardship role was set up to handle additional exceptions that may arise in future data loads
  • The DZ Team improved the match percentage from less than 30% to over 95%, enabling the customer to integrate syndicated sources

    Impact

    The match percentage improved from less than 30% to over 95%, enabling the client to integrate syndicated sources like DDD and SHA with internal sources such as finance, sales and targeting data, thereby improving accuracy of analytical outputs by a huge margin