Blueprint Data Health Assessment


for a Specialty Pharma Manufacturer

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

Background

  • The Specialty Business Unit of a top Pharma company was facing operational challenges owing to issues with the data
  • Internal stakeholders had a lack of trust in data content and inaccurate data, resulting in frequent data rework and clarification efforts
  • Legal, compliance, sales, and marketing teams were not able to consume data effectively owing to low trust and poor data health
  • Data inaccuracy and lack of trust in content resulted in operational challenges and poor data health and consumption for the customer

    Solution

  • DataZymes conducted a Blueprint Data Health Assessment that was highly customized to deliver maximum value to the client
  • We put together a multi-skilled team consisting of specialty pharma subject matter experts, data management and MDM experts and process management experts to conduct the assessment
  • Client stakeholders included stakeholders from senior management, data management, IT, MDM, and business teams
  • The assessment was undertaken by applying 4 lenses to each phase of the data management cycle - process/workflow, data, rules/logic, and technology
  • The assessment was undertaken through a detailed review process consisting of 18 stakeholder interviews, analyzing 500+ emails, review of 20+ process documents and data models, as well as in-flight initiatives
  • The assessment included assessment of all data sources used by the Specialty Business Unit – IMS DDD, SPP, VCEN, CARS, Hub Data, SHA data
  • The DZ Team made recommendations of the overall data health framework, including an automated dashboard to monitor real time data health with proactive alerts

    Impact

  • The key outcome of the project was identification of key improvement opportunities and a roadmap for implementation of these opportunities
  • As part of the deliverables, we developed and shared SIPOC diagrams, over 6 process maps, 90+ data quality checks and improvement opportunities, and training materials for internal consumption
  • We also made recommendations of the overall data health framework, which included a automated dashboard to monitor data health in a near – real time, with proactive, automated alerts