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M3 Framework Introduction M3 Framework Overview M3 Phase 0 - Assessment M3 Phase 1 - Readiness M3 Phase 2 - Selection M3 Phase 3 - Engagement M3 Phase 4 - Migration M3 Phase 5 - Operations M3 Resources
M3 Playbook > Phase 1: Readiness
1.10 Plan and Conduct Initial Data Quality Assessment
1.9 Define As-Is and Initial Target State Systems Environments
1.11 Develop Initial Target State Concept of Operations and Scope of Services
Image Map Phase 0: Assessment Phase 1: Readiness Phase 2: Customer Readiness Phase 3: Engagement Phase 4: Migration Phase 5: Operations
Objective:   Develop initial data governance approach and conduct initial data quality assessment and cleansing plan.

Phase 1 GuidanceLegend - Customer, Provider, Shared
 Activities
1. Develop Data Governance Model to include the approach, process, roles and responsibilities, criteria/metrics (C)
2. Determine criteria for assessing data quality (C)
3. Conduct Data Quality Assessment, including master and transactional data (C)
4. Identify data issues (e.g. duplication, missing data, incorrect data) based on the assessment and prioritize data cleansing needs (C)
5. Develop a Data Cleansing Plan based on the prioritization (C)
6. Report updates in governance meetings and Status Reports/Dashboards (C)
7. Begin initial data cleansing (C)

Inputs
• Existing System Data Dictionaries
• Existing Data Quality Assessments
• Functional Specifications

Outputs
• Data Governance Model
• Data Cleansing Plan
Stakeholders Update
• Business Owner (C)
• Program Manager (C)
• Functional Lead (C)
• Technical Lead/Solution Architect (C)
• Data Conversion Lead (C)
• Data SME (C)

Best Practice
• Begin data cleansing activities prior to migration activities and continuously throughout the implementation to assist with data readiness
• Gain agreement on data governance including metadata management and data quality management
• Allocate a sufficient number of Subject Matter Experts (SMEs) with the appropriate skill sets to support data conversion activities throughout the implementation
• Establish criteria and metrics through the Data Governance Model on what threshold constitutes “clean” data