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CodeNinja is a full-stack AI delivery company that helps enterprises, governments, and software acquirers build and operate intelligence-driven systems for mission-critical workflows. We specialize in deploying AI into real operations—combining strong engineering fundamentals with AI-native delivery to create measurable value, resilience, and long-term ownership for our clients. Our global footprint and delivery model are supported by AI Labs, AI Pods, and Global Capability Centers, enabling teams to co-engineer scalable platforms across regions and time zones.
Lead the ERP program’s data strategy by ensuring data readiness, conceptual data modelling, reporting data sourcing, and structured data migration planning, enabling a reliable, unified data foundation across all modules.
· Conduct comprehensive assessment of legacy data sources:
o Inventory, Maintenance (MRO), Procurement, HR, Technical Docs
· Identify:
o Data quality issues (duplicates, missing, inconsistencies)
o Data ownership and governance gaps
o Redundant/siloed datasets
· Define data cleansing and standardization approach
· Produce Data Readiness Assessment Report
· Define end-to-end Data Migration Strategy, including:
o Migration approach (phased, module-wise, or big-bang)
o Data prioritization (master data vs transactional vs historical)
· Develop Data Mapping Specifications:
o Legacy → ERP field-level mappings
o Transformation rules and standardization logic
o Handling of missing/invalid data
· Define Data Cleansing & Preparation Plan:
o Deduplication rules
o Data enrichment where required
o Validation checkpoints before migration
· Design Migration Execution Plan:
o Migration cycles (mock runs, dry runs, final cutover)
o Rollback strategy in case of failure
o Downtime and cutover planning
· Define Data Validation & Reconciliation Framework:
o Pre-migration vs post-migration validation rules
o Reconciliation reports (counts, totals, relationships)
o Sign-off criteria for each module
· Produce key deliverables:
o Data Migration Plan
o Data Mapping Documents
o Data Validation & Reconciliation Reports
· Identify and enable AI/ML use cases, such as:
o Predictive maintenance modeling
o Inventory demand forecasting
o Supplier performance analytics
· Leverage AI tools for:
o Data profiling and anomaly detection
o Data quality assessment and cleansing recommendations
o Advanced analytics and insights generation
· Define enterprise conceptual data model:
o Assets, Inventory, Work Orders, Suppliers, Workforce, Technical Docs
· Establish:
o Entity relationships
o Master data definitions
o Data standardization rules
· Support BI teams for dashboards and analytics
· Align data models with business workflows
· Ensure requirements are data-feasible and consistent
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