Project Scope

• Data Dictionary: Create a centralised reference document for all key data elements used across the plant’s operations (e.g., product details, batch numbers, manufacturing processes, distribution channels).
• Data Model: Design a logical and physical data model to map out the relationships and structure of the plant’s data, ensuring smooth integration across systems (e.g., ERP, production, distribution, and inventory systems).
• Standardisation: Establish standard data definitions, formats, and naming conventions to ensure consistency across all platforms.

Project Phases

Phase 1 - Discovery & Requirements Gathering

• Interview key stakeholders (e.g., production managers, distribution leads, IT staff) to understand business processes and data needs.
• Identify critical data sources and flows within the plant (e.g., manufacturing records, inventory levels, order processing).
• Review existing data structures, identifying gaps, redundancies, or inconsistencies.

Phase 2 - Data Dictionary Design

• Define all key data elements used across the plant’s operations, including their attributes (e.g., data type, format, description, allowed values).
• Standardise terminology and units of measurement across all data definitions (e.g., units of measure, date formats, etc.).
• Organise the data dictionary into categories or domains (e.g., product data, packaging data, shipment data).
• Develop guidelines for maintaining and updating the data dictionary over time.

Phase 3 - Data Model Development

• Design a logical data model to define entities (e.g., products, orders, batches) and relationships between them (e.g., one-to-many, many-to-many).
• Map out data flows between systems, ensuring smooth integration and minimisation of redundancy.
• Develop a physical data model based on the logical design, ensuring alignment with existing IT infrastructure (e.g., database platforms, ERP systems).
• Review the data model with key stakeholders to validate accuracy and completeness.

Phase 4 - Implementation & Integration

• Implement the data dictionary and model into the plant’s systems, ensuring that all relevant teams (e.g., IT, production, inventory, sales) have access.
• Integrate the data model with existing systems (ERP, CRM, manufacturing software) to streamline data collection and usage.
• Develop automated workflows to maintain and update the data dictionary and model as new data elements or processes are introduced.

Phase 5 - Testing & Validation

• Test the implementation to ensure the data dictionary and model are functioning as intended.
• Validate that data is being entered, processed, and stored consistently across all systems.
• Conduct user training to ensure all departments understand and follow the new standards.

Phase 6 - Ongoing Maintenance & Governance

• Set up a governance structure for the data dictionary and model, including regular reviews and updates.
• Implement version control for data model changes and document the update process.
• Establish best practices for data entry and ongoing data quality checks.

Key Deliverables

• A comprehensive, well-organised data dictionary with clear definitions for all key data elements.
• A logical and physical data model that maps out data relationships and flows across systems.
• Integration of the data dictionary and model into plant systems for real-time use.
• User training materials and guidelines for ongoing data governance.

Key Benefits

• Consistency: Standardised definitions and formats for data ensure consistency across departments.
• Efficiency: Reduced errors and duplications, leading to more streamlined operations.
• Improved Decision-Making: Access to accurate, well-structured data that can be leveraged for reporting and analysis.
• Scalability: A robust data model that can be easily adapted to future business needs or system upgrades.

Data Lifecycle Management

• Defining workflows for data creation, updates, and retirement
• Ensuring consistency in source data records over time
• Implementing version control and audit tracking
• Establishing a system for continuous data quality monitoring