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REPAIRS DIAGNOSTICS AND DYNAMIC FIELD SERVICE SCHEDULING

• Accuracy: Meaning data reflects the actual state of repairs and maintenance activities. It is free from errors, duplications, or outdated information. This helps at the end of the workflow too, for example, if a repair has been completed the data should accurately reflect total costs and any associated asset failure flags.
• Completeness: Data encompasses all relevant information required for repairs management. It includes essential details such as repair requests, work orders, field operative assignments, repair descriptions, parts and materials used, photos, and timelines. Complete data ensures that the entire repair process can be tracked and analysed effectively.
• Consistency: Across different data sources and records. It follows predefined formats, standards, and conventions for capturing information. For instance, consistent categorisation of repairs, standardised codes for equipment or parts, and uniform labeling of repair stages or statuses ensure clarity and ease of satisfaction reporting.
• Timeliness: Live, or as close to live as possible. It is essential to capture repairs and maintenance activities in a timely manner to ensure accurate tracking and reporting of Time-to-respond and First-time fix.
• Integration: Clean data in repairs management may involve integrating data from various sources, such as digital repair request forms, work orders, equipment databases, inventory systems, and financial records. Integration ensures that relevant information is consolidated and accessible in a centralised system, reducing data silos and facilitating efficient headcount management.
• Data validation: Thorough validation processes to identify and rectify any errors, inconsistencies, or missing information. Validation checks may include verifying data against predefined rules, cross-referencing with existing records, and confirming accuracy with relevant stakeholders. Validation helps maintain data integrity and reliability.
• Data governance: Repairs management in tenants' homes and estates often relies on robust data governance practices. Data governance involves establishing policies, procedures, and controls to ensure data quality, security, privacy, and compliance. It includes defining roles and responsibilities, implementing data quality metrics, and enforcing data management standards throughout the repairs management process.