Best Practices for Successful Data Migration

Authored by: Gautam Pandit, Harsha Chandorkar, Jitesh Shah, Naveen SR, Satheesh K

• Abstract:

Data migration plays a crucial role in modern IT environments, involving the movement of data across different storage types, formats, or systems. This blog offers a comprehensive overview of the data migration process, highlighting key triggers such as lean systems landscape, plant and product data consolidations and organizational mergers. It also addresses challenges related to data integrity, downtime risks, and compatibility.

In the context of a case study, we explore a global leader in audio equipment and lifestyle audio innovations. This company undertook a complex data migration project, dealing with multiple data sources and transformational use cases. The paper outlines the structured approach to data migration, emphasizing pre-migration analysis for scoping and requirements assessment. Additionally, it discusses the actual migration process, emphasizing steps and methodologies for a seamless transition.

Best practices for successful data migration are presented, including thorough planning, stakeholder involvement, and post-migration validation. This case study demonstrates how robust strategies, and meticulous execution can lead to successful data migration in a multifaceted environment.

• Overview of data migration process and data migration triggers:

Migrating data from multiple sources is a complex and challenging process that requires careful planning, execution, and management. In this blog, we present a case study of a data migration project undertaken by a global leader in audio equipment, lifestyle audio innovations, design and analytics. The project involved migrating data from various systems, including ERP, Legacy and PLM, with materials and bills of materials (BOMs) from ERP and datasets from other systems.

The migration involved transforming data from legacy systems to Teamcenter platform, while also ensuring data accuracy, completeness, and security. The process required significant collaboration and coordination among stakeholders across the organization, including IT, business, and functional teams.

• Data migration process
• Data Identification – Objective is to identify all relevant data sources and understand the data requirements for the PLM system. Comprehensive Pre-Migration Analysis (PMA) is done to identify the data eligible for migration. Migration use cases are also identified.
• Identify Sources: Determine all the systems, databases, and files from which data needs to be migrated. These could include ERP systems, CAD systems, spreadsheets, legacy databases, and more.
• Data Mapping: Map the data fields from each source to the corresponding fields in the PLM system. This involves understanding the data structure, formats, and relationships.
• Data Profiling: Assess the quality, completeness, and relevance of the data in the existing systems. This helps in identifying any data cleansing needs beforehand.
• Data Extraction – Objective is to Extract data from the identified sources.
• Extraction Methods: Choose appropriate extraction methods based on the source systems. This could involve database queries, APIs, or manual extraction for unstructured data.
• Automation: Automate the extraction process where possible to ensure consistency and reduce manual errors.
• Incremental Extraction: For systems that are in continuous use, implement incremental extraction to capture changes over time without affecting system performance.

• Data Transformation – – Objective is to cleanse, format, and transform the extracted data to fit the target PLM system’s requirements. During PMA, transformational use cases are also identified.
• Data Cleansing: Address data quality issues such as duplicates, missing values, and inconsistencies. This step is critical to ensure the accuracy and reliability of the data being migrated.
• Normalization: Standardize data formats, units of measure, naming conventions, and other attributes to ensure consistency.
• Transformation Rules: Apply transformation rules to convert data from the source format to the target PLM system format. This could involve data type conversions, merging fields, splitting fields, and other modifications.
• Data Enrichment: Enhance the data by adding missing information or deriving new data fields where necessary.

• Data Loading – Objective is to load the transformed data into the PLM system.
• Initial Load: Perform an initial load of the data into a staging environment within the PLM system. This allows for preliminary testing and validation.
• Batch Processing: Load data in batches to manage system performance and handle large volumes of data effectively.
• Data Integrity Checks: During loading, perform integrity checks to ensure that relationships between data entities are maintained (e.g., linking parts to assemblies in a PLM system).
• Data Validation – Objective is to ensure that the data in the PLM system is accurate, complete, and functional.
• Data Verification: Compare the loaded data against the original source data to verify accuracy. This involves checking for data completeness, consistency, and correctness.
• User Acceptance Testing (UAT): Engage end-users to test the data in the PLM system to ensure it meets their requirements and functions correctly within their workflows.
• Reconciliation: Address any discrepancies identified during validation by correcting the data and re-loading if necessary.
• Final Validation: Perform a final round of validation to ensure all issues have been resolved and the data is ready for production use.
• Additional Considerations
• Documentation: Maintain detailed documentation throughout the migration process, including data mapping, transformation rules, and validation procedures.
• Data Governance: Implement data governance policies to manage data quality and ensure compliance with relevant regulations and standards.
• Change Management: Prepare users and stakeholders for the transition to the new PLM system through training and support.

By following these steps, we can effectively migrate data from multiple sources into a single PLM system, ensuring that the data is accurate, complete, and ready for use in managing the product lifecycle.

• Challenges associated with data migration

This blog also outlines the challenges faced during the project, including data quality and clean-up issues, data governance concerns, transformational use case alignments and integration challenges. We describe the technology solutions used to overcome these challenges and highlight the best practices that are implemented to ensure the success of the data migration. We also provide insights into the benefits of data migration, including improved business operations, enhanced customer experiences, and driving growth.

By sharing our experience and lessons learned, we hope to provide guidance and recommendations for organizations undertaking similar data migration projects.

Below listed are some of the data migration challenges which organization may face during execution phase.

Data Migration Challenges can be minimized by selecting appropriate migration approach and defining data migration strategies.

• Data Migration Approach:

Data Migration Solution Approach refers to the strategic plan and methodology adopted by organizations when transitioning from one technology, system, platform or environment.