What are the key data migration standards and guidelines that you follow or recommend?

Want to be a rockstar DM consultant? The 1st step to writing a DM strategy is to NOT write one. Data transformation requires business transformation. Understand the business outcome desired, ask the executive stakeholders what is keeping them up at night, listen to the legacy superusers who have lived the datajourney. DM is not ETL or a technical exercise. Understand the Business Outcome and connect the dots to the data that matters most. As tablestakes, your strategy document will address the process for Extracting, Mapping, Transforming, Testing, Cleansing, Harmonizing, Validating and loading data for your project. Included in this is the process for data cleansing and harmonization, as well as the different technologies used.

Key data migration standards and guidelines include adherence to ISO/IEC 25064 for data quality, utilizing the DAMA-DMBOK framework for comprehensive data management, and following the Extract, Transform, Load (ETL) process as defined by Kimball methodologies. Additionally, compliance with industry-specific regulations like GDPR or HIPAA is crucial. Employing version control, checksums, and data lineage tools ensures data integrity. A well-defined data migration plan encompassing source analysis, mapping, testing, and validation stages is essential. Real-time monitoring and error handling mechanisms, coupled with continuous improvement practices, contribute to a robust and successful data migration strategy.

Data migration thrives on a mix of structure and adaptability. Instead of rigid guidelines, I embrace a fluid strategy that evolves with project needs. First, align migration goals with business outcomes, treating data like a living asset that must adapt as business priorities shift. Leverage predictive analytics to identify potential risks before they arise, allowing proactive course correction. Implement an ETL process that isn't static but agile, adjusting to real-time data flow changes. Continuous testing becomes a natural part of the workflow, not just a final checkpoint. Open, ongoing dialogue with stakeholders transforms them from mere observers into active participants in the migration journey.

Data Migration Steps 1) Planning & Strategy Objective, Scope & Stakeholders 2) Data Assessment, Preparation & Migration Strategy Data Quality, Data Cleansing and Data Mapping 3) Execution, Monitoring & Testing Define Approach, Phased Migration, Tools to be used & Testing Plan 4) Validation & Verification Data Validation & UAT 5) Post Migration Activities Documentation & Training 6) Compliance & Security Regulatory compliance & Data Security 7) Continuous Improvement Review post Migration and Update the procedures as needed.

Below are the key important points in migration strategy 1) Build inventory for business areas involved, business & IT applications, systems, database & volume etc. 2) document business prioritized applications with criticality, their dependencies, software tools, reporting tools etc. currently used and new tools required for new migrating application platforms. 3) Identify and use technical & business domain expert(s) for type(s) of business involved in migration for migration challenges and business process flows. 4) Build high level migration plan that brings confidence to stakeholders to perform data migration concurring with stakeholders, Architects, business/technical managers etc. to have an proper strategy for successful migration

A typical data migration project consists of four phases: analysis, design, execution, and validation. In the analysis phase, source and target systems are assessed, with data entities, attributes, and relationships identified, and data transformations mapped. The design phase defines the migration architecture, test cases, and scripts. Execution focuses on the ETL process—extracting, transforming, and loading data, while monitoring and resolving errors. Finally, the validation phase verifies data quality, completeness, and accuracy, followed by user acceptance testing (UAT) and sign-off. Each phase has specific tasks, deliverables, and documentation.

The approach described here seems to only consider data migration as a big bang operation. In reality the migration needs to happen alongside the system development. Analysis of existing data should inform the design of a service alongside user research and business objectives. The design, execution and validation of a data migration process is most successful when it is an iterative activity that aligns with the system development roadmap. The data migration team needs to work closely with the rest of the system designers and implementers as part of the overall delivery team and fit in and help drive the delivery schedule.

A successful data migration project should adhere to a structured process consisting of four main phases: analysis, design, execution, and validation. Each phase has specific tasks and deliverables that must be documented. Example: During a migration from an on-premises ERP system to a cloud-based solution, we meticulously followed these phases. In the analysis phase, we assessed the source and target systems, identifying data entities and relationships. This structured approach ensured that we addressed all aspects of the migration and minimized the risk of oversights.

To ensure a successful data migration, follow proven best practices. Start by establishing a solid data governance framework that defines roles, responsibilities, and standards. Conduct a thorough data quality assessment and cleansing before migration to resolve any issues. Select a data migration tool that supports your approach and offers features like data profiling, mapping, validation, and reporting. Test processes iteratively using realistic data sets and scenarios. Plan for migration downtime and communicate clearly with stakeholders. Finally, document all processes, provide training, and offer support to users on the new system to ensure a smooth transition.

Data quality is paramount during migration. Implementing best practices for data cleansing and validation is essential to ensure that the data being migrated is accurate and reliable. Example: In a project where we migrated customer data for a retail client, we conducted a thorough data quality assessment before migration. We used data profiling tools to identify duplicates and inconsistencies, which we addressed through data cleansing. This proactive approach significantly improved the quality of the migrated data and enhanced user trust in the new system.

Best practices always vary between the origin and destination of the data migration, but in general they are: 1. Clean the data 2. Format it for destination 3. Migrate 4. Report in origin and destination to monitor differences 5. Backup!

Get yourself a copy of Practical Data Migration by Johny Morris. It is on version 3 now and its worth getting the latest version as it stays up-to-date with the latest technology stacks. It is required reading for all our Data Engineers and will help you navigate all the competing demands of delivering a Data Migration that does not cost the earth or break your staff.

Here are a few tips that could help ensure a successful data migration. Scoping: It's crucial to establish a clear scope of the data migration. i.e. What are the Data sources?, Number of tables?, Out of scope considerations, etc. Tool Selection: Choosing the right tool for data migration should be tailored to the complexity of the scope. Analysis and Discovery: - Identify the key stakeholders for the data migration discovery who could provide details about the legacy architecture. - Identify any transformation business rules that could be required as part of the data migration. - Develop a comprehensive data mapping strategy to understand how data elements (Tables/Fields) in the legacy system correspond to those in the target system.

Some of inter-platform data migrations challenges I have been facing were: data corruption, data dependencies, data quality and data loss. In order to avoid those common issues, I needed to analyze all thrown exceptions, and to determine significance of their occurrence. Usually not a single database/table record is allowed to be lost. Therefore I needed to break down the migration process to several iterations, and design/develop data cleansing pre-migration procedures consequently. Analysis of found commonalities in the lost or corrupted data gives you an idea of possible solution. Usually some single records have low quality, also data type conversions must be applied, and post-migration statistical data analysis is always required;

1. Clearly define the data to be migrated, target systems, timelines, and resources needed. 2. Protect sensitive data during migration. 3. Establish clear mapping rules and transformation needed to migrate data between source and target systems. 4. Thoroughly test migrated data and systems before roll-out. 5. Use Data Versioning along with rollback and recovery strategies for reverting to previous states in case of issues. 6. Consider professionals who have successfully completed similar projects to complete it gracefully. 7. Choose migration methods that minimize interruption to business operations. 8. Keep all stakeholders and business persons informed of the plan, progress, and potential challenges throughout the migration.

I participated in a major data migration project for our enterprise data warehouse. Beyond just transitioning our data and users to a cloud-based platform, we also saw this as a chance to make significant improvements in key areas: 1. Standardizing coding and deployment procedures. 2. Enhancing our data documentation around data lineage and data transformations. 3. Redesigning data models to better align with the business reporting needs. 4. Streamlining by not migrating outdated data sets that were no longer needed by stakeholders. When planning a data migration, it's important to look for opportunities to improve processes and standards, ensuring a more efficient data management environment that better supports your company.

While the technical aspects of data migration are crucial, it's equally important to consider the human element. Effective communication and collaboration among stakeholders are essential for a successful project. In my experience, involving end-users early in the process can lead to valuable insights and help mitigate resistance to change. Key Takeaways: Develop a comprehensive data migration strategy that aligns with your business goals. Prioritize data quality assessment and cleansing to ensure accuracy and consistency. Foster effective communication and collaboration among stakeholders, including end-users. View data migration as an ongoing process and be prepared to adapt to evolving business needs.

The cutover plan and strategy are critical phases in data migration. They define the moment of transition from the old to the new system, requiring meticulous planning. Timing, data freeze, validation, backups, rollback plans, testing, communication, and monitoring are key considerations. Timing should minimize business disruption, a data freeze prevents complications, validation ensures accuracy, and backups provide safety nets. Rollback plans are contingency measures, testing catches issues, and communication keeps stakeholders informed.These elements collectively guarantee a seamless transition, reducing disruption, data loss, and potential setbacks while maintaining data integrity in the new system.

Testing a number of pilot solutions on differently scaled sample data sets is the shortest way for preparing the whole data for the migration.