Mastering Data Migrations: Strategy, Tools, and Best Practices

In today’s dynamic digital landscape, data migration is a critical undertaking. Whether modernizing IT infrastructure, transitioning to cloud platforms, or merging systems after an acquisition, organizations rely on seamless data migration to maintain operational continuity and unlock new opportunities. But data migration isn’t just about moving data from one place to another; it’s a comprehensive process involving meticulous planning, cleansing, automation, and validation.

This blog delves into the intricacies of data migration, addressing four essential pillars: Migration and Strategy Definition, Migration Automation Scripts, Data Scrubbing & Cleansing, and Validation Testing. We will also explore how Infor’s products and services provide robust solutions to ensure smooth migrations.

Migration and Strategy Definition

A clear and structured migration strategy is the cornerstone of any successful data migration initiative. Without it, organizations risk encountering unexpected roadblocks, corrupted data, or prolonged downtime.

Why Is a Data Migration Strategy Important?

A data migration strategy outlines the what, why, and how of your migration process. It ensures alignment between technical teams and business stakeholders, reduces risks, and optimizes costs and timelines.

Key Drivers of Data Migration

System Modernization: Upgrading from legacy systems to cutting-edge platforms like Infor Cloud Suite or Infor LN to improve efficiency and scalability.
Cloud Adoption: Migrating on-premises data to cloud ecosystems, such as Infor Cloud Suite Industrial, to leverage flexibility and cost advantages.
Mergers and Acquisitions: Integrating disparate systems to achieve operational and data consistency.
Regulatory Compliance: Ensuring adherence to frameworks like GDPR or HIPAA by centralizing data and ensuring security protocols are met.

Steps in Strategy Definition

Assessing the Existing Landscape

Data Inventory: Catalogue all data assets, including structured, semi-structured, and unstructured data.
System Compatibility: Assess compatibility between the source and target systems, including schema differences and infrastructure.
Stakeholder Involvement: Collaborate with key stakeholders, including IT, operations, and compliance teams, to ensure alignment with organizational goals.

Defining Objectives and Scope

Clearly articulate what the migration aims to achieve. For instance:

Data Retention Goals: Determine whether all historical data or only recent data needs to be migrated.
Performance Improvements: Set benchmarks for system response times and processing speed in the target environment.

Risk Management and Contingency Planning

Identify Potential Risks: Pinpoint risks like downtime, data loss, or compliance breaches.
Develop Contingency Plans: Define fallback procedures in case the migration does not go as planned.

Tool and Platform Selection

Modern tools like Infor OS (Operating Service) can act as an integration hub to streamline complex migration workflows. Infor Data Lake enables the storage of unstructured data in a centralized, scalable repository, simplifying the migration of diverse data types.

Timelines and Resource Allocation

Establish realistic timelines for each phase and allocate resources appropriately. Break down the migration into smaller milestones to facilitate progress tracking.

Migration Automation Scripts

Data migrations are inherently complex, involving repetitive tasks that are prone to human error. Automation scripts are indispensable for improving efficiency, accuracy, and speed.

What Are Migration Automation Scripts?

Migration automation scripts are pre-coded sets of instructions designed to handle repetitive, labor-intensive migration tasks. They facilitate tasks such as:

Extracting data from the source system.
Transforming it to meet the schema and requirements of the target system.
Loading it into the destination environment.

The Role of Automation in Data Migration

Simplifying the ETL Process

Scripts streamline the Extract, Transform, Load (ETL) process, which forms the backbone of most migration efforts.

Extraction: Automation scripts can extract data incrementally, reducing the load on source systems during operational hours.
Transformation: Scripts can clean, deduplicate, and restructure data to match target schemas dynamically.
Loading: Batch or incremental loads minimize disruptions to business operations.

Error Reduction

Automated scripts reduce the likelihood of common errors, such as mismatched fields or skipped records, by executing defined rules consistently.

Accelerated Timelines

Automation significantly shortens the migration cycle by performing tasks concurrently or sequentially without manual intervention.

Tools and Techniques for Automation

Infor provides powerful tools to aid automation during migrations.

Infor Mongoose

A low-code application platform that supports the creation of automated workflows and integrations, enabling non-technical users to automate migration tasks.

REST APIs and Integration Frameworks

Infor’s REST APIs facilitate direct communication between legacy systems and Infor CloudSuite, enabling automated data extraction and loading.

Scripts and Frameworks

Python & SQL: Widely used for custom ETL scripting.
RPA (Robotic Process Automation): Tools like UiPath, integrated with Infor platforms, can automate repetitive migration tasks.
Infor ION (Intelligent Open Network): Provides real-time monitoring and automation of business processes, reducing the risk of human oversight.

Best Practices for Writing Automation Scripts

Create Reusable Modules: Develop templates that can be reused for future migrations.
Test in a Staging Environment: Always test scripts in a sandbox environment to identify potential issues before deployment.
Build Error Logging Mechanisms: Ensure scripts can capture errors and provide actionable logs for debugging.

Data Scrubbing & Cleansing

Clean data is critical for the success of any migration project. Migrating bad data results in operational inefficiencies, reporting inaccuracies, and poor user experiences in the target system.

The Importance of Data Cleansing

Data scrubbing ensures the integrity, consistency, and usability of migrated data. Common issues in unclean data include:

Duplicates: Repeated records that inflate data volumes and confuse operations.
Inaccurate Fields: Incorrect or outdated data that undermines decision-making.
Inconsistent Formats: Variations in data formats (e.g., date formats) that disrupt system compatibility.
Incomplete Records: Missing critical fields required for operational use.

Steps to Clean Data Before Migration

Data Profiling

Analyze the source data to identify errors, inconsistencies, and redundancies. Profiling tools can reveal valuable insights, such as missing values or skewed distributions.

Standardizing Formats

Ensure uniformity in formats across the dataset. For example:

Standardize date formats to ISO 8601 (YYYY-MM-DD).
Normalize units of measurement (e.g., kilograms instead of lbs).

Deduplication

Use advanced matching algorithms to identify and remove duplicate records. Tools like Infor Data Lake Catalog facilitate deduplication by leveraging metadata and semantic relationships.

Validation Rules

Implement rules to validate fields such as email addresses, phone numbers, or ZIP codes against expected formats.

Infor’s Data Cleansing Solutions

Infor’s products are designed to simplify and enhance data cleansing:

Infor Data Lake: Enables organizations to process large-scale data scrubbing efficiently by integrating AI-powered deduplication and validation mechanisms.
Infor Birst: Helps transform and cleanse data while providing a unified view for analytics and reporting.

Best Practices for Data Cleansing

Focus on Critical Data Sets: Prioritize cleansing data critical to business processes.
Involve Domain Experts: Engage domain experts to verify data quality and relevance.
Automate Where Possible: Use cleansing scripts or automation tools to process large data sets quickly and accurately.

Validation Testing

Once data has been migrated, the final step is to validate its accuracy, completeness, and usability. Validation testing ensures that the migration meets business and technical requirements.

Types of Validation Tests

Pre-Migration Validation

Before initiating migration, verify the integrity of the source data:

Ensure schemas align between source and target systems.
Validate data formats, completeness, and relationships in the source system.

Post-Migration Validation

Once data is loaded into the target system, confirm its accuracy and usability:

Check record counts to ensure no data was lost during the transfer.
Validate relationships between tables, such as foreign key constrain

Functional Validation

Test the functionality of migrated systems, ensuring that workflows and business rules operate as expected.

Reconciliation Testing

Reconcile data between the source and target systems using automated comparison scripts.

Automating Validation Testing

Data Comparison Tools

Automated tools can compare row counts, sums, and other metrics across source and target databases. Scripts written in SQL or Python are often used for this purpose.

AI-Powered Anomaly Detection

Infor’s Coleman AI can analyze migrated datasets and identify anomalies or inconsistencies that may be difficult to detect manually.

Automated Reports

Infor’s ION Workflow generates detailed reports on validation test results, providing insights into migration success rates and exceptions.

Key Metrics for Validation Success

Error Rate: Percentage of records with inconsistencies.
Performance Metrics: Response time and system availability post-migration.
User Satisfaction: Feedback from end-users on system usability and accuracy.

Conclusion

Data migration is a high-stakes initiative requiring precision and robust tools. By defining a comprehensive strategy, automating migration tasks, scrubbing and cleansing data, and performing thorough validation testing, organizations can minimize risks and maximize the value of their data systems.

Infor’s suite of tools, including Infor Cloud Suite, Infor Data Lake, and Infor Birst, empowers businesses to execute migrations with confidence and agility. Whether you are modernizing IT infrastructure or consolidating disparate systems, Infor’s solutions offer the scalability, automation, and intelligence required to succeed.

Are you ready to transform your data systems? Contact Infor’s migration experts today to explore how their solutions can simplify your journey.