top of page

Automating Data Migration

Writer's picture: Suhas VivekSuhas Vivek

Updated: Jan 23

Data migration is a critical process in ensuring the success of any implementation project, as the quality and integrity of the data directly influence the overall effectiveness of the new system. Successful data migration ensures that the data is consistent, accessible, and secure, making the transition seamless for end-users. To achieve this, an automated approach is key.


We have developed a robust framework that streamlines the migration process, minimising errors and saving time by maintaining high data quality and integrity throughout the transition. This framework includes a well-organised folder structure and clearly defined processes, enabling an efficient and reliable migration from legacy systems to new platforms.

Folder Structure for Data Migration


To maintain organization and streamline workflows, we created the following folder structure:

  1. Data Migration Templates: Contains all predefined templates for the migration process, typically provided by the target application. These templates define the required fields and rules for uploading data into the target system.

  2. Data Migration Mapping: Contains mapping files, including details of source and target fields, data types, transformations, and business rules.

  3. Data Extraction Files: Stores raw data extracted from the source system.

  4. Scripts: Houses all automation scripts to process, transform, and validate data.

  5. Output Files: Includes processed and validated files in Excel format for review.

  6. Upload Files: Contains files formatted for upload into the target system.

    Data Migration Folder Structure
    Data Migration Folder Structure

Step-by-Step Migration Process


Step 1: Populate the Templates Folder

Begin by adding all data migration templates into the "Data Migration Templates" folder. These templates define the structure of the data required for the target system, including field names, formats, and constraints.

Step 2: Configure the Mapping Files

The "Data Migration Mapping" folder stores mapping files that bridge the gap between the source system and the target system. Each mapping file should include:

  • Target Fields: Names of fields required in the target system, as defined in the template.

  • Format: Data type and field length of the target fields.

  • Input Source: The source file or table from which data will be extracted.

  • Input Field: Corresponding field in the source system.

  • Business Rule: Predefined rules to validate data, such as:

    • Phone Validation: Ensures phone numbers follow a standard format.

    • Email Validation: Verifies email addresses are syntactically correct.

    • ABN Validation: Checks the validity of Australian Business Numbers (ABNs).

    • Transactional Data Lookup Validation:  Ensures that transactional data align with the corresponding entries in the master data, maintaining consistency and accuracy.

  • Transformations: Logic to clean and format data, such as:

    • Adding a leading zero to numeric fields.

    • Converting text to uppercase.

    • Removing blank spaces or invalid characters.

    • Applying predefined transformation logic based on keywords, such as replacing invalid characters with specified placeholders or trimming trailing spaces in specific columns.

Step 3: Extract Data

Extract data from the source system and save it in the "Data Extraction Files" folder. Use automated extraction tools or SQL queries to ensure data accuracy and completeness.

Step 4: Process Data with Scripts

Place all automation scripts in the "Scripts" folder. These scripts should:

  • Read the mapping files to identify target fields, data formats, and transformation rules.

  • Apply business rules to validate data.

  • Execute transformations, such as text cleaning, formatting, and data enrichment.

  • Log errors and exceptions for further review.

Step 5: Review Outputs

Once the scripts have processed the data, store the results in the "Output Files" folder. These Excel files allow for manual review and validation to ensure the data meets business requirements.

Step 6: Generate Upload Files

After reviewing and approving the output files, transform the data into the required format for the target system. Save these files in the "Upload Files" folder. This ensures the data is ready for seamless upload without requiring further manipulation. Step 7: Reconciliation

Once the upload files are generated, use customised reconciliation scripts stored in the "Scripts" folder to verify that the data in the target system aligns with the source data. The reconciliation process should:

  • Compare key fields and records between the source and target systems.

  • Identify discrepancies, such as missing or mismatched data.

  • Provide logs for any issues detected, which are stored in the "Output Files" folder for further resolution.

  • Ensure that data integrity is maintained throughout the migration process.

    Flowchart for Data Migration Process
    Flowchart for Data Migration Process

Automating Business Rules and Transformations


To minimise manual intervention, business rules and transformations are embedded into the scripts. For instance:

  • Business Rule Automation:

    • Phone validation script checks for valid country codes and formats numbers accordingly.

    • Email validation ensures the presence of an "@" symbol and a valid domain.

    • ABN validation uses checksum algorithms to verify legitimacy.

  • Transformation Automation:

    • Text cleaning scripts remove extra spaces, special characters, or invalid inputs.

    • Formatting logic adds leading zeros or standardises field lengths.

    • Conversion functions ensure consistency in case (uppercase/lowercase).

By embedding these rules into the scripts, the system can automatically process and validate data during migration, significantly reducing errors and improving efficiency.

Benefits of an Automated Approach



  • Time Efficiency: Automation accelerates the data migration process, reducing the overall timeline.

  • Improved Data Quality: Validations and transformations ensure clean and consistent data in the target system.

  • Scalability: The structured approach supports the migration of large datasets and multiple data sources.

  • Error Reduction: Automated checks and validations minimise the risk of human error.

Conclusion


Adopting an automated approach to data migration offers significant benefits for a smooth transition from legacy systems to modern platforms. By organizing data into clear folder structures and embedding business rules and transformation logic into automated scripts, we ensure consistent, accurate, and high-quality data throughout the migration. This approach reduces errors, minimizes manual effort, and accelerates the process, while improving scalability and data integrity. The result is an efficient, reliable migration that supports various data sources and system integrations, ultimately reducing risk and enhancing operational efficiency.

 

34 views0 comments

Comments


©2023 by AYLA Solutions.

bottom of page