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Sarika Madrewar

Organizations are gradually transitioning from legacy application systems to modern technologies by leveraging cloud-based storage to reap the benefits of modernization. However, their modernization journeys will remain incomplete unless the data is migrated to a current and relevant database hosted on-premises or on cloud. Data migration is the process of transforming extracted data from a source system and loading it on to a target system. There are compelling reasons for data migration, such as application migration, database upgrade, extension of a data center to cloud, disaster recovery, data center relocation, or the merging of data from new sources. Regardless of the reasons, a successful data migration project is beneficial in terms of performance improvement and cost optimization.

Challenges in Data Migration

Although it appears to be a seemingly straightforward process, however, data migration could often be complex, risky, expensive and time-consuming. The following common challenges should be addressed beforehand so that the transition can be completed smoothly:

  • Poor planning and improper analysis of the project scope can lead to the erroneous implementation of migration.

  • Inadequate business engagement with the project management can result in a migration that is not in line with the business needs and requirements.

  • Lack of technical skills and incomplete understanding of data can lead to errors that generate higher unexpected costs.

  • Incorrect estimation of the cost, effort and time during such an immensely complex organizational endeavor can lead to the unavailability of resources and disruption of business operations.

  • Incomplete and inaccurate data backup is a serious threat as it can cause critical process failures and the loss of crucial and confidential organizational data.

A Streamlined Approach to Data Migration

Organizations need to chart a well-planned strategy by conferring with all business stakeholders to outline the scope of data migration, the timeline and the availability of resources. The strategy should be in alignment with business goals to ensure seamless migration. The approach for successful data migration could be:

  • Data Analysis - Examine and define the data before migration. This helps to determine the level of source information that can be included. Analyze the source and target systems by referring to end-users so that the process is fully functional.

  • Proper Allocation of Resources – Formulate a well-defined project scope by involving relevant stakeholders at an early stage for easy budget and resource allocation, and successful process implementation within the stipulated timeline.

  • Data Integrity Validation – Design contingency plans that identify and rectify ‘dirty’ data before its migration to the target system. This is crucial and could compromise process efficiency if not addressed in time.

  • Creation of the Migration Solution – Define the transformation logic on the data chosen for migration, and code the data migration logic to move the transformed data.

  • Testing and Verification – After the migration is complete, create test data in a test database, and subject it to various test scenarios. This reduces the risk of running into issues later when it will be greatly difficult to rectify them.


  • Decommissioning of Older Systems - Shut down the old system functionally and use the migrated data from the modern database for future business purposes.

An Ideal Data Migration Solution

Choosing an effective, ready-to-use solution for organizational data migration is vital since building a tool is complex and time-consuming. The chosen solution should address all major data migration challenges. A solution with the following key capabilities is an ideal choice:


  • Adequate Connectors – The data migration solution should allow the source and target databases to be connected through a wide range of heterogeneous sources, file types and extract, transform, load (ETL) instances.

  • Easy Data Mapping – An intuitive solution with a graphical user interface (GUI) should enable easy visualization of the migration process. A code-free, drag-and-drop GUI for mapping the source and target metadata and transforming rules can reduce or eliminate any tedious efforts during this critical process.

  • Automated Attribute Mapping - Automated mapping of the source and target attributes based on the attribute names reduces 50-60% of the total effort and risks associated with the manual implementation process.

  • Portability: The data migration process facilitated by the solution should be portable and functional in all compatible environments regardless of where it is being defined. This is convenient for users working on production and on-going migrations.

  • Data Integrity and Data Atomicity - The solution should factor in the orchestration of related tables and migrate data to the target system accordingly so that the target records of tables remain in-sync.

  • Data Reconciliation Report - Detailed performance statistics of the volume of data migrated, its accuracy, and completeness offer users with deep insights into the efficiency of the migration process. The solution should be capable of providing such reports and even fixing flawed records.

Along with an ideal data migration solution, skilled experts are also required to support the data migration process, which is unique to the organizational requirements.

For more information on the ideal data migration solution for your organization, contact us at

About the author

Sarika Madrewar
Sarika Madrewar completed her undergraduate degree in Computer Science Engineering from Babasaheb Naik College of Engineering. She leads the Solutions team of the Data Transformation Edition of TCS MasterCraftTM TransformPlus, which is an intelligent automation product for defining and executing data migration from legacy to relational DB or NOSQL databases. She has more than 15 years of industry experience, and continuously contributes to building product solutions and is involved in deployment at customer-end.
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