Data Migration

The architecture and storage methods of modern software systems are radically different from older systems, legacy data often does not meet the criteria set by the new system, and must be modified prior to migration.

Solution Overview

In a globally competitive business world, enterprises need to keep pace with changing technologies by upgrading existing applications, or replacing current product with a new solution. Because the architecture and storage methods of new or updated systems are usually quite different, legacy data often does not meet the criteria set by the new system, and must be modified prior to migration.

At Bangalore Softsell, we have developed a comprehensive suite of solutions that address a wide range of data migration needs for businesses today. Our solutions focus on your business needs, with expert consultation, rapid implementation, and business driven results.

Whether the need be for data quality management on an on-going basis or data migration for technology modernization/ new product implementations, we offer data management services using well-proven processes.

We help minimize customer pain points due to issues of complexity, disruption, performance, and host availability. Our methodology ensures that our data quality management & data migration services are delivered on time and stay the course on budget.


Data Landscape Study

The aim of this stage is to come up with a tailored made Data management solution depending upon the Data landscape at the organization along with issues that they face from the data perspective. High level scope and data volumes are understood. This stage will help to come up with a project strategy. In some cases, multiple approaches may be evaluated to collaboratively arrive at a preferred approach, which requires the users buy-in & all other stake-holders.

Finalize Scope

The scope of the data management project which is outlined during the Data Landscape study is finalized. All important aspects including production data migration planning, data governance framework, acceptance & sign-off procedures, project plan are finalized in this stage.

Target-to-Source Schema Mapping

In the case of data migration projects, data elements are mapped from the source data store to target databases. Gaps between the source and target data structures are highlighted. Data transformations are defined to bridge these gaps.

Data Profiling

Data profiling is essentially checking the fitment of the source data to the target environment. ‘Fitment’ refers to the attributes of the source data, which enables it to seamlessly be migrated into the data structure of the target system without losing its usability in the target system’s application environment. Rules based on the target database attributes and business validations are defined to identify the ’polluted’ data.

Data Cleansing

Polluted data encountered during the profiling stage is either cleansed manually or using automated processes. In the presence of a non-ambiguous cleansing logic, data cleansing can be automated. This process is always carried out with the buy-in and acceptance of business teams from the customer organization.

Data Migration with Transformation

Usually a pilot phase of Data migration is carried out before the actual production data migration. About 2% of production data is used in this phase. This phase will help to test out the approach and to apply any corrections, if any for the actual production data migration. We address detailed production migration planning items like roll-back window, reconciliation procedures, user acceptance, etc.

We are innovators. We build automation tools. We support them with people & processes.

On-premise systems have been the backbone of businesses for decades. But technology & more importantly, the pace of change in technology has affected the way businesses run. We thrive on building bridges from older technologies to new ones. We use a judicious mix of forward & reverse engineering techniques to manage modernization; we make them predictable and risk-averse.