Deloitte Migration Accelerator

At a glance
Deloitte Migration Accelerator is a highly flexible tool used for data migration to core banking systems. It has been used by a considerable number of Deloitte’s clients globally, and is now ready to support a migration to Thought Machine’s Vault.
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The challenge

The transition of core banking systems is often challenging for banks. At Deloitte, we believe it requires a dedicated approach, as standard data migration solutions are rare. Banks work with large volumes of disparate data on a daily basis.  After years of expanding infrastructure, extending offers and growing by acquisition, data are often distributed in multiple legacy systems with missing documentation and technical knowledge. Each system stores data in a disparate manner. Often there is no mechanism in place allowing the identification of duplicated information.

Modern systems do not rely on relational databases anymore. Instead, data are stored as objects or even as states. Migrating directly through a database interface is no longer a valid option. Dedicated APIs for migration messages must be used as the data must undergo a system validation process that verifies internal dependencies and data quality, often with the same scrutiny as regular user input.

Today, time-sensitive projects require a fast migration process alongside functional implementation. This approach increases the complexity of the migration development and testing, which cannot wait for the completion of all functional solutions. Migration and functional design teams must tightly cooperate and maintain a constant feedback loop to ensure that the system design includes data migration related challenges. Early execution of migration runs is essential to validate the current solutions with real data and minimise the impact of migration on functional system design, which may be a substantial hidden cost item if detected later during the project.

Common ETL (extract, transform, load) tools are often insufficient to address these challenges and ensure successful migration according to the project plan.

The solution

Deloitte Migration Accelerator was built based on our extensive experience in core banking migration projects and used during multiple migrations. Core design principles allow our clients to smoothly transition to new systems. We have focused on the successful execution of migration runs despite imperfect conditions in early project stages when most functional solutions are not yet fully ready and require the flexibility of the migration process adjustment. Owing to our migration accelerator, even first migration runs will be finalised within the schedule, and the migrated data will exist in the target system. Bank’s personnel will be able to start validating the migration and learn the use of the new system and its functional solutions right after the first migration run. Any changes being part of the provided development services can be requested at early project stages when the cost of their implementation is still low. Using Deloitte Migration Accelerator not only minimises costs and reduces time but also helps with the functional transition.

Image of Deloitte migration accelerator

Easy to use – it allows data conversion without coding

Cost-effective - there is no licensing involved

Transparent in terms of purchase, cost of deployment, cost of service maintenance, acquiring the related knowledge

Developed with advisory input from Thought Machine, which brings additional benefits, such as no need to customise

Tried and tested with a considerable number of Deloitte clients

Architectural diagram of Deloitte migration accelerator
How the solution works

Deloitte Migration Accelerator is much more than just an ETL tool. It’s a highly flexible and proven migration engine dedicated to supporting core banking system migration projects.

Key differentiations of Deloitte’s tool are:

·   Data transformation and reconciliation rules are described using XML parameterisation templates. They do not require compilation and deployment. Transformation rules may be modified within minutes and reloaded to Deloitte Migration Accelerator (DMA), the same as migration fixes and workarounds.

·   Business and technical mapping products are stored in dictionaries, which are easily understandable for businesspeople. Invalid or missing values will trigger warnings that allow the modification of dictionaries during migration testing.

·   Generators create unique target system identifiers and remember their linkage to legacy records. Each repetition of data transformation will assign the same target identifiers if required. It also allows updating the existing data if required, as the tool “knows” how the legacy IDs are translated to IDs in the new system.

·   Using the ID generation mechanism available in Deloitte Migration Accelerator prevents the serialisation of the target system processing, which often impacts data load performance.

·   Dependency rules can be implemented as part of load processing. The Migration Accelerator can automatically track the order of loading messages. If one message in a sequence fails, following messages relating to the same object will not be sent to avoid generating unnecessary issues. For example, an account belonging to a customer will not be opened if a customer creation has failed or posting to an account will not take place if the account is not opened.

·   Reload and retry functionality allows reverting failed migration objects back to the transformation stage, where they can be retransformed with new rules or mapping processes if required. There is no need to retransform full sets of data; instead, all or selected rejected messages can be retried.

·   Before starting the full migration, sampling can be executed. Deloitte Migration Accelerator operators can choose a set of smaller migration batches at random or applying other criteria, and try to transform and load them. This is especially useful when checking whether known issues from previous migration executions have already been resolved. If not, a known fix or workaround can be quickly applied.

·   Data load mechanisms are implemented as plugins outside of the transformation engine. Such an approach allows efficient integration of new target systems without affecting functionalities that have been already provided.

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