Data Migration

Digiata offers financial services companies the best of both worlds. First, an industry-leading comprehensive data migration framework that is based on two decades of experience. Then, Digiata works closely with its customer’s in-house experts and the target system vendor to define a comprehensive, customised data migration plan. Any Digiata data migration is underpinned by the ability to move data from any source, to any target, any way.

Data Migration Framework

Timeline

2021
Jun 30

Engagement

Understand the high-level challenges and requirements for the migration. Define sources, targets and tools. Formulate the policies that will regulate the data governance standards to be followed during the entire data migration process.
Jun 29

Discovery

Formulate the detailed requirements and migration approach. Carry out data acquisition planning, data sensitivity classification and business rule categorisation. Supported by compliance strategies that provide guidance regarding data sensitivity, fraud, client privacy and illegal data transfer.
Jun 28

Analysis & Design

Analyse and examine the data from sources, document the data dictionary and map the source to target fields. Business and transformation rules are identified and validated in conjunction with backup and archiving strategy planning.
Jun 27

Configuration & Analytics

Implementation of the automated migration solution. Data load processes are configured, business and transformation rules are implemented and reconciliation reports are built. Data models are constructed for data analytics and dashboards, reinforced by data governance and compliance strategies.
Jun 26

Assurance

Run and repeat. Test migrations and parallel run reconciliations are executed multiple times and defects found in reconciliations are managed and addressed. Rehearsals are done to determine the exact timing of a migration run and mock reconciliation sign-offs are fulfilled to re-enact the processes to be followed on production day.
Jun 25

Deployment

Final migration run to move source data to the target system within the production environment. Full sign-off through reconciliation reports and target system testing is done. Decommissioning of the legacy systems.

TARGET

Data Migration

by the numbers

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TOTAL RECORDS MIGRATED
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COUNTRIES
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SATISFIED CLIENTS
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PROJECTS COMPLETED

Any Source, any Target, with any Tool

Sources

Sources

Tools

Tools-Color

Targets

Targets-color

Case Studies

Core banking system data migrations

Digiata’s customer, a tier one banking financial services company headquartered in South Africa and operating in more than 16 countries across Africa, embarked on a project to replace multiple existing core banking…

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Centralised AML compliance

Digiata’s customer, a tier one banking financial services company, tapped into Digiata’s data migration expertise and experience to support the migration of millions of records every day to meet its AML obligations.…

Read Article

FAQ

While we have preferred tools, our data migration framework is tool agnostic. Based on the specific requirements of your data migration needs, the best data migration tool will be used for migrating your data.

All data migration projects are different, and the duration of a data migration project is determined by different factors, such as – number of sources, quality of the data and the level of unstructured data in the sources.

Yes, Digiata is equipped with the necessary skills and required tools to allow data migration to be done from multiple different source systems providing data in a variety of different formats.

Any data flagged as PII (Personally Identifiable Information) will be kept secure throughout the data migration process by means of Dynamic Data Masking and/or encryption.

Yes, Data cleansing will form part of the migration process and can be done using three different approaches; at source, in-flight and at target. We will guide you on the best approach for your project.

Identification and handling of duplicate records is done by custom rules implemented in our migration process.

Multiple times. The number of test/dry runs will be dependent on the data quality at source, the amount and complexity of transformation applied and the migration strategy which could be either big bang or campaign based. A typical data migration project will plan for between 3 and 5 test/dry runs before the final migration is executed.

Testimonials