Data migration is typically an undervalued part of the process when it comes to adopting new systems. Because the new system or application you’re adopting is often seen as the actual investment, data migration planning is often considered as a “necessary evil” of less importance. However, transferring data points from one system to another isn’t as simple as dumping information from one bucket into the next.

The main challenges that banks face during the data migration process are:

• Quality of source data

Data quality seems to be the biggest challenge for project delays and cost overruns. Most banks are not aware of such quality issues at the time of embarking on a core banking transformation. Also, they lack the expertise to take decisive action on dirty data in the legacy system. Often data quality issues are not identified till the target system fails.

• Lack of data knowledge

Lack of knowledge and understanding of the data that exist in the legacy is one of the reasons that can cause a project to fail. This is mainly because there is incomplete documentation of the legacy system data and their structure and the relationships between the data are not defined accurately. It can be all too easy to assume that your data can easily be configured into the parameters of the target system however the reality could mean critical failures when it comes to User Acceptance.

• Large volumes of data

With large volumes of data comes increased complexity. Huge data volumes increase the burden of data governance and affect data quality.

• Data Governance and business standards

Large volumes of customer data and traditional methods of mapping, typically matching exact name fields, create a wide spread of possible matches requiring extensive manual review and adding to your compliance burden. You must ensure that data fed into financial reporting and business intelligence tools is consistent and complete, while conforming to the new controls  and regulations framework.

• Mapping of data

A key challenge that most organisations face with their data migration projects is the inability to map multiple Core  Banking  legacy  systems  into  a  single  platform  system  at  the  same  point.

• Duplicate data

Legacy systems often contain multiple entries for the same customer. Depending on the bank’s requirement and the target system specifications, the data should be handled so as to avoid duplication or redundancy.

• Data Reconciliation

Although reconciliation initially is seen as a simple activity, due to a number of data transformations necessary to accommodate the internal data structures of the target system, complex business rules and large volumes of data this activity becomes complicated and onerous. Additionally, if during the data migration there is a need to consolidate legacy data the reconciliation complexity is increased significantly.

• Business-as-usual operations

A major risk that any core banking implementation faces is its effect on “business as usual” operations. An effective migration methodology enables banks to upgrade to the latest system with minimal business disruption ensuring customer satisfaction at the same time. The data migration process bears significant risks if not carried out effectively and poor data quality can delay the core banking transformation project. To succeed, data migrations must be given the attention they deserve, rather than simply being considered part of a larger underlying project. Lacking this and without proper planning, there is a high risk that the project will go over budget, exceed the allotted time, or even fail completely.   Make your Data Migration awesome with V-Conn!