BLOG – September 2023
How Financial Institutions Can Profit From Smoother Data Migrations
Fear of data migrations is a common complaint among financial institutions of all stripes. And as we noted in an earlier blog in this series, that fear is hobbling their ability to modernise and grow.
Data migration difficulties make moving from one IT system to another a perilous undertaking, fraught with financial, operational and reputational risk. Yet keeping pace with technology innovation is essential if firms are to automate and digitalise their service offerings in line with the needs of the modern marketplace.
Ease of migration opens up world of possibilities
By eliminating – or at least minimising – data migration fears, institutions gain the freedom and confidence to pursue the business strategy they, and their clients, want.
Knowing they can adapt their IT infrastructure as required to deliver the relevant business support creates more nimble firms. It adds real client and enterprise value by allowing them to:
- Grow through acquisition
- Diversify into new customer segments
- Expand their product sets
- Access opportunities in new markets
- Drive operating efficiencies
- Stay compliant.
The opposite is also true. Avoiding a technology upgrade or new system implementation because they are too scared of the consequences, or only undertaking one out of desperation when they face no alternative, restricts financial institutions’ potential to change and grow.
Clean data that allows for faster and simpler system migrations can also help where companies want to be acquired. Having a solid data platform that isn’t riddled with errors will make them a more attractive prospect for prospective suitors. And any subsequent M&A transaction stands a better chance of success.
The clean data solution to minimise migration pain
The key to a successful IT migration lies in understanding what data you have at the outset. Getting a clear picture of the state of the dataset at an early stage by identifying any discrepancies and missing elements enables firms to enhance the quality, integrity and consistency of their data before it is moved from point A to point B. That minimises the data errors and duplication that come across during the transition. Clean, uniform data also tightens the mapping to the new platform and makes it less complex to implement.
Focus and control are essential. Troubled system implementations often occur where no one in the organisation takes full responsibility for the data clean-up and migration.
In many cases, institutions hope their software company will take charge of bringing the data over during the system implementation. Growing numbers of software vendors, wary of being blamed for any failed migrations, will no longer start the implementation though until there has been a sufficient degree of data validation. Increasingly we’re seeing vendors outsource the data migration process to third parties, creating an additional layer of cost where data quality is poor.
One (common) approach financial institutions employ to achieve the requisite data validation is to commit bodies to the data cleansing tasks – often by running spreadsheets and comparing them against downloaded data and data scripts to check for discrepancies. By dedicating people to the mapping they then aim to ensure all the necessary data translates across and goes where it should.
The downside is that takes time and consumes valuable resources. And it introduces the potential for human error. Even where it seems everything has been correctly mapped, rogue lines of data that put a spanner in the works are inevitable.
Automated data quality tools offer a faster, more reliable alternative. An automated system removes the expense and variability of human checking. Data errors, inconsistencies and duplication can be identified much quicker, enabling staff to concentrate their efforts on resolving issues. By seeing where and why an issue emerged and how to fix it, users can avoid repeating the same mistakes. For acquiring firms in M&A transactions, automated checks can also evidence where data problems lie and ease the integration effort.
Plus an automated data quality system is always on. It offers institutions a 24/7 data analyst that takes no holidays, requires no training and makes no mistakes. And the business opportunities it opens up will be invaluable.