CASE STUDY
Additionally, data quality validation was initially handled through manual scripts within IT. Transitioning to a centralised, self-service model required careful design of governance policies, automation workflows and access control to ensure both agility and compliance.
How did Informatica work with Krungsri in implementing the solution?
Informatica adopted a co-creation with local partners and enablement approach rather than a traditional vendor-client model. We worked hand-in-hand with Krungsri’ s data governance and data quality teams to customise implementation, design reusable frameworks and align with their data strategy roadmap.
users are now able to accomplish much more on their own, reducing their reliance on IT.
Where do you see the partnership with Informatica taking Krungsri in the future?
We envision the expansion of usage to cover more critical data elements, implementing additional data quality rules and integrating more data sources. We would like the portal to become the first resource that our data users – whether business analysts, data scientists or others – turn to when faced with a business problem.
Additionally, we aim for our team to fully maximise the capabilities of Informatica, including its AI capabilities. This will require our team to understand the Informatica product better so that it can be seamlessly embedded and integrated into our processes and way of thinking. This also means that Informatica will need to work closely with our team to ensure understanding of both existing and new features.
We also built joint governance playbooks and created reference architectures for metadata and lineage management. This ensured that ownership and operational knowledge stayed within Krungsri – a sustainable model that allows the bank to continue expanding its data capabilities independently.
What future technical requirements does Informatica foresee evolving over the life of the partnership?
Looking ahead, we see AI-driven automation and AI governance as the next frontier. Informatica is already embedding AI governance and capabilities to automate metadata discovery, suggest quality rules and detect anomalies in real time.
We also anticipate the need for broader data democratisation – empowering more business users to access governed data confidently, supported by data privacy and classification automation.
Finally, integration with multi-cloud and hybrid ecosystems will become increasingly vital. We plan to have tighter interoperability with Krungsri’ s enterprise data lake and analytics stack, ensuring the foundation remains flexible, scalable and future-ready. p
Steven Seah
What were the technical challenges inherent in implementing the Informatica solution with Krungsri?
One of the key challenges was harmonising multiple legacy systems and data sources that had evolved independently over time. The Informatica team needed to ensure consistent metadata structures, data lineage tracking and rule definitions across disparate environments.
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