Our customer is a U.S. national bank that had multiple siloed internal systems supporting different functions across the bank, at times hosting contradictory data. Reporting was becoming an inefficient and complicated task requiring manual data gathering from disparate systems, often producing inconsistent results.
The bank also had a few spreadsheet-based tracking processes where data was being gathered and updated by users manually — instead, they needed a better tool for information management which would keep track of all historical changes and prevent unauthorized/erroneous data updates.
In collaboration with the bank’s business analysts, Kagesoft developed a deep understanding of the data domain, leading to the creation of the optimal data structure to support the bank’s business needs. In addition, the bank had historical data that needed to be cleaned and migrated to the new environment.
Besides integration with the existing internal systems, several new external data sources were added to the developed solution, expanding the bank’s capabilities, eliminating data silos and duplicate data across the bank as well as automating manual work.
The new data warehouse consolidates data from multiple sources on a daily basis and includes complex business rules and data integrity checks as well as a web portal for business users that serves as a tool for:
- administrators to configure system settings, schedule data loads based on dependency rules and monitor user activity;
- business users to view and update data in the manually maintained tables. The portal incorporates data integrity checks and enforces data entry rules with field interdependencies, greatly reducing the risk of erroneous input.
The user-friendly, clean and logical interface of the web portal presents a subset of functionality to users based on their assigned roles, eliminating confusion and decreasing the time needed to get users trained on the system.
The data warehouse includes audit functionality: all data updates are recorded with the ability to revert changes if needed.
- Consolidation of all pertinent data in the data warehouse;
- Elimination of data inconsistencies and creation of a single source of truth;
- Increase in operations efficiency through reporting automation and implementation of business intelligence tools;
- Estimated payback period — 1.5 years.
The new data warehouse has become a system of record, with data being used not only for day-to-day operational decisions, but also for regulatory reporting and strategic planning purposes.
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