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Data warehouse solution for Lithuanian bank
Šiaulių Bankas has announced its 2024-2029 strategy, aiming to become Lithuania’s top bank by 2029. The bank plans to lead in customer experience, be the first choice for consumers, grow faster than the market in customer base and loan portfolio.
Their strategic transformation demanded external IT support, especially for data solutions. Discover how we implemented the Data warehouse (DWH) and what value it created for the client.
Situation
The project aimed to modernise the existing reporting and data processing for the client. Šiaulių Bankas had such needs not only for the bank itself but also for its subsidiaries, including leasing (SB Lizingas), insurance (SB Draudimas), real estate management (SB Turto Fondas), and other companies.
Our initial proposal addressed the business need for new reports by questioning data sources and confirming we could extract needed data from existing systems. The client wanted to have flexible dashboarding: stay easily up to date on the current situation and gain clear insights into trends. Following this, we implemented a Proof of Concept that evolved into an Azure-based DWH solution.
Solution
Our team implemented a DWH solution using Azure cloud services, including Azure Data Factory, to support Šiaulių Bankas in modernising its data processing and reporting capabilities. We built a scalable platform that connects critical data sources from the main bank entity and its subsidiaries into a centralised DWH for streamlined data processing.
We provided a range of data services, including integration and engineering to unify various data systems, plus BI and reporting, with a focus on data modelling. Our team established data management tools through Power Apps, enabling straightforward data handling within the DWH setup without additional visualisation components. This structured approach delivers a cohesive platform aligned with the client’s data processing objectives.
Added value
The executed solution is seamlessly integrated with the existing DWH using a lake-house model, functioning as a powerful engine for report generation. This system adapts to new data sources without requiring infrastructure changes, offering flexibility and scalability for future data needs.
Data management processes are now more organised. However, there is an ongoing need to clarify roles – specifically, defining responsibilities between data owners and clients when setting requirements. For example, a monthly report on new clients revealed differing interpretations of terms, but consistency is improving. We continue to support the client with ongoing tasks.
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