Snowflake, Kafka, Python
Our client is one of the top-tier Fintech companies in the Middle East that provides innovative financial technology solutions and services to their customers, while also complying with regulatory requirements and ensuring the security of financial transactions.
Our client needed to consolidate data sources and use them to drive better business outcomes to facilitate raising awareness of the value of data within the company and increase overall data maturity. This can lead to improved business results and a better understanding of how data can be used to make better decisions.
Like any other Fintech organization, our client was facing difficulty in managing massive amounts of data related to financial transactions and customer support.
It is critical to have a clear data management strategy in place to prevent service interruptions brought on by poor planning.
Our client was unable to conduct a thorough analysis of the situation from all angles, the decision-makers in the organisation relied on their intuition and made choices based on gut feelings.
1. The bank data from different sources such as MySQL, Five9, GENESYS and some bank APIs were pulled using Python.
2. Data preparation and necessary data transformations were also applied using Python.
3. After data transformation, the data was pushed to Snowflake as a unified source for all banking data.
By unifying multiple data sources into a single platform and consolidating data from various sources, our client was able to reduce complexity and eliminate duplication, which in turn saved time and resources. This means that teams were able to focus on delivering the right mix of products, services, and loyalty program offers to customers, which improved customer satisfaction and loyalty.
Through raising awareness of data value and maturity within the organization and providing a centralized platform for data management and analysis, our client got a better understanding of the importance of data in making informed business decisions. This has helped to drive a data-driven culture within the organization and ultimately led to better outcomes.
By allocating resources based on utilization, our client was able to ensure that the customers stood happy and received the right level of support. Additionally, having a standardized pipeline allows for scalability and expansion into different regions, making it easier for businesses to grow and adapt to changing market conditions. The implementation of this pipeline has saved hundreds of man-hours. Finally, the use of effective dashboards allows our client to make informed decisions based on real-time data, further improving efficiency and customer satisfaction.