Today, business and technology are inextricably linked. And keeping pace with the emerging technology landscape can be difficult for even the most tech-savvy leaders. This case study dives deep into the challenges faced by the client in terms of manual and time-consuming financial processes, lack of data insights, and increased risk of errors followed by describing the solutions provided by Cognine that enhanced the overall functional efficiency of the client.
Client is an American financial technology company that creates and provides financial advisors with wealth management tools and products. Their flagship product is an advisory platform that integrates the services and software used by financial advisors in wealth management.
- Client receives the customer data related to transactions, accounts, open positions, and other financial data in various formats from custodians such as Pershing, TD Ameritrade, Schwab etc. which made it difficult to analyze, understand and reuse it.
- Client receives more than 100 GB of incremental data on daily basis on to their on-prem servers which has computing challenges.
- Most of the reports and metrics were shown in a tabular format, which were not providing enough insights and were not supportive for decision making.
- Ensuring data quality of the large sets of incremental data had become cumbersome and was affecting downstream systems.
- There were architectural issues causing latency in the conversion of raw data into customer consumable data.
Solutions Provided by Cognine:
- Cognine provided a highly scalable and robust architecture that supports various file formats with different sizes & data volumes which automated and improved client’s financial workflows, real-time data visibility.
- The new multi-layer (staging, standardized, curated) and multi-tenant architecture supports ETL operations to run parallel and different teams can access data at different layers based on their needs.
- An event driven micro services architecture ensured the data load into targets without delay. Implemented configurations at environment and object level, such that very minimal changes are needed during the deployment.
- Embedded Collibra DQ to monitor the DWH jobs and notify users based on the requirement.
- Cognine analyzed the business requirements for their analytical needs, designed data models and developed reports. Implemented CI/CD functionality for auto deployment processes.
Technology Stack Used:
Improved Data Accuracy And Increased Productivity:
Cognine successfully automated Normalization of files through UI. With the help of React UI the user can upload file metadata information and download the Normalizer file. Previously, each Normalizer file required manual development. Post automation, using the new UI, users can develop up to 20 Normalizer files at one time which saved user’s time largely and boosted efficiency.
Reduced Processing time:
We introduced a centralized library for standard logging where we implemented logging format for different steps and saved them into CloudWatch.
Quantitative Data Management:
There was a huge scale-down on manual intervention on client-specific data extraction.
Quick Decision Making:
Data insights fostered the client to take quick decisions based on the highly sophisticated visuals
- Data insights are helping the business to take quick decisions based on the highly sophisticated visuals.
- Manual intervention on client specific data extraction came down extensively as we were able to automate the functionalities.
- Automating the on-boarding & processing of new client requests was made as easy as just a click and go. This is helping to scale up client’s operations. The marketing team can utilize this to grab the attention of new prospects.
- Latency of accessing data from Analytical system has been reduced tremendously, it was scaled down to multi-folded level.
- The cost of the DWH layer has been reduced nearly half per month. This gave financial freedom to the business.
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