Big Data BI Reporting and Dashboard Solution
Executive Summary / Key Highlights:
- One of the flagship programs of our client
- One of the leading global financial institutions
- Teams between Boston, Pune, and Poland
- The aim of the project was to create an insightful and actionable Big Data powered BI solution
- Real time predictive analytics that helps in preventing issues and problems
- Help clients solve their business problems by providing deep, meaningful, and interpretable business insights using advanced analytics and machine learning techniques to directly aid our clients in their decision-making process to solve pressing business challenges and problems related to operations, finance, and IT.
The Client: One of the world's largest asset management and financial services institutions.
Problem Statement: Our client wanted to improve daily work with new technologies. The goal was to create a solution that will capture, analyze and present data from various business areas within the customer’s organization to provide insight to users based on their needs and requirements. The project had to be broken down into 12 smaller projects as it covered lots of areas and took care of complex data processing.
Solution: We can break down the solution into 4 areas:
Data Analysis: Our team created a centralized data analysis structure to standardize on tools, reporting/dashboards and data sources
Business Intelligence: For business and technical SLAs we created a business intelligence solution based on existing processes. It allowed tracking progress against specific SLAs within the critical pricing window of 4 to 7 PM for operational and management reporting. In addition to that, we decided to leverage Machine Learning to predict behavior and identify areas of opportunity.
Adoption Monitoring & Opportunity Identification: The software was created to track and trend adoption and optimization of new functionalities
Data Acquisition & Processing: Our team has developed a Data Ecosystem that will be utilized for data analysis across different processes, applications and platforms
During the project:
To bring the most value we focused on the real-time transaction and system monitoring, this helped to get valuable data and present it in a user-friendly way.
To improve all the processes we did a thorough business analysis, our team worked on aligning the systems and applications. Resiliency was another key aspect, to put it at the highest level we used the follow-the-sun model for incident data capture and tagging. Together with the resiliency team we worked on the tagging process and created new resiliency dashboards. To map the SLA performance and reporting we created a system to capture SLAs and source data for relevant value streams. In the dashboard, it can be sorted by client, delivery type, region, division and system.
Results: The Big Data Ecosystem works to identify various deep learning techniques suitable for business requirements and drive digital transformation. Machine Learning algorithms help the organization to make quick business decisions. The whole solution can now provide data around missed SLA deliveries caused by IT-related incidents. The data science implementation within the system is continuously looking to improve the funds processing timeline and using advanced scientific technologies like artificial intelligence, deep learning and machine learning to find and predict the factors affecting processing time.