Use of Predictive Analysis to Convert Portfolio Investments through Enterprise Data Management Strategy

A growing online global trading platform was unable to use a wide range of data that they collected through diverse investment portfolios. Those data were considerable enough to understand the investment patterns, portfolio preferences, and future prospects. But they needed a unified solution that could provide them with a cookie-cutter approach to put their data in use for better decision making. Check out the case study to find out how Glorious Insight Enterprise Data Management Solutions helped them make use of such a large chunk of data. 

                                                     

Customer Background 

A highly prominent and reliable global trading platform with diverse portfolios for investment had been performing spectacularly in the market. But most of their transactional data of the clients were lying hidden in silos for exploration to further amplify their performance. Lying on such a treasure trove of data that could maximize sales outcomes by 5x to 10x times was bothering their management. 

As a result, they collaborated with Glorious Insight to help explore the data stored in silos and put it to use for marketing and sales purposes. Such a large chunk of data lying hidden in different sources required a scalable and agile approach to address multiple issues that the organization was facing. That’s where the role of data lake and data warehouses helped the client reach a satisfactory conclusion.  

 

Challenges 

As an online global trading platform, the client had maximum visitors that had opted for the platform either for investments or inquiry. With such a large chunk of data, it was hard to set-up a strategy of follow-ups, customer pitching, and marketing. Realizing that such a large chunk of data could further help the organization dominate the competition, they approach us, Glorious Insight, and asked a way-out to explore the colossal data and draw actionable insights from the same. Their core purpose was to put the data into a decision-making model, where they can identify prospects based on their queries, investment patterns, and market analytics influencing their decision making in buying or selling portfolios. Such a pin-pointed approach required a clinical approach because;

 

  • Data was too difficult to locate and access since it had accumulated a lot over time. 
  • There was a lack of data integrity because of poor data lineage tracking 
  • The Discovery of self-service data was nearly impossible.
  • Data entitlement and access controls were not adequate or as per the business requirements 
  • Inconsistency in the data model did not allow integrating an efficient data model for predictive insights.

 

Solution 

Glorious Insight recommended deploying data management strategies that could easily help set up a data governance model. Such an approach would help in making data accessible, usable, accurate, and highly secure. By using data governance and data architecture frameworks and maturity models, Glorious Insight helped the client perform the following functions; 

  • Carefully analyze the data landscape 
  • Determine the current state data model 
  • Determine the current data process, systems, and lineage 
  • Set-up a strategic vision for firm-wide data management 
  • Develop high-level target data architecture and scalable governance models 
  • Help set-up strategies streamlined via specific road maps to deliver the business objectives. 

 

Results

Glorious Insight used power BI to help make use of data warehouses for actionable claims. As the operational data was consolidated at one single access point, it enhanced data integrity, consistency, and lineage tracking. In this way, business operational activities were improved. Data-driven business value was generated using cloud-hosted data lakes that made business decisions swifter and precise.