Cost effective, rule based, big data analytical aggregation engine for investment portfolios

Abstract

Recent developments in Big Data in financial industry has created a huge opportunity for design and development of effective aggregation (higher level) analytical measures (Fund, Portfolio, Sector, Industry etc.). Lack of these aggregated measures will jeopardize organization’s ability to provide the financial services promised to clients. Vendor solutions and existing academic research (Data Cube, OLAP) can provide these aggregated measures but are expensive, time consuming and not practical to implement for a small to mid-size investment organization. Our proposed solution using rule-based architecture is cost effective, efficient and building block for “Rapid Application and Decision Support Systems on Big Data”. Our new approach “Selective Dimensional Cuboids” provides a simple but robust solution with flexibility for future expansion into data mining, portfolio trend analysis and cycle forecasting. The solution is easily portable to any dimensional data set.