Aggregating financial services data without assumptions: A semantic data reference architecture

Abstract

We are seeing a sea change down the pike in terms of financial information aggregation and consumption; this could potentially be a game changer in financial services space with focus on ability to commoditize data. Financial Services Industry deals with a tremendous amount of data that varies in its structure, volume and purpose. The data is generated in the ecosystem (its customers, its own accounts, partner trades, securities transactions etc.), is handled by many systems - each having its own perspective. Front-office systems handle transactional behavior of the data, middle office systems which typically work with a drop-copy of the data subject it to intense processing, business logic, computations (such as inventory positions, fee calculations, commissions) and the back office systems deal with reconciliation, cleansing, exception management etc. Then there are the analytic systems which are concerned with auditing, compliance reporting as well as business analytics. Data that flows through this ecosystem gets aggregated, transformed, and transported time and again. Traditional approaches to managing such data leverage Extract-Transform-Load (ETL) technologies to set up data marts where each data mart serves a specific purpose (such as reconciliation or analytics). The result is proliferation of transformations and marts in the Organization. The need is to have architectures and IT systems that can aggregate data from many such sources without making any assumptions on HOW, WHERE or WHEN this data will be used. The incoming data is semantically annotated and stored in the triple store within storage tier and offers the ability to store, query and draw inferences using the ontology. There is a probable need for a Big Data Solution here that helps ease data liberation and co-location. This paper is a summary of one such business case of the Financial Services Industry where traditional ETL silos was broken to support the structurally dynamic, ever expanding and changing data usage needs employing Ontology and Semantic techniques like RDF/RDFS, SPARQL, OWL and related stack.