Financial services industry is one of the major generators and users of data. However, sourcing data from multiple sources, integrating them with the existing systems and processes and ascertaining data quality has always been a challenge for financial institutions (FIs). Therefore they are always on the lookout for solutions that can improve data quality, transform data sourcing and manage a golden copy of data.
I believe Semantics and Ontology technologies are redefining the process of sourcing and using data and information.
Semantic technologies include a set of programs that contextualize and gather information based on cognition and auto-sensing of logical and linguistic expressions. This technology wraps the underlying data models and processes to make the information responses more intuitive. The best realization of this technology application is enabled when combined with the ontological representation of the data. Ontological structures enable a domain centric view of data with correlation between the objects based on domain characteristics.
Let's see how these technologies help an asset manager interested in sourcing data for "Bonds with yield greater than 7% in APAC".
To solve this query, first the semantic engine will decode the business language and identify data elements and the relational factors. Second, the underlying semantic engine would traverse through the ontological structures to get the related bonds that satisfy the given yield and market conditions.
I believe the use of Semantics and Ontology in financial data management will lead to the following:
- Shift towards a consumer centric data consumption model
- Organizing data based on Ontological structure with a Semantic language layer
- Focus on sourcing credible data rather than ascertaining data credibility in-house
- Measure and meter usage of data consumed and pay for what is sourced
These technologies benefit financial institutions reshape their operating models and processes to bring in more data credibility and reduce the total cost of data-centric operations and ownership.
- Operating Model – The future operating model (Figure A) will aggregate data from various sources that meet the needs of the data consumers shifting the golden source of data residing in consumer’s firm to the data aggregator’s firm. Aggregators will provide data based on semantic language and maintain the data credibility.
Future Operating Model