Finding Value in Semantic Visualization

The reference data world is experiencing constant change. Firms that don't use reference data to get a competitive edge are turning to more economically viable business models, such as reference data managed services or utilities. For companies using reference data to contribute to revenue generation or competitive advantage, the evolution of data management technologies often enables them to build more efficient, future-proof, dynamic and flexible systems allowing for better data governance and quality.

Semantic technology increasingly is a basis for next-generation data management systems, powered by a growing support in enterprise-level technology platforms. Ontologies, which play a role in data models and metadata in the semantic world, allow for multi-dimensional, multi-faceted views. Ontologies capture data definitions, structures, relations and links between objects. Applying the semantic web approach to reference data dynamically and flexibly defines and changes data models.

One of the main drivers for selecting semantic technologies as a basis for reference data systems is a need to address business changes reflected in data models in a timely manner. These changes may be caused by introduction of new financial instrument types or attributes or a need to rapidly integrate with new internal or external data systems or sources. Even if the need is not evident in the very beginning, data model flexibility is one of the key characteristics enabling long-term system survival.

Visualization Support

A growing number of banks and data vendors are making use of semantic technologies in data management. Even when the semantic aspect is not exposed to external users, it may well be there. Some companies are working toward supporting the Financial Industry Business Ontology (FIBO) - a proposed ontology standard; others go beyond FIBO, combining multiple data models and taxonomies in their internal ontology.

The Basel Committee on Banking Supervision's principles for effective risk data aggregation and risk reporting is a regulatory driver for reviewing and re-designing reference data systems. These principles contain integrated data taxonomies (including metadata) and data architectures across banking groups as a data architecture principle. Transition from siloed departments' data systems to a federated data management landscape powered by semantic technologies could be one of the best ways to move forward.

Ontologies and semantic technologies allow for smarter dynamic system logic and bring functional requirements for data models or ontology governance to a new level. However, semantic web technologies still lack good and simple data and metadata visualization methods that are crucial to greater adoption in the industry.

In the semantic world, data is not viewed in columns and rows; it is in multi-dimensional graphs customizable to show various aspects. Such data graphs are a natural fit for innovative and interactive data visualization technologies. The technology is making its way into various business functions of financial firms, outside of a semantic data space, allowing business users and data specialists to more fluidly interact with and manage data.

Evolving Techniques

Recent data visualization trends included a move to improve users' efficiency with advanced interactive data visualizations, like dashboards or innovative charts for multi-dimensional data. The last few years saw both the emergence and decline of 3-D data visualization, as it became clear that data is more malleable and easier to understand in two dimensions, especially with interactive visualizations.

From a technology perspective, these solutions are sometimes powered by enterprise business intelligence and visualization platforms sourcing data from data warehouses, or they may be developed be-spoke, often with new generation of cross-platform open source data visualization libraries, such as D3.js and Raphael. The latter option supports increasingly popular enterprise mobile applications allowing users to view data with the same flexibility and convenience seen in consumer applications.

These evolving data visualization techniques allow firms to extract better value from the use of semantic or semantically enriched traditional data. Many of these techniques are agnostic to the underlying data management landscape and have a great potential for customization. This use is not limited to semantic data visualization for data management or exploration purposes, but also includes presentation of traditional data structures, such as tabular market data, but in an ontology-aware manner, thus enabling a new level of user interaction.