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(CFOZone) By Denise Bedell-Bleeker
Semantic technologies are helping innumerable companies across various different sectors - such as healthcare, retail, government systems, and manufacturing - improve data mining and integration. Improved data management and integration is a key goal for most corporate finance executives, however within the corporate financial technology realm the use of semantic methodologies to drive data mining has encountered a great deal of resistance.
At present, it takes significant investment to adopt semantic technologies as a result of the complexity and depth of information mining and connectivity.
Semantic technologies are all about interoperability and data management. On a broad scale, semantic methodologies were developed to provide the World Wide Web with a way to integrate and combine data from extremely diverse sources, rather than simply allowing an exchange of documents housed in data silos. While most traditional techniques for data mining involve a keyword search, semantic approaches mine for concepts and relational data to understand the meaning of a particular piece of text and use that to more effectively interrelate information across data sets. The web of data made available online is known as the Semantic Web and is based on the Resource Description Framework (RDF) model for data interchange.
Cost and complexity aren't the only things keeping finance departments from adopting the technology. Data integration and business intelligence within the financial technology realm are relatively saturated markets and thus difficult to break into. Finally, competitors within the space argue that semantic technologies lack the tools to help business users make sense of the wealth of data made available - in other words, they provide too much information.
This view is slowly changing, though, and semantic methodologies are now found layered within everything from ERPs to reporting and control systems, and finance execs may not even realize that they are using semantic technologies. Semantic data management solutions are used by companies ranging from Verizon and Téléfonica to Eli Lilly, Genentech and Lockheed Martin, and semantic technology software providers now offer a wide range of solutions of use to the finance department of a company.
Semantic methodologies can be used on a smaller scale to mine and interrelate existing data sets for various specific functions. For example, for corporate investment managers dealing with a wide array of data and information for any given trade, a data management solution using a semantic backbone could more effectively and quickly search though a vast array of documents and extract specific information based on the concepts and related key words, saving countless hours of research.
Semantic methodologies are also beginning to permeate the enterprise system market, with Oracle Enterprise Edition including RDF support and increasingly being included within other Oracle apps. And semantic solution providers are targeting the lower-end analytics market, providing data mining solutions to manage a vast array of data sets, including - much to the delight of many companies - Excel spreadsheet management. For the corporate finance director or CFO, such semantic technology solutions offer the chance to take existing systems and solutions and layer on a solution to manage all of that data in a deeply explorable way, without the need for extensive data mapping.
Nonetheless, it still only underlies a tiny portion of the corporate financial tech market. It will be some time, if ever, before semantic technology becomes a central part of the financial technology market as its merits are better understood and its costs rationalized.
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