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Big Data Analytics As A Service, With An Ontology For Cross-Platform Analysis At Its Core

By   /  March 22, 2013  /  No Comments

Oversight Systems is in the business of Big Data analytics. Come June, it also will be in the business of having its technology serve as a platform behind third-party business intelligence and analytics applications on-demand – including its ontology approach for integrating data from disparate enterprise systems.

The company currently provides packaged solutions that let front-line employees involved in processes such as procure-to-pay or order-to-cash conduct continuous transaction analysis for insights into transactions that violate business rules, so that the business can take action to close gaps and assure compliance to operational and regulatory requirements. The ontology it’s developed over the years, which includes proprietary semantic and relationship information and infers some additional information, is there to help with the acquisition and preparation of data.

Converting transactions from internal heterogeneous systems, as well as external data sets, into a common ontology for cross-platform analysis underlies the data analysis. “If you can’t get the data, then nothing matters. You can’t analyze it,” says Manish Singh, director of corporate development at Oversight Systems. “So, the first barrier is to effectively get the data together and then analyze it, and do it in an automated fashion.”

Every ERP system, for example, has a unique way of defining a vendor. “Our ontology has a common definition for how a vendor should be defined, and then we take the definitions of how it is defined in SAP or Oracle and map that into a common data model, so we have one view of what a vendor should be regardless of how it is in each system.

So, customers can maintain their individual systems but still get visibility into those environments supporting the underlying business process because of that ontology,” he says. The software uses pre-defined extractors to get data from the multiple systems, and the data model to which it consolidates that represents the business process; this way, the analysis is tied to the process and not the source systems, he says.

The company’s ontology is focused on business’ core financial processes, though it also has developed custom implementations to configure it for customers involved in more niche activities like energy trading so that they, too, can have visibility into areas unique to their operations. That’s where Oversight sees partners coming into the mix with third-party analytics solutions built on its platform, Singh says.

“There is a lot of opportunity for partners to develop and extend the ontology into niche areas that set up for the analysis and types of transactions where we don’t have expertise,” he says. Rapid deployment of new ontologies by partners is possible, he says, because they can layer on top of what’s already there vs. building from scratch each time. “Our concept of rapidly deployed ontologies is the ability to have an ecosystem of partners,” he says.

Partners will be able to deploy their own applications to Oversight’s cloud, and customers can come in and subscribe to those applications and upload data that’s of interest to them against those applications.

About the author

Jennifer Zaino is a New York-based freelance writer specializing in business and technology journalism. She has been an executive editor at leading technology publications, including InformationWeek, where she spearheaded an award-winning news section, and Network Computing, where she helped develop online content strategies including review exclusives and analyst reports. Her freelance credentials include being a regular contributor of original content to The Semantic Web Blog; acting as a contributing writer to RFID Journal; and serving as executive editor at the Smart Architect Smart Enterprise Exchange group. Her work also has appeared in publications and on web sites including EdTech (K-12 and Higher Ed), Ingram Micro Channel Advisor, The CMO Site, and Federal Computer Week.

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