RAGE Frameworks Real Time Intelligence for Supplier Risk

There are many articles and reports about using Big Data for supplier risk, but there is still confusion about what Big Data is and how exactly one moves forward. Tom Fishburne at marketcartoonist.com succinctly sums it up with this gem, “many companies struggle with small data, let alone big data.”

On Feb. 6, RAGE Frameworks presented the webinar, Real-Time Supplier Risk Surveillance: Using Big Data to Look Beyond the Financial Statement, with Nick Adams and Joy Dasgupta presenting. RAGE has also published a whitepaper on the same topic entitled “Harnessing Big Data for Supplier Risk Surveillance, Real Time.”

Learning about RAGE Frameworks Real Time Intelligence (RTI) for Supplier Risk solution is an effective way to understand how Big Data can successfully be used for supplier risk monitoring.

What this technology does is tackle the biggest headaches associated with Big Data: 1) information overload and how to aggregate large amounts of data, 2) how to assess content to determine what should be highlighted and made visible, and, 3) how to organize and present the information in a quick and easy way to digest.

In the webinar, RAGE refers to structured and unstructured data as the “two frontiers of Big Data.” RTI is able to combine analysis from structured data (financial documents, credit bureau information, subscription providers), and unstructured data (text in media, blogs, social media), in addition to internal sources.

RTI works by trawling the web, looking for data, both structured and unstructured, and then interpreting and identifying “what is relevant and what amongst the relevant is most important.”

How the content gets interpreted and assessed is key. RTI assessments are based on detailed ontological models. The Content Assessment Engine uses “linguistic rules for the various categories in the ontologies and the relationship strengths in the ontologies to rate content. Every article is first tested for relevance to the topic of ‘supplier risk’ as defined in the customizable ontology and then assessed for impact. The impact is scored on a 5-point scale.” For example, in the case of Sharp, the event of “Sharp Rises After Report of Job Cuts, Board Revamp,” receives a ‘Positive’ impact score with a rating of 0.3. The event of “Sharp Close to Bankruptcy” receives a ‘strongly negative’ impact score with a rating of -1.5.

Visual impact scoring is graphically presented in a way that is easy to interpret and use.


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