Simply put, Rage changes everything about how we contextualize and bring new types of information into our risk analyses to drive better predictive analysis (for past coverage of Rage, see links at the end of this post).
Rage Frameworks is a true big data analytics platform – along with a business process management (BPM) module – that has a supplier risk module built on top of it. What makes Rage so unique is its semantic intelligence capabilities that are able to mine, classify, and interpret unstructured text using natural language processing built within specific ontologies (knowledge domains), and then provide predictive analytics and heat maps based on those analytics.
This includes taking text-based financials statements and then converting them to financial ratios such as an Altman Z score – and associated prediction on it. Moreover, Rage factors into account that risk is contextual – and the toolset lets users define their own context! This stands in contrast to other risk assessment firms that typically spoon-feed clients their abstract risk definition, based on somewhat to highly nebulous data points, aggregated and massaged via proprietary formulas.
Rage brings the potential to analyze unbounded data sets – potentially delivering a comprehensive analysis that doesn’t stand and fall with any particular data source. Any organization interested in developing new insights into supplier and supply chain risk owes it to itself to investigate what Rage Frameworks is up to. We promise: you won’t see anything else like them in the market, at least not at this time.
We’ll be providing some additional color on Rage Frameworks to Spend Matters Plus subscribers later today. Unfortunately, there’s nothing simple about the genius of Rage and what they’ve done. It’s complex, it’s elegant, and its game changing. We’ll explore and distill how they do what they do in this additional analysis.