The big data methods adopted by CRIF help reconstruct the links in the value chain, integrating open data coming from a number of heterogeneous sources.
For industrial economists, but also for the banking and financial system, supply chains have always been particularly interesting as they are able to define a development and interrelationship model between significantly distinctive companies.
The concept of the supply chain, as a set of activities in a reciprocal relationship for the creation, transformation, distribution, marketing and supply of a specific product/service within its value chain, requires a particularly complex and demanding reconstruction and mapping of the correlations between companies.
Among the various areas of effective application, big data allow the limits of the most traditional approach based on economic sectors to be overcome, and to understand and reconstruct the links in the value chain through structured and non-structured information coming for a variety of sources.
In fact, analysis by economic sector is not able to return exhaustive information for the completion of the value chain, since within the chain there is a crossover of activities and services that go beyond the traditional paradigm of purchase-transformation-sale of a product.
The big data methods adopted by CRIF help reconstruct the links in the supply chain, integrating the huge CRIF proprietary data asset (i.e.: financials statements, unstructured data from firms sites, “report requests” log) with open data coming from many heterogeneous sources: these include, for example, ATECO classifications or corporate purposes, and additional available data or information within the digital world but which, very often, are not sufficiently structured.
Pierpaolo Cristaudo
Principal - Management Consulting
CRIF Credit Solutions
