Graphext · A REVOLUTIONARY SYSTEM IN THE EMERGING MARKET OF ADVANCED ANALYTICS. Graphext bridges the gap between Data Science and Business Intelligence, democratising Advanced Analytics.

The landscape for data analytics is undergoing profound changes, 1) because of the growing awareness of organisations in the intrinsic value of the data, 2) because of the huge explosion in amount and sources of data accessible, and 3) because of developments in tools and methods to extract value from the data.The challenge for organisations is to optimally extract intrinsic value (economic, social and environmental) to remain effective and competitive. Organisations that can achieve this rapidly and most economically will certainly excel. Currently, the tools available on the market have are scattered and diverse, on one hand being conventional BI that have failed to keep up (with traditional databases and visualisation schema designed for structured data), and on the other there are sophisticated tools designed for use by data scientists, creating a major gap in between, to reach business analysts and decision makers.To date there are no tools on the market that combine these advanced data science methods together with user-friendly value-creation results and front-ends, aimed at tactical and strategic decision making.Graphext disrupts the existing market for data-science and BI analytics, by combining the full-stack functionalities in one seamless cloud-based SaaS solution. Graphext’s revolutionary approach in the analysis and visualization of complex datasets, brings the field of advanced Data Science to the mainstream.Graphext delivers an advanced system for data analytics and value-creation - providing direct ‘actionable insight` for decision makers. Graphext applies advanced scientific techniques with unsupervised automated learning together with advanced data visualisation. This is an entirely different approach to data analysis as it takes a network and statistical approach, e.g. through employing techniques such as heuristics, looking at inherent patterns, anomalies and extracting intrinsic meaning from the data, doing the work of the data scientists.