Complexity Economics

Beyond Pre-Crisis Analytics

Conventional data mining technology has the objective of establishing patterns and rules from large amounts of data by combining such techniques as statistics, artificial intelligence and data-base management. Data-mining and Analytics techniques are supposed to provide managers with an extra edge and to transform data into business intelligence. Have they succeeded? To find the answer take a look at the state of the global economy.

Conventional pre-crisis data mining and data analysis techniques display information by means of curves, 2D or 3D plots, pie charts, bar charts, or fancy surfaces. When the dimensionality of data is high methods become impractical in that one has to cope with hundreds if not thousands of such plots. It is necessary to resort to methods that really synthesize data not just transform one problem to another. Our complexity-based Analytic Engine OntoNet™ does something completely different.

Every time you do something to data you destroy some of the information it contains. But data is expensive. We have developed innovative model-free technology that doesn’t destroy information. In fact, our approach emulates the way the brain works when you actually look at data. By transforming raw data into structure we also achieve an unprecedented degree of synthesis. And it all comes in one single Business Structure Map. This means you get to appreciate the nature and dimensionality of all your data to the fullest possible extent.

Extracting knowledge from data is not just about putting together pieces of information. This is precisely where traditional technology has failed. We drown in data but we are thirsty for knowledge, not information. Capturing knowledge, be it from field data or data that emerges from computer simulation, means transforming it into structure. Structure means relationships, degrees of freedom, constraints. Structure means gaining understanding and knowledge. Precisely what OntoNet™ is about.

The moment you map multi-dimensional data onto structure you get to appreciate a fundamental and new aspect of a business – its complexity.OntoNet ™ not only provides a unique and modern representation of a business, it also measures its complexity. Why is this so important? Because the rapid increase of business complexity, which is an inevitable consequence of turbulence and globalization, is one of the biggest enemies of growth, stability and resilience. With OntoNet™, conventional risk management transitions into its more advanced and natural form: complexity management.

In a globalized and increasingly turbulent economy the survival of a business hinges on its ability to react quickly to unexpected, unique and extreme events. The economy is not linear, it is not stationary, it is not in a state of equilibrium and not everything follows a Gaussian distribution. However, many of the conventional BI and Analytics techniques are in violation of the basic laws of physics. Building a sustainable and resilient economy means also going beyond regressions, neural nets or statistics.

By the way, have you ever seen the DJIA like this?

See it in motion here.

Established originally in 2005 in the USA, Ontonix is a technology company headquartered in Como, Italy. The unusual technology and solutions developed by Ontonix focus on countering what most threatens safety, advanced products, critical infrastructures, or IT network security - the rapid growth of complexity. In 2007 the company received recognition by being selected as Gartner's Cool Vendor. What makes Ontonix different from all those companies and research centers who claim to manage complexity is that we have a complexity metric. This means that we MEASURE complexity. We detect anomalies in complex defense systems without using Machine Learning for one very good reason: our clients don’t have the luxury of multiple examples of failures necessary to teach software to recognize them. We identify anomalies without having seen them before. Sometimes, you must get it right the first and only time!

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