Beyond Data Mining:: knowledge mining


We live in the digital age. The flow of data is truly impressive. Statistics inform us that each year we generate more data than the past generations did in decades. But the problem is that Information Technology (IT) has concentrated on simply automating old ways of thinking, creating bottlenecks and problems we didn’t even imagine, and not really inventing new processes or approaches. There is little innovation going on in the IT arena. But one thing is certain, we are drowning in data and we’re thirsty for knowledge…

Turn data into structure

Structure is the overture to knowledge. But what is knowledge? What is a “body of knowledge”? Setting aside ontological hair-splitting  we could say that a body of knowledge is equivalent to a structured and dynamic set of inter-related rules. The rules can be crisp or fuzzy or both. But the key here is structure. Structure is the skeleton upon which a certain body of knowledge can be further expanded, refined, modified (this is why we say “dynamic”). One could say that structure forms the basis of a model or of a theory. Today there exist many ways of extracting structure from data. Statistics is one way. Building models based on data is another. But because building models and mis-handling of statistics has contributed to the destruction of a big chunk of our economy, we have invented a new method of identifying structure in data – a model-free method, which if free of statistics and building models. A method which is “natural” and un-biased.

via Ontonix – Complex Systems Management, Business Risk Management.

Risk:: some things just CANNOT be modelled


Believe it or not this is only an extract from a longer article by the Founder of Ontonix. I am, very much a layman when it comes to computer models but that is most certainly not the case with Jacek (Marczyk). However, even I know enough to question, what I have come to refer to as, the “prediction addiction”  that afflicts the insurance and wider financial sector.

There is a fundamental principle – the Principle of Incompatibility – which states that as complexity increases, precision and relevance become mutually exclusive. In other words, as things get complex (and they seem to be) your statements about it become less and less precise. This means that as something becomes highly complex you can forget building models. You need to change strategy. A new approach is needed. You must change direction. Large consulting firms claim otherwise. Read more of this post

Hierarchies of Understanding:: data is useful, INFORMATION “invaluable”


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I’m as good (or bad) at understanding humans and human learning as the next person! I am not an “educator” just someone who, I suspect (like everyone else), has at some time or other felt swamped: by too much to do; too much to absorb; too little time. We know that people learn in different ways and at different speed and, quite apart from Carpenter & Cannady, there are any number of alternative views on HoU…take your pick!

What I like about the C&C approach is that it reflects an ongoing process – we ARE (or should be) constantly learning – with feedback from our environment shaping our perspectives. On one occasion rendering the “expert” a “novice” and, on another, providing the vital “missing piece” that transforms information to knowledge and, through understanding, to wisdom.

In the beginning was information*…

But, ever the contrarian, I can’t ignore the fact that, the limitations to obtaining data (about anything) pertaining to that which we are observing, are our own!

Read more of this post

The Inevitable Next Economy


The Human Productivity Chart

Courtesy of Dan Robles (Ingenesist

Human civilization has progressed through many stages.  Each stage arose from the “integration” of the tools developed in the prior stage.  Believe it or not, the next economic paradigm will arise from the integration of the tools being developed in the current stage of human development. Let me explain:

Hunter -gatherer:

We started as hunter-gathers who travelled from place to place to follow animal migrations and seasonal flora.  People would collect fallen branches and burn them for heat or cooking.  Then people started to sharpen rocks that could be used to hunt food better than a dull rock. They sharpened rocks to chop down trees for warmth and shelter.  Soon they sharpened rocks to till soil.

The agrarians

The arrival of the agrarian age came when the arrow, the axe, and the plough were integrated; that is, the output of one became the input of another – allowing people to conserve energy and increasing productivity. The emergence of communities led to the division of labour as people specialized their skills. People soon developed tools and techniques for forging metals, building structures, and harnessing of forces such as wind, sun, water, and domesticated animals.

City-states

The arrival of City-States arose when division of labour, harnessing forces, and transportation became integrated.  Spare time became available to experiment in ideas such as governance, laws, civil services, and currency. Travel allowed for trade of goods, services, and the spread of knowledge across great distances.

Read more of this post

Identifying Hidden Structure (in Data) and Computing Knowledge


Extract taken from Ontonix Corporate website. Full article here.

 

We live in the digital age. True.  The flow of data is impressive. Statistics inform us that each year we generate more data than the past generations did in decades. But the problem is that Information Technology (IT) has concentrated on simply automating old ways of thinking, creating bottlenecks and problems we didn’t even imagine, and not really inventing new processes or approaches. There is little innovation going on in the IT arena. But one thing is certain, we are drowning in data. The problem is not simply storage (disk space is cheap). The big deal is how to extract workable knowledge out of all this data.

But what is knowledge? What is a “body of knowledge”? Setting aside ontological hair-splitting, we could say that a body of knowledge is equivalent to a structured and dynamic set of inter-related rules. The rules can be crisp or fuzzy or both. But the key here is structure. Structure is the skeleton upon which a certain body of knowledge can be further expanded, refined, modified (this is why we say “dynamic”). One could say that structure forms the basis of a model or of a theory. Today, there exist many ways of extracting structure from data. Statistics is one way. Building models based on data is another. But because building models and mis-handling of statistics has contributed to the destruction of a big chunk of our economy, we have invented a new method of identifying structure in data – a model-free method, which is free of statistics and building models. A method which is “natural” and un-biased…