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.

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


image

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!

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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.

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The difference between knowledge and understanding


This answer is from a Linkedin discussion forum and, judge for yourself, but I thought the answer was about as complete as it could get:

  • Knowing; having the ability to cite chapter and verse with reference to an issue / area / industry, facilitating one to project oneself as somewhat of a subject matter expert
  • Understanding; when the full depth and breadth of a given subject matter is inseparably interwoven / integrated within one’s core, resulting in seemingly instinctive thoughts and awareness

CRITICAL LOSS and FAILURE; refers to the impact at personal and group level (of immeasurable scope) where an individual with true understanding, is unable or unwilling to consistently translate that vast understanding into effective action.

Snake Oil Sales; refers to an individual without true understanding, yet portraying oneself as a having vast understanding of subject matter.

*NOTE: Both Critical Loss as well as Snake Oil Sales result in unequalled damage upon the greater group. Both equate to dysfunctional conditions & damage, and are thus in need of identification and immediate handling by truly effective leadership

We have all come across individuals, organisations and scenarios such as the above and, if you have anything further to add, please feel free to comment.

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…