More: Enterprise Wisdom Management and the Flow of Understanding


The DIKWI model

The DIKWI model or DIKWI pyramid is an (I) extension to the DIKW model/pyramid. It´s an often used method, with roots in knowledge management, to explain the ways how we move from data (D) generated by Human and Technical Sensors to information (I), knowledge (K), wisdom (W) and best imaginative decisions and actions (I).

Simply put, it’s a model to look at various ways of extracting insights and value from all sorts of data: big data, small data, smart data, fast data, slow data, it doesn’t matter. The DIKWI model is often depicted as a hierarchical model in the shape of a pyramid and also known as the data-information-knowledge-wisdom hierarchy, among others.

What matters: decisions and actions in DIKWI

What we’re most interested in, is the decision and action part. We’ve talked about ‘actionable data’ and ‘actionable information’ before and Jennifer Rowley refers in her paper to knowledge as being actionable information, based on the work of E.M Awad and H.M. Ghaziri, more specifically their 2004 book Knowledge Management.

Decisions and Actions. That’s what we need. Because without action there is little sense in gathering, capturing, understanding, leveraging, storing and even talking about data, information and knowledge. We mean action as in business and customer outcomes, creating value in an informed way. But of course in the bigger picture, action can also simply be learning or anything else.

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Optimizing Your Health with the DKIWI Model -The Diabetes Case

Dexcom G6 Continous Glucose Monitoring (CGM) System


More: Dexcom G6 Starter Guide



To setup the Integrated Continous Glucose Monitoring (ICGM) System, take a look on the following Webpage: ICGM-System Setup



Short term: The sensor of your Continous Glucose Monitoring (CGM) System generates every 5 Minutes raw Data about your Glucose Value.

Long term: The CGM generated a lot time series data which will be stored in the Clarity Big Datawarhouse.


Short term: This means: At YYYY.MM.DD and Time your Glucose Value is 225

Long term: I can see the development of my blood sugar over time


Short term: In my Context 225 is a high value and is 75 away from the upper boarder of my glucose target range.

Long term: After 14 days my estimated HbA1c is 7,1 and the average glucose value is 156.



Short term: This number applied to my context means, I should do a little walk or sport to drop it from 225 to 100.

Long term: On a 30 days base, I have been 67% within the target range (Max=300, Min=60). 33% above and 1% under the target range.  My estimated HbA1c is 7,3 with a glucose value of 162. Within the generated raw data 3 Pattern have been found: High values during the night, High values in the morning and Best day was the 2018/12/19.


More: Clarity Report


Short term: In my current context, it´s just raining outside and I think I will get wet when I do a little walk, but not getting wet when I use my inside bike. The best imaginative decision and action in my current context is to use my inside bike.

Long term: In my longterm context. If I do more sports, I can drop my HbA1c. To get there, I use Samsung Health on my Handy: Samsung Galaxy S7 and on my Smartwatch: Samsung Galaxy S3 Frontier.

I hope, that I improve my HbA1c on a longterm with all the positive impacts on my health.


Screenshot_20181225-151958_Samsung Health.jpgScreenshot_20181225-152034_Samsung Health.jpg


In the broadest definition, a sensor is a device, module, or subsystem whose purpose is to detect events or changes in its environment and send the information to other electronics, frequently a computer processor. A sensor is always used with other electronics.

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The following picture show examples of Sensors.



Data and information or knowledge are often used interchangeably; however data becomes information when it is viewed in context or in post-analysis [2]. While the concept of data is commonly associated with scientific research, data is collected by a huge range of organizations and institutions, including businesses (e.g., sales data, revenue, profits, stock price), governments (e.g., crime rates, unemployment rates, literacy rates) and non-governmental organizations (e.g., censuses of the number of homeless people by non-profit organizations).

Data is measured, collected and reported, and analyzed, whereupon it can be visualized using graphs, images or other analysis tools. Data as a general concept refers to the fact that some existing information or knowledge is represented or coded in some form suitable for better usage or processing. Raw data (“unprocessed data”) is a collection of numbers or characters before it has been “cleaned” and corrected by researchers. Raw data needs to be corrected to remove outliers or obvious instrument or data entry errors (e.g., a thermometer reading from an outdoor Arctic location recording a tropical temperature). Data processing commonly occurs by stages, and the “processed data” from one stage may be considered the “raw data” of the next stage. Field data is raw data that is collected in an uncontrolled “in situ” environment. Experimental data is data that is generated within the context of a scientific investigation by observation and recording. Data has been described as the new oil of the digital economy.[3][4]

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Sensor Data Fusion


Sensor fusion is combining of sensory data or data derived from disparate sources such that the resulting information has less uncertainty than would be possible when these sources were used individually. The term uncertainty reduction in this case can mean more accurate, more complete, or more dependable, or refer to the result of an emerging view, such as stereoscopic vision (calculation of depth information by combining two-dimensional images from two cameras at slightly different viewpoints).[1][2]

The data sources for a fusion process are not specified to originate from identical sensors. One can distinguish direct fusion, indirect fusion and fusion of the outputs of the former two. Direct fusion is the fusion of sensor data from a set of heterogeneous or homogeneous sensors, soft sensors, and history values of sensor data, while indirect fusion uses information sources like a priori knowledge about the environment and human input.

Sensor fusion is also known as (multi-sensor) data fusion and is a subset of information fusion.

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More: Sensor Whitepaper

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Information is the resolution of uncertainty; it is that which answers the question of “what an entity is” and is thus that which specifies the nature of that entity, as well as the essentiality of its properties. Information is associated with data and knowledge, as data is meaningful information and represents the values attributed to parameters, and knowledge signifies understanding of an abstract or concrete concept.[1] The existence of information is uncoupled with an observer, which refers to that which accesses information to discern that which it specifies; information exists beyond an event horizon for example, and in the case of knowledge, the information itself requires a cognitive observer to be accessed.

In terms of communication, information is expressed either as the content of a message or through direct or indirect observation. That which is perceived can be construed as a message in its own right, and in that sense, information is always conveyed as the content of a message.

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Knowledge is a familiarity, awareness, or understanding of someone or something, such as facts, information, descriptions, or skills, which is acquired through experience or education by perceiving, discovering, or learning.

Knowledge can refer to a theoretical or practical understanding of a subject. It can be implicit (as with practical skill or expertise) or explicit (as with the theoretical understanding of a subject); it can be more or less formal or systematic.[1] In philosophy, the study of knowledge is called epistemology; the philosopher Plato famously defined knowledge as “justified true belief“, though this definition is now thought by some analytic philosophers[citation needed] to be problematic because of the Gettier problems, while others defend the platonic definition.[2] However, several definitions of knowledge and theories to explain it exist.

Knowledge acquisition involves complex cognitive processes: perception, communication, and reasoning;[3] while knowledge is also said to be related to the capacity of acknowledgement in human beings.[4]

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Wisdom, sapience, or sagacity is the ability to think and act using knowledge, experience, understanding, common sense and insight.[1] Wisdom is associated with attributes such as unbiased judgment, compassion, experiential self-knowledge, self-transcendence and non-attachment [2], and virtues such as ethics and benevolence.[3][4]

Wisdom has been defined in many different ways,[2][5][3] including several distinct approaches to assess the characteristics attributed to wisdom.[6][7]

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Imagination is the ability to produce images, ideas and sensations in the mind without any immediate input of the senses (such as seeing or hearing). It is also described as the forming of experiences in the mind, which can be recreations of past experiences such as vivid memories with imagined changes or that they are completely invented.[1] Imagination helps make knowledge applicable in solving problems and is fundamental to integrating experience and the learning process.[2][3][4][5] A basic training for imagination is listening to storytelling (narrative),[2][6] in which the exactness of the chosen words is the fundamental factor to “evoke worlds”.[7]

Imagination is a cognitive process used in mental functioning and sometimes used in conjunction with psychological imagery. It is considered as such because it involves thinking about possibilities.[8] The cognate term of mental imagery may be used in psychology for denoting the process of reviving in the mind recollections of objects formerly given in sense perception. Since this use of the term conflicts with that of ordinary language, some psychologists have preferred to describe this process as “imaging” or “imagery” or to speak of it as “reproductive” as opposed to “productive” or “constructive” imagination. Constructive imagination is further divided into voluntary top-down imagination driven by the prefrontal cortex, that is called mental synthesis, and spontaneous bottom up involuntary generation of novel images that occurs during dreaming. Imagined images, both novel and recalled, are seen with the “mind’s eye“.

Imagination, however, is not considered to be exclusively a cognitive activity because it is also linked to the body and place, particularly that it also involves setting up relationships with materials and people, precluding the sense that imagination is locked away in the head.[9]

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Best Imaginative Decisions and Actions

The Role of Imagination in Decision‐Making

Bence Nanay

Homepage: Bence Nanay

Wikiwand:  Bence Nanay

Centre for Philosophical Psychology University of Antwerp
and Peterhouse Cambridge University

The psychological mechanism of decision making has traditionally been modeled with the help of belief desire psychology: the agent has some desires (or other pro attitudes) and some background beliefs and deciding between two possible actions is a matter of comparing the probability of the satisfaction of these desires given the background beliefs in the case of the performance of each action. There is a wealth of recent empirical findings about how we actually make decisions that seems to be in conflict with this picture. Bence aim is to outline an alternative model that is consistent with these empirical findings. This alternative model emphasizes the role imagination plays in our decisions: when we decide between two possible actions, we imagine ourselves in the situation that we imagine to be the outcome of these two actions and then compare these two imaginings.

Here is a toy example to demonstrate how the imagination-based account of decision-making would work (I used this example in Nanay 2013, Chapter 4). You need to decide between two academic jobs: one of them is at a prestigious university in a not very nice small town, and the other is at a not very prestigious university in a great city. How do you decide? The belief–desire model would suggest that you have some desires (or other pro-attitudes) about how you want to live the rest of your life, and you also have some background beliefs; deciding between the two jobs is a matter of comparing the satisfaction of these desires given the background beliefs for each of the two choices.

How would I describe the decision-making process in this example? When you decide between the two jobs, you imagine yourself in the situation that you imagine to be the outcome of your decision one way or the other. You imagine yourself at the prestigious university surrounded by great colleagues and doing excellent research in a sleepy small town, spending the evenings working or with colleagues. You also imagine yourself at the not so prestigious university, spending every night out in cool restaurants and at various cultural events, to return to teaching the next day among your mediocre colleagues and not-so-bright students. Then you compare these two imaginative episodes, and the one you prefer will be the course of action to follow.
According to this new picture, decision-making is not the comparison of the probability of the satisfaction of desires given background beliefs. Decision-making is a matter of comparing imaginative episodes (even if these imaginative episodes are constrained by our beliefs).

More: Wiley Article

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