2Know Magazine: Sharing KM Knowledge
2Know: Sharing KM Knowledge
October 2019 - Magazine No. 241
October 2019 - Magazine No. 241
Edition:
Written By Lior Cohen

Learning, according to Wikipedia, is the process of acquiring new, or modifying existing, knowledge, behaviors, skills, values, or preferences. When discussing knowledge transferring, one can review transferring methods as a learning process. The knowledge is made accessible in a certain manner that is deemed either effective or ineffective. It reaches the knowledge consumer to be learned, comprehended and even remembered.

 

Yet, is it?

Learning is a process that may be created and benefit any setting in which knowledge is stored. But is knowledge transfer aimed, by definition, at enabling learning processes?

In short, we must ask: is this knowledge required for real-time action?

Let’s say you are looking for a solution for some malfunction. Alternatively, you might be seeking a solution for a client that contacted you for service. You could also be trying to figure out how to bake a three-layered fudge cake. These three remotely different examples all have something in common: immediate action in real time. These cases require a more efficient method, namely algorithmics.

Wikipedia defines an algorithm as "a self-contained step-by-step set of operations to be performed". Wait, you might ask. I thought algorithms are for programmers; that's a valid question. Algorithms are indeed a branch of computer sciences. It is also, however, the most efficient way to motivate human action.

The human brain consumes vast amounts of energy, a whopping twenty percent of the body's overall energy. For us to save resources and make work more efficient, we must skip processing and comprehension processes. We must shift to practical and simple commands that call to action. To reduce cognitive effort, we must operate our brain's simpler, more technical mechanisms.

Knowledge formulated for learning purposes usually features a hierarchy in which the most important and common issues appear from start to finish. The marginal, rare issues will appear separately (if they appear at all). Since knowledge aimed at immediate action serves a specific scenario, its frequency is of no relevance. Therefore, a knowledge manager will have to provide the users a "one stroke" rendition of the process. They must be presented the journey from start to finish, including its most miniscule, rare ramifications.

An Algorithm written for a computer action requires precision with regard to a number of elements. Any deviation from the correct sequence of action or lack of the smallest element is critical. The process may subsequently come to a halt or produce an erroneous result.

To return to Wikipedia, an algorithm must meet two requirements to ensure its performance quality: every input the algorithm receives will reach its end at one some point. When this point is reached, it must provide a correct answer.

An unclear direction might cause the user to stop using knowledge. This is one reason not to reach the end. An incorrect or misplaced direction will lead to an incorrect action.

The range of the damage a wrong action can cause is obviously broad. This range features a relatively simple malfunction on its one end and financial loss on the other. It might even lead to the loss of a life when dealing with medical information. The methodical course the algorithm provides is the key to effective and efficient knowledge transfer.

 

There are several ways in which we construct knowledge algorithmically:

  • Starting point- define the level of knowledge with which the users are equipped when accessing this data. Will the recipe require frying onion or explain how to fry an onion, step by step?
  • Construction of the process as a flow chart- every algorithm is based on a flow chart. The visual presentation for users can appear in various ways. The flow chart, however, is what directs the process and is the basis of said presentation.
  • Presenting the directions as a sequence: information presented sequentially conveys a sense of security. There is a start and a finish, reasons the user. This structure allows the users to advance gradually by following instructions. Users should be able to recognize their current stage relatively easily.
  • Complete decision junctions- an algorithm must refer to various existing possibilities. If instructed to ask the customer for a certain detail, it should be clear how to proceed in both cases (the customer has/doesn't have said detail).

On the other hand, keep it light on the edge cases. Decide when to refer the reader to the appendix, or even an official, and refrain from including the information in the body of knowledge.

  • Visual differentiation between content types:
    • The content of a set of instructions can contain:
      • Instructions
      • Subsidiary instructions (e.g. 'update the system change' can be followed by a subsidiary instruction such as a process which presents the systems' operation method)
      • An explanation related to an instruction
      • Results: what will happen if an action is performed
    • For the journey to be easy and comfortable, the instructions must be presented uniformly and clearly. The other types of content can appear as links incorporated in the original instruction as comments designed differently than the instructions. These comments can appear as data elaborated on with the click/stall of a mouse.
    • Updates: written knowledge's greatest threat is the moment in which a change is require or knowledge must be added. The key to these cases is identifying the critical stage. We might reach an incorrect result or an error due to placing data hastily. The new information can be inserted in one location its entirety or divided into a number of segments and distributed throughout the system. The manager can change the current knowledge (perhaps extensively) before inserting the new data.

In conclusion, knowledge consumers are demanding simplicity and clarity. This requires knowledge managers to work harder as they must now simplify and distill knowledge into an algorithm and call to action. In the near future, knowledge will be used by machines as well. Therefore, structuring data and algorithms plays a meaningful role regarding any information calling to action.

As said, algorithms aren't just for computers. 

 

 

"The Magical Number Seven, Plus or Minus Two: Some Limits on Our Capacity for Processing Information"[1] is one of the most highly cited papers in psychology.[2][3][4] It was published in 1956 in Psychological Review by the cognitive psychologist George A. Miller of Harvard University's Department of Psychology. It is often interpreted to argue that the number of objects an average human can hold in short-term memory is 7 ± 2. This has occasionally been referred to as Miller's law.[5][6][7]

The paragraph cited above is the opening paragraph of the Wikipedia entry discussing "The Magical Number Seven, Plus or Minus Two". To sum it up, Miller's 1956 seminal paper describes the limits of the human working memory. Our short term memory can process only a few items at any given moment. Miller even goes as far as stating a specific number: 7 ± 2. Miller argues that people can hardly remember more than 9 items recently learned. Actually, new studies have come up with even smaller numbers… obviously, this rule has its exceptions. Furthermore, the processed items may be item groups rather than individual items.

What does our limited working memory have to do with Knowledge Management?

When constructing knowledge items, we must consider the limitations of our working memory. This knowledge should affect how we construct lists, menus, catalogues and landing pages.

Here are some examples of how this knowledge can be implemented.

  • Collecting items and grouping them by category/family to reduce list length
  • Store similar items under multiple categories
  • Construct decision trees and navigation menus comprised of branches that do not exceed the 7 ± 2 rule
  • Planning the number of subjects/buttons on each internet page

References

Wikipedia

 

The 21st Century has ushered in an impactful revolution which has made data much more accessible to the public. Due to the acceleration in the development of the marvels of the internet and mobile technology we are witnessing base changes in Open Source Intelligence (OSINT). Let's start from the top.

What is OSINT?

OSINT is a field of data processing that includes locating, selecting and collecting data from sources explicitly available to the public and are accessible to the majority of the public. This data is then analyzed and used to generate military, business or political information. OSINT's relevance to Knowledge Management has increased in recent years. OSINT grants us large amounts of data which are usually utilized by intelligence analysts of various sorts (business/finance/military/etc.) to identify tacit potential risks or recommend strategic decisions.

 

Where can OSINT be found?

Actually, any internet search is OSINT. When you memorize your schedule- that's OSINT, too. When you watch the news or check for any information on a product or service you wish to purchase- that's OSINT. We are all exposed to intelligence and said data.

Where's the problem? Anyone can use OSINT as a tool.

That would be a common assumption, based on the notion that all data is at our fingertips. However, despite its vast advantages our technological age has its catch. There is too much information, so much that it is referred to as an Information Explosion. This is actually the premise to all KM systems and frameworks. The vast amounts of data have made finding data quickly, easily and efficiently a difficult feat. Knowledge Management tools, specifically data/text mining tools, should be used to mine the data relevant to us.

The value of Open Source Intelligence

The main objective of OSINT is mining the data we need. Its main success is the combination of large data quantities and quality. If we can mine the data amount that suits our needs, we can be much more efficient. in short, we must refer to Knowledge Management.

For example, market surveys and comparisons for product marketing. To market a new product, we must review whether this product is in demand. If so, which ages consume it most? Which area would buy more of it? Would it be easy to operate by the target audience? Will it be age/gender dependent? Etc.

The more data we hold (quantity) and the better we know our target audience (quality), the greater will our product/service/solution succeed and benefit us.

 

OSINT on social networks

Applying a knowledge and information monitoring strategy to social networks is highly recommended. It is the first step towards discovering what different individuals from different communities think about your company, about a type of products and more specifically: your product.

The tacit meaning of "social" is "public". Besides serving as a social tool used for sharing among community members, social networks are also a public domain accessible to all. A high percentage of data shared in social networks is public. While there is some private data, it is usually not revealed.

Of course, social networks are also a source of military intelligence. For example, if the military is searching for a certain individual, social networks can be used to locate them. This individual's Facebook profile might reveal their whereabouts, as would their check-in points, uploaded pictures and Facebook pictures. OSINT combined with social networks accelerate knowledge and data locating and sharing processes. This combination makes the process easier and quicker, as well as more efficient. it opens to us a world of data and knowledge previously invisible due to the limits of traditional tools.

 

Advantages and disadvantages

Advantages

  • Cheap OSINT is substantially cheaper than traditional data collection tools
  • Sharing and producing data- data can be legal and shared with everyone; accessible sources are always available and are usually more up to date on any given subject
  • Information collected from public sources is a meaningful resource to be exploited for both military and business intelligence

Disadvantages

  • There are vast amounts of data in OSINT environments. Filtering them might be a difficult task.
  • OSINT requires human involvement- information filtering requires much human analysis to pick out reliable information from the great pile of irrelevant data.

My personal experience with the world of OSINT shows that it can be a very efficient Knowledge Management tool. It is constantly available and is legal, accessible and comfortable.

In short, let's use this amazing information to optimize and improve our organization.

 

References


https://www.expertsystem.com/osint/ 
https://en.wikipedia.org/wiki/Open-source_intelligence#History 

 

Written by Rom Knowledgeware
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