Is anyone actually using AI? #KM101 #AI


Matt Moore <innotecture@...>
 

Hi,

Is anyone using "Artificial Intelligence" as part of their Knowledge Management programs? I will let you define that term however you wish.

This is to prep for a talk that I am giving in a few months. My impression is that very few people are using this on a day-to-day basis but I am very curious to find out if they are.

Regards,

Matt


Murray Jennex
 

I used machine learning and natural language processing (not really AI but generally lumped together with it) as part of a KM approach to identifying victims of human sex trafficking.  the listserver isn't letting me send the paper so either go to researchgate or send me an email and I'm happy to send it....murray


-----Original Message-----
From: Matt Moore innotecture@... [sikmleaders]
To: Yahoo! Inc.
Sent: Thu, Oct 25, 2018 8:15 pm
Subject: [sikmleaders] Is anyone actually using AI?

Hi,

Is anyone using "Artificial Intelligence" as part of their Knowledge Management programs? I will let you define that term however you wish.

This is to prep for a talk that I am giving in a few months. My impression is that very few people are using this on a day-to-day basis but I am very curious to find out if they are.

Regards,

Matt


------------------------------------
Posted by: Matt Moore <innotecture@...>
------------------------------------


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Murray Jennex
 

basically I see AI and related technologies as knowledge discovery filters on large data sets such as IoT and Big Data.  My revised knowledge pyramid:

Jennex, M.E., (2017).  “Big Data, the Internet of Things and the Revised Knowledge Pyramid,” Data Base for Advances in Information Systems, 48(4), pp. 69-79.

shows AI, machine learning, analytics, and other technologies as filters on real world data to create KM knowledge.  The research I mentioned earlier on Identifying victims of human sex trafficking:

Hultgren, M.; Whitney, J.; Jennex, M.E.; Elkins, A. (2018).  A Knowledge Management Approach for Assisting in Identifying Victims of Human Sex Trafficking, Communications of the Association for Information Systems, volume 42, article 23, pp. 602-620.  Available at http://aisel.aisnet.org/cais/vol42/iss1/23/

Whitney, J., Jennex, M.E., Elkins, A., Frost, E., (2018).  “Don’t Want to Get Caught? Don’t Say It: The Use of EMOJIS in Online Human Sex Trafficking Ads.”  51st Hawaii International Conference on System Sciences, HICSS51, IEEE Computer Society, January 2018.

are applications of this model.  Our current work below on how to do an automated census is also an application of this model.  Basically I look at the above model as a guide to creating KM Strategy and the knowledge content process by helping to identify needed knowledge and the tools/filters needed to obtain that knowledge

Kelly, J., Jennex, M.E., Abhari, K., Durcikova, A., and Frost, E., (2019). Data in the Wild: A KM Approach to doing a Census Without Asking Anyone and the Issue of Privacy.  52nd Hawaii International Conference on System Sciences, HICSS52, IEEE Computer Society, January 2019.

Another area being investigated is the use of AI tools in content/concept mapping.  The tools are a huge advantage in looking at large numbers of document (unstructured data) and extracting terms and concepts and creating linkages.  A rudimentary example of this with respect to bigram plot analysis is:

Jennex, M.E.; Dittes, S.; Smolnik, S.; Croasdell, D.; King, D.: Knowledge, Innovation, and Entrepreneurial Systems at HICSS, Communications of the Association for Information Systems, In Press  (accepted, finalized, waiting to be published)

So while many organizations may not be using AI tools, I expect they will be real soon in their KM programs. (let me know if you want to see any of these papers)....murray jennex


-----Original Message-----
From: Matt Moore innotecture@... [sikmleaders] To: Yahoo! Inc.
Sent: Thu, Oct 25, 2018 8:15 pm
Subject: [sikmleaders] Is anyone actually using AI?

Hi,

Is anyone using "Artificial Intelligence" as part of their Knowledge Management programs? I will let you define that term however you wish.

This is to prep for a talk that I am giving in a few months. My impression is that very few people are using this on a day-to-day basis but I am very curious to find out if they are.

Regards,

Matt


------------------------------------
Posted by: Matt Moore <innotecture@...>
------------------------------------


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Randhir Pushpa
 

We used AI technologies like Natural Language Processing, Machine Learning and Analytics in IT Services engagement. The tool helped service engineers find the closest matching ticket, predict possible impact of an incident and also identify the right person to resolve a ticket. The tool keeps learning about new patterns and finds out the best person to resolve a ticket. We mainly used Unsupervised learning.

Similarly we are in the process of developing a tool to find similar user stories in a DevOps engagement. here also machine learning is used to identify patterns.

We have also evolved concepts related to Digital assistants who can keep watching how an employee works and suggest resolutions, SMEs to work with or learning materials to improve skill set.

An interesting use case that may be a reality or is alreay is automated taxonomy mapping for knowledge artifacts. This is a pain area for most companies and an intelligent machine can do this easily. 

As Murray mentioned AI tools can be used to extract terms and concepts and creating linkages. In a few years we should see huge adoption of AI in KM programs. 

Regards
Randhir


On Sat, Oct 27, 2018 at 2:33 AM Murray Jennex murphjen@... [sikmleaders] <sikmleaders@...> wrote:
 

basically I see AI and related technologies as knowledge discovery filters on large data sets such as IoT and Big Data.  My revised knowledge pyramid:


Jennex, M.E., (2017).  “Big Data, the Internet of Things and the Revised Knowledge Pyramid,” Data Base for Advances in Information Systems, 48(4), pp. 69-79.

shows AI, machine learning, analytics, and other technologies as filters on real world data to create KM knowledge.  The research I mentioned earlier on Identifying victims of human sex trafficking:

Hultgren, M.; Whitney, J.; Jennex, M.E.; Elkins, A. (2018).  A Knowledge Management Approach for Assisting in Identifying Victims of Human Sex Trafficking, Communications of the Association for Information Systems, volume 42, article 23, pp. 602-620.  Available at http://aisel.aisnet.org/cais/vol42/iss1/23/

Whitney, J., Jennex, M.E., Elkins, A., Frost, E., (2018).  “Don’t Want to Get Caught? Don’t Say It: The Use of EMOJIS in Online Human Sex Trafficking Ads.”  51st Hawaii International Conference on System Sciences, HICSS51, IEEE Computer Society, January 2018.

are applications of this model.  Our current work below on how to do an automated census is also an application of this model.  Basically I look at the above model as a guide to creating KM Strategy and the knowledge content process by helping to identify needed knowledge and the tools/filters needed to obtain that knowledge

Kelly, J., Jennex, M.E., Abhari, K., Durcikova, A., and Frost, E., (2019). Data in the Wild: A KM Approach to doing a Census Without Asking Anyone and the Issue of Privacy.  52nd Hawaii International Conference on System Sciences, HICSS52, IEEE Computer Society, January 2019.

Another area being investigated is the use of AI tools in content/concept mapping.  The tools are a huge advantage in looking at large numbers of document (unstructured data) and extracting terms and concepts and creating linkages.  A rudimentary example of this with respect to bigram plot analysis is:

Jennex, M.E.; Dittes, S.; Smolnik, S.; Croasdell, D.; King, D.: Knowledge, Innovation, and Entrepreneurial Systems at HICSS, Communications of the Association for Information Systems, In Press  (accepted, finalized, waiting to be published)

So while many organizations may not be using AI tools, I expect they will be real soon in their KM programs. (let me know if you want to see any of these papers)....murray jennex


-----Original Message-----
From: Matt Moore innotecture@... [sikmleaders] <sikmleaders@...>
To: Yahoo! Inc. <sikmleaders@...>
Sent: Thu, Oct 25, 2018 8:15 pm
Subject: [sikmleaders] Is anyone actually using AI?

Hi,

Is anyone using "Artificial Intelligence" as part of their Knowledge Management programs? I will let you define that term however you wish.

This is to prep for a talk that I am giving in a few months. My impression is that very few people are using this on a day-to-day basis but I am very curious to find out if they are.

Regards,

Matt


------------------------------------
Posted by: Matt Moore <innotecture@...>
------------------------------------


------------------------------------

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Stephen Bounds
 

Hi Murray,

Thanks for your linked article. I'm curious about why you are persisting with the knowledge pyramid.

Adding extra layers of data at the bottom really just highlights the weakness of the DIKW model to me. Wouldn't it be much simpler to use a hierarchical agent model? ie

This still reflects the a system's use of multiple agents to achieve data processing, but doesn't artificially force things into a rigid pyramid structure.

To return to Matt's question, I think many of the AI uses in KM are very subtle and typically not thought of as "KM". I agree with others that mostly we are using AI as a recommendation engine (whether for search results or taxonomy classification). It's very rare that we provide AI agents with significant autonomy in actual decision-making. The closest I know would be the use of AVR to send user's to the correct support agent -- and it could be argued that this is just another form of recommendation in any case.

Cheers,
Stephen.

====================================
Stephen Bounds
Executive, Information Management
Cordelta
E: stephen.bounds@...
M: 0401 829 096
====================================
On 27/10/2018 8:01 AM, Murray Jennex murphjen@... [sikmleaders] wrote:

 

basically I see AI and related technologies as knowledge discovery filters on large data sets such as IoT and Big Data.  My revised knowledge pyramid:


Jennex, M.E., (2017).  “Big Data, the Internet of Things and the Revised Knowledge Pyramid,” Data Base for Advances in Information Systems, 48(4), pp. 69-79.

shows AI, machine learning, analytics, and other technologies as filters on real world data to create KM knowledge.  The research I mentioned earlier on Identifying victims of human sex trafficking:

Hultgren, M.; Whitney, J.; Jennex, M.E.; Elkins, A. (2018).  A Knowledge Management Approach for Assisting in Identifying Victims of Human Sex Trafficking, Communications of the Association for Information Systems, volume 42, article 23, pp. 602-620.  Available at http://aisel.aisnet.org/cais/vol42/iss1/23/

Whitney, J., Jennex, M.E., Elkins, A., Frost, E., (2018).  “Don’t Want to Get Caught? Don’t Say It: The Use of EMOJIS in Online Human Sex Trafficking Ads.”  51st Hawaii International Conference on System Sciences, HICSS51, IEEE Computer Society, January 2018.

are applications of this model.  Our current work below on how to do an automated census is also an application of this model.  Basically I look at the above model as a guide to creating KM Strategy and the knowledge content process by helping to identify needed knowledge and the tools/filters needed to obtain that knowledge

Kelly, J., Jennex, M.E., Abhari, K., Durcikova, A., and Frost, E., (2019). Data in the Wild: A KM Approach to doing a Census Without Asking Anyone and the Issue of Privacy.  52nd Hawaii International Conference on System Sciences, HICSS52, IEEE Computer Society, January 2019.

Another area being investigated is the use of AI tools in content/concept mapping.  The tools are a huge advantage in looking at large numbers of document (unstructured data) and extracting terms and concepts and creating linkages.  A rudimentary example of this with respect to bigram plot analysis is:

Jennex, M.E.; Dittes, S.; Smolnik, S.; Croasdell, D.; King, D.: Knowledge, Innovation, and Entrepreneurial Systems at HICSS, Communications of the Association for Information Systems, In Press  (accepted, finalized, waiting to be published)

So while many organizations may not be using AI tools, I expect they will be real soon in their KM programs. (let me know if you want to see any of these papers)....murray jennex


-----Original Message-----
From: Matt Moore innotecture@... [sikmleaders]
To: Yahoo! Inc.
Sent: Thu, Oct 25, 2018 8:15 pm
Subject: [sikmleaders] Is anyone actually using AI?

Hi,

Is anyone using "Artificial Intelligence" as part of their Knowledge Management programs? I will let you define that term however you wish.

This is to prep for a talk that I am giving in a few months. My impression is that very few people are using this on a day-to-day basis but I am very curious to find out if they are.

Regards,

Matt


------------------------------------
Posted by: Matt Moore <innotecture@...>
------------------------------------


------------------------------------

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Murray Jennex
 
Edited

https://sikm.groups.io/g/main/photo/137830/2199146/reinterpretation%2Bof%2Bdikw%2Bas%2Baki.jpg

I persist with the pyramid model as it is a known visualization that all understand.  The real value of my model is when you look at in conjunction with the inverted pyramid reflecting reality, i.e. that we've created vast amounts of knowledge and wisdom off of relatively small amounts of data until the advent of IoT and Big Data and that the purpose of KM is to focus organizations on that knowledge and actionable intelligence needed to achieve business objectives.  I don't see that shown very clearly on your model below.  As to the use of agents, they are built in as the filters I show on the pyramid include technical and human elements.  While I see my model as being very clear on relationships and processes, i don't get that with your model below.  All that said, models help understanding so which ever model works for you is fine.  Note, though that I do think my model showing how large IoT and Big Data is with respect to regular data, information, and knowledge is a crucial point to make to those trying to understand why big data and IoT is so difficult to manage and for showing how it integrates into KM...murray

Just wondering, are you biased against traditional models and have to use something different?


-----Original Message-----
From: Stephen Bounds km@... [sikmleaders]
To: sikmleaders Sent: Sat, Oct 27, 2018 1:25 pm
Subject: Re: [sikmleaders] Is anyone actually using AI? [1 Attachment]

[Attachment(s) from Stephen Bounds included below]

Hi Murray,
Thanks for your linked article. I'm curious about why you are persisting with the knowledge pyramid.
Adding extra layers of data at the bottom really just highlights the weakness of the DIKW model to me. Wouldn't it be much simpler to use a hierarchical agent model? ie
This still reflects the a system's use of multiple agents to achieve data processing, but doesn't artificially force things into a rigid pyramid structure.
To return to Matt's question, I think many of the AI uses in KM are very subtle and typically not thought of as "KM". I agree with others that mostly we are using AI as a recommendation engine (whether for search results or taxonomy classification). It's very rare that we provide AI agents with significant autonomy in actual decision-making. The closest I know would be the use of AVR to send user's to the correct support agent -- and it could be argued that this is just another form of recommendation in any case.
Cheers,
Stephen.
====================================
Stephen Bounds
Executive, Information Management
Cordelta
E: stephen.bounds@...
M: 0401 829 096
====================================
On 27/10/2018 8:01 AM, Murray Jennex murphjen@... [sikmleaders] wrote:
 
basically I see AI and related technologies as knowledge discovery filters on large data sets such as IoT and Big Data.  My revised knowledge pyramid:
 
Jennex, M.E., (2017).  “Big Data, the Internet of Things and the Revised Knowledge Pyramid,” Data Base for Advances in Information Systems, 48(4), pp. 69-79.
 
shows AI, machine learning, analytics, and other technologies as filters on real world data to create KM knowledge.  The research I mentioned earlier on Identifying victims of human sex trafficking:
 
Hultgren, M.; Whitney, J.; Jennex, M.E.; Elkins, A. (2018).  A Knowledge Management Approach for Assisting in Identifying Victims of Human Sex Trafficking, Communications of the Association for Information Systems, volume 42, article 23, pp. 602-620.  Available at http://aisel.aisnet.org/cais/vol42/iss1/23/
 
Whitney, J., Jennex, M.E., Elkins, A., Frost, E., (2018).  “Don’t Want to Get Caught? Don’t Say It: The Use of EMOJIS in Online Human Sex Trafficking Ads.”  51st Hawaii International Conference on System Sciences, HICSS51, IEEE Computer Society, January 2018.
 
are applications of this model.  Our current work below on how to do an automated census is also an application of this model.  Basically I look at the above model as a guide to creating KM Strategy and the knowledge content process by helping to identify needed knowledge and the tools/filters needed to obtain that knowledge
 
Kelly, J., Jennex, M.E., Abhari, K., Durcikova, A., and Frost, E., (2019). Data in the Wild: A KM Approach to doing a Census Without Asking Anyone and the Issue of Privacy.  52nd Hawaii International Conference on System Sciences, HICSS52, IEEE Computer Society, January 2019.

Another area being investigated is the use of AI tools in content/concept mapping.  The tools are a huge advantage in looking at large numbers of document (unstructured data) and extracting terms and concepts and creating linkages.  A rudimentary example of this with respect to bigram plot analysis is:
 
Jennex, M.E.; Dittes, S.; Smolnik, S.; Croasdell, D.; King, D.: Knowledge, Innovation, and Entrepreneurial Systems at HICSS, Communications of the Association for Information Systems, In Press  (accepted, finalized, waiting to be published)
 
So while many organizations may not be using AI tools, I expect they will be real soon in their KM programs. (let me know if you want to see any of these papers)....murray jennex


-----Original Message-----
From: Matt Moore innotecture@... [sikmleaders]
To: Yahoo! Inc.
Sent: Thu, Oct 25, 2018 8:15 pm
Subject: [sikmleaders] Is anyone actually using AI?

Hi,
 
Is anyone using "Artificial Intelligence" as part of their Knowledge Management programs? I will let you define that term however you wish.
 
This is to prep for a talk that I am giving in a few months. My impression is that very few people are using this on a day-to-day basis but I am very curious to find out if they are.
 
Regards,
 
Matt
 
 
------------------------------------
Posted by: Matt Moore <innotecture@...>
------------------------------------
 
 
------------------------------------
 
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Randhir Pushpa
 

Hi Stephen,

 

Responding to your mail to Murray. The DIKW model does not change. Technology has helped in mining more data. We are generating huge data as compared to earlier days, which is resulting in much more information and better knowledge. This has lead to better insight and understanding of any system, process or phenomenon including 'purchasing behaviour'.

 

IOT for example is just replacing humans and allowing machines to talk to each other and make decisions. For example instead of me taking a decision to buy my dairy products, my fridge takes the decision. IOT is able to generate lot of data which is being used by companies like ‘Amazon’ to become more knowledgeable about the customers. IOT is a means to create large amount of data. Hence the image that you had shared in the email is not a correct representation.

 

Regarding ‘AI uses of KM being subtle”, what I found is that AI uses of KM are not getting bundled under AI. Its mainly because those who are doing are not KM practitioners and they do not realise that they are leveraging knowledge. For example along with suggesting agents to resolve an incident, the tool itself can resolve incidents. There are cases where the tool uses pattern mapping both at problem and resolution level and understands the solution and starts applying. However most examples that I have come across are based on classifiers (unsupervised learning or pattern mapping).

 

In many cases we are not allowing machines to take a decision because of legal issues. In health sector, machines are able to predict diseases much better than doctors. But they are being used as enablers than decision makers.

 

Regards

 

Randhir


On Sun, Oct 28, 2018 at 6:00 AM Murray Jennex murphjen@... [sikmleaders] <sikmleaders@...> wrote:
 
[Attachment(s) from Murray Jennex included below]

I persist with the pyramid model as it is a known visualization that all understand.  The real value of my model is when you look at in conjunction with the inverted pyramid reflecting reality, i.e. that we've created vast amounts of knowledge and wisdom off of relatively small amounts of data until the advent of IoT and Big Data and that the purpose of KM is to focus organizations on that knowledge and actionable intelligence needed to achieve business objectives.  I don't see that shown very clearly on your model below.  As to the use of agents, they are built in as the filters I show on the pyramid include technical and human elements.  While I see my model as being very clear on relationships and processes, i don't get that with your model below.  All that said, models help understanding so which ever model works for you is fine.  Note, though that I do think my model showing how large IoT and Big Data is with respect to regular data, information, and knowledge is a crucial point to make to those trying to understand why big data and IoT is so difficult to manage and for showing how it integrates into KM...murray

Just wondering, are you biased against traditional models and have to use something different?


-----Original Message-----
From: Stephen Bounds km@... [sikmleaders] <sikmleaders@...>
To: sikmleaders <sikmleaders@...>
Sent: Sat, Oct 27, 2018 1:25 pm
Subject: Re: [sikmleaders] Is anyone actually using AI? [1 Attachment]

[Attachment(s) from Stephen Bounds included below]

Hi Murray,
Thanks for your linked article. I'm curious about why you are persisting with the knowledge pyramid.
Adding extra layers of data at the bottom really just highlights the weakness of the DIKW model to me. Wouldn't it be much simpler to use a hierarchical agent model? ie
This still reflects the a system's use of multiple agents to achieve data processing, but doesn't artificially force things into a rigid pyramid structure.
To return to Matt's question, I think many of the AI uses in KM are very subtle and typically not thought of as "KM". I agree with others that mostly we are using AI as a recommendation engine (whether for search results or taxonomy classification). It's very rare that we provide AI agents with significant autonomy in actual decision-making. The closest I know would be the use of AVR to send user's to the correct support agent -- and it could be argued that this is just another form of recommendation in any case.
Cheers,
Stephen.
====================================
Stephen Bounds
Executive, Information Management
Cordelta
E: stephen.bounds@...
M: 0401 829 096
====================================
On 27/10/2018 8:01 AM, Murray Jennex murphjen@... [sikmleaders] wrote:
 
basically I see AI and related technologies as knowledge discovery filters on large data sets such as IoT and Big Data.  My revised knowledge pyramid:

Jennex, M.E., (2017).  “Big Data, the Internet of Things and the Revised Knowledge Pyramid,” Data Base for Advances in Information Systems, 48(4), pp. 69-79.

shows AI, machine learning, analytics, and other technologies as filters on real world data to create KM knowledge.  The research I mentioned earlier on Identifying victims of human sex trafficking:

Hultgren, M.; Whitney, J.; Jennex, M.E.; Elkins, A. (2018).  A Knowledge Management Approach for Assisting in Identifying Victims of Human Sex Trafficking, Communications of the Association for Information Systems, volume 42, article 23, pp. 602-620.  Available at http://aisel.aisnet.org/cais/vol42/iss1/23/

Whitney, J., Jennex, M.E., Elkins, A., Frost, E., (2018).  “Don’t Want to Get Caught? Don’t Say It: The Use of EMOJIS in Online Human Sex Trafficking Ads.”  51st Hawaii International Conference on System Sciences, HICSS51, IEEE Computer Society, January 2018.

are applications of this model.  Our current work below on how to do an automated census is also an application of this model.  Basically I look at the above model as a guide to creating KM Strategy and the knowledge content process by helping to identify needed knowledge and the tools/filters needed to obtain that knowledge

Kelly, J., Jennex, M.E., Abhari, K., Durcikova, A., and Frost, E., (2019). Data in the Wild: A KM Approach to doing a Census Without Asking Anyone and the Issue of Privacy.  52nd Hawaii International Conference on System Sciences, HICSS52, IEEE Computer Society, January 2019.

Another area being investigated is the use of AI tools in content/concept mapping.  The tools are a huge advantage in looking at large numbers of document (unstructured data) and extracting terms and concepts and creating linkages.  A rudimentary example of this with respect to bigram plot analysis is:

Jennex, M.E.; Dittes, S.; Smolnik, S.; Croasdell, D.; King, D.: Knowledge, Innovation, and Entrepreneurial Systems at HICSS, Communications of the Association for Information Systems, In Press  (accepted, finalized, waiting to be published)

So while many organizations may not be using AI tools, I expect they will be real soon in their KM programs. (let me know if you want to see any of these papers)....murray jennex


-----Original Message-----
From: Matt Moore innotecture@... [sikmleaders]
To: Yahoo! Inc.
Sent: Thu, Oct 25, 2018 8:15 pm
Subject: [sikmleaders] Is anyone actually using AI?

Hi,

Is anyone using "Artificial Intelligence" as part of their Knowledge Management programs? I will let you define that term however you wish.

This is to prep for a talk that I am giving in a few months. My impression is that very few people are using this on a day-to-day basis but I am very curious to find out if they are.

Regards,

Matt


------------------------------------
Posted by: Matt Moore <innotecture@...>
------------------------------------


------------------------------------

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Stephen Bounds
 

Hi Murray,

Whether models are understood or not is neither here nor there. Something can be widely understood but wrong (see: 19th century aether and miasma).

While my preference is always to adopt existing models where possible, it is essential that we critically assess them and ensure they have a sound theoretical basis for their adoption.

DIKW has multiple flaws, not least that it lacks rigor around the definitions of data, information, knowledge and wisdom. I'll just quote Patrick Lambe verbatim since he said it all really well about a decade ago:

It’s important to understand the origins of a model to understand what it was designed for. The DIKW model emerged out of the struggles of computer science and information science through the late 1970s and early 1980s to legitimise themselves as strategic disciplines for the enterprise. For the data managers, the struggle was to get their organisations to treat data as a strategic resource, so establishing a relationship to information that fed decisions based on knowledge made a lot of sense. For the information managers the “downwards” link to data gave them a structure to work from, and the “upwards” link to knowledge gave them legitimacy in the eyes of senior management.

So while it had utility for data and information managers, the hierarchy was never designed to accommodate the far more complex world uncovered by knowledge management, and as Dave [Snowden] points out, it completely fails to acknowledge the naturalistic ways that data, information and knowledge interact. For example, it does not reflect the fact that data is a very small subset of repeatable information, abstracted and structured for mechanical processing based on knowledge. Data is the product of a knowledge-driven, purposeful piece of design work. The DIKW model implies the opposite, that knowledge is the product of a series of operations upon data. The model also completely fails to account for the sea of knowledge activity in an enterprise which is never informationalised or structured as data. In the natural world, data is the product of a very small component of knowledge activity.

From the data manager’s point of view, the problem in the enterprise is “we have all of this data sitting around, think of what we could do with it if we could figure out how to squeeze insight out of it”. While this is a legitimate question, the knowledge manager has discovered rather painfully, that you need to go back to the contexts that created the data and the knowledge activities the data supports, in order to figure out how the data can be manipulated for greater advantage. You can’t get there by performing a series of logical transformations on the data to create information, and then another series of operations to create knowledge.

So DIKW is a managerial model intended to explain how data can be leveraged as an enterprise resource. It has no practical value for guiding action beyond the D-I interface where it has limited value, explains almost nothing about knowledge, and its references to wisdom have always been completely without substantive or actionable content.

Any attempt to implement a management plan through the lens of big data is just as reductive and flawed as other attempts to understand organisations through a data funnel. Use of complex adaptive systems theory and agent-based models of behaviour (eg AKI cycles) may not lend themselves to a single slide, but I believe they offer a far more promising and robust way to explain systems failure and success.

Cheers,
Stephen.

====================================
Stephen Bounds
Executive, Information Management
Cordelta
E: stephen.bounds@...
M: 0401 829 096
====================================
On 28/10/2018 11:30 AM, Murray Jennex murphjen@... [sikmleaders] wrote:
 

I persist with the pyramid model as it is a known visualization that all understand.  The real value of my model is when you look at in conjunction with the inverted pyramid reflecting reality, i.e. that we've created vast amounts of knowledge and wisdom off of relatively small amounts of data until the advent of IoT and Big Data and that the purpose of KM is to focus organizations on that knowledge and actionable intelligence needed to achieve business objectives.  I don't see that shown very clearly on your model below.  As to the use of agents, they are built in as the filters I show on the pyramid include technical and human elements.  While I see my model as being very clear on relationships and processes, i don't get that with your model below.  All that said, models help understanding so which ever model works for you is fine.  Note, though that I do think my model showing how large IoT and Big Data is with respect to regular data, information, and knowledge is a crucial point to make to those trying to understand why big data and IoT is so difficult to manage and for showing how it integrates into KM...murray

Just wondering, are you biased against traditional models and have to use something different?


-----Original Message-----
From: Stephen Bounds km@... [sikmleaders]
To: sikmleaders
Sent: Sat, Oct 27, 2018 1:25 pm
Subject: Re: [sikmleaders] Is anyone actually using AI? [1 Attachment]

[Attachment(s) from Stephen Bounds included below]

Hi Murray,
Thanks for your linked article. I'm curious about why you are persisting with the knowledge pyramid.
Adding extra layers of data at the bottom really just highlights the weakness of the DIKW model to me. Wouldn't it be much simpler to use a hierarchical agent model? ie
This still reflects the a system's use of multiple agents to achieve data processing, but doesn't artificially force things into a rigid pyramid structure.
To return to Matt's question, I think many of the AI uses in KM are very subtle and typically not thought of as "KM". I agree with others that mostly we are using AI as a recommendation engine (whether for search results or taxonomy classification). It's very rare that we provide AI agents with significant autonomy in actual decision-making. The closest I know would be the use of AVR to send user's to the correct support agent -- and it could be argued that this is just another form of recommendation in any case.
Cheers,
Stephen.
====================================
Stephen Bounds
Executive, Information Management
Cordelta
E: stephen.bounds@...
M: 0401 829 096
====================================
On 27/10/2018 8:01 AM, Murray Jennex murphjen@... [sikmleaders] wrote:
 
basically I see AI and related technologies as knowledge discovery filters on large data sets such as IoT and Big Data.  My revised knowledge pyramid:

Jennex, M.E., (2017).  “Big Data, the Internet of Things and the Revised Knowledge Pyramid,” Data Base for Advances in Information Systems, 48(4), pp. 69-79.

shows AI, machine learning, analytics, and other technologies as filters on real world data to create KM knowledge.  The research I mentioned earlier on Identifying victims of human sex trafficking:

Hultgren, M.; Whitney, J.; Jennex, M.E.; Elkins, A. (2018).  A Knowledge Management Approach for Assisting in Identifying Victims of Human Sex Trafficking, Communications of the Association for Information Systems, volume 42, article 23, pp. 602-620.  Available at http://aisel.aisnet.org/cais/vol42/iss1/23/

Whitney, J., Jennex, M.E., Elkins, A., Frost, E., (2018).  “Don’t Want to Get Caught? Don’t Say It: The Use of EMOJIS in Online Human Sex Trafficking Ads.”  51st Hawaii International Conference on System Sciences, HICSS51, IEEE Computer Society, January 2018.

are applications of this model.  Our current work below on how to do an automated census is also an application of this model.  Basically I look at the above model as a guide to creating KM Strategy and the knowledge content process by helping to identify needed knowledge and the tools/filters needed to obtain that knowledge

Kelly, J., Jennex, M.E., Abhari, K., Durcikova, A., and Frost, E., (2019). Data in the Wild: A KM Approach to doing a Census Without Asking Anyone and the Issue of Privacy.  52nd Hawaii International Conference on System Sciences, HICSS52, IEEE Computer Society, January 2019.

Another area being investigated is the use of AI tools in content/concept mapping.  The tools are a huge advantage in looking at large numbers of document (unstructured data) and extracting terms and concepts and creating linkages.  A rudimentary example of this with respect to bigram plot analysis is:

Jennex, M.E.; Dittes, S.; Smolnik, S.; Croasdell, D.; King, D.: Knowledge, Innovation, and Entrepreneurial Systems at HICSS, Communications of the Association for Information Systems, In Press  (accepted, finalized, waiting to be published)

So while many organizations may not be using AI tools, I expect they will be real soon in their KM programs. (let me know if you want to see any of these papers)....murray jennex


-----Original Message-----
From: Matt Moore innotecture@... [sikmleaders]
To: Yahoo! Inc.
Sent: Thu, Oct 25, 2018 8:15 pm
Subject: [sikmleaders] Is anyone actually using AI?

Hi,

Is anyone using "Artificial Intelligence" as part of their Knowledge Management programs? I will let you define that term however you wish.

This is to prep for a talk that I am giving in a few months. My impression is that very few people are using this on a day-to-day basis but I am very curious to find out if they are.

Regards,

Matt


------------------------------------
Posted by: Matt Moore <innotecture@...>
------------------------------------


------------------------------------

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Murray Jennex
 

curious, have you actually looked at my model?  it isn't the tradition dikw model.  It was developed in conjunction with a defense contractor as a way of designing a KMS.  your comments are interesting but probably not well based....murray


-----Original Message-----
From: Stephen Bounds km@... [sikmleaders]
To: sikmleaders
Sent: Sat, Oct 27, 2018 11:50 pm
Subject: Re: [sikmleaders] Is anyone actually using AI?



Hi Murray,
Whether models are understood or not is neither here nor there. Something can be widely understood but wrong (see: 19th century aether and miasma).
While my preference is always to adopt existing models where possible, it is essential that we critically assess them and ensure they have a sound theoretical basis for their adoption.
DIKW has multiple flaws, not least that it lacks rigor around the definitions of data, information, knowledge and wisdom. I'll just quote Patrick Lambe verbatim since he said it all really well about a decade ago:
It’s important to understand the origins of a model to understand what it was designed for. The DIKW model emerged out of the struggles of computer science and information science through the late 1970s and early 1980s to legitimise themselves as strategic disciplines for the enterprise. For the data managers, the struggle was to get their organisations to treat data as a strategic resource, so establishing a relationship to information that fed decisions based on knowledge made a lot of sense. For the information managers the “downwards” link to data gave them a structure to work from, and the “upwards” link to knowledge gave them legitimacy in the eyes of senior management.

So while it had utility for data and information managers, the hierarchy was never designed to accommodate the far more complex world uncovered by knowledge management, and as Dave [Snowden] points out, it completely fails to acknowledge the naturalistic ways that data, information and knowledge interact. For example, it does not reflect the fact that data is a very small subset of repeatable information, abstracted and structured for mechanical processing based on knowledge. Data is the product of a knowledge-driven, purposeful piece of design work. The DIKW model implies the opposite, that knowledge is the product of a series of operations upon data. The model also completely fails to account for the sea of knowledge activity in an enterprise which is never informationalised or structured as data. In the natural world, data is the product of a very small component of knowledge activity.

From the data manager’s point of view, the problem in the enterprise is “we have all of this data sitting around, think of what we could do with it if we could figure out how to squeeze insight out of it”. While this is a legitimate question, the knowledge manager has discovered rather painfully, that you need to go back to the contexts that created the data and the knowledge activities the data supports, in order to figure out how the data can be manipulated for greater advantage. You can’t get there by performing a series of logical transformations on the data to create information, and then another series of operations to create knowledge.

So DIKW is a managerial model intended to explain how data can be leveraged as an enterprise resource. It has no practical value for guiding action beyond the D-I interface where it has limited value, explains almost nothing about knowledge, and its references to wisdom have always been completely without substantive or actionable content.
Any attempt to implement a management plan through the lens of big data is just as reductive and flawed as other attempts to understand organisations through a data funnel. Use of complex adaptive systems theory and agent-based models of behaviour (eg AKI cycles) may not lend themselves to a single slide, but I believe they offer a far more promising and robust way to explain systems failure and success.
Cheers,
Stephen.
====================================
Stephen Bounds
Executive, Information Management
Cordelta
E: stephen.bounds@...
M: 0401 829 096
====================================
On 28/10/2018 11:30 AM, Murray Jennex murphjen@... [sikmleaders] wrote:
 
I persist with the pyramid model as it is a known visualization that all understand.  The real value of my model is when you look at in conjunction with the inverted pyramid reflecting reality, i.e. that we've created vast amounts of knowledge and wisdom off of relatively small amounts of data until the advent of IoT and Big Data and that the purpose of KM is to focus organizations on that knowledge and actionable intelligence needed to achieve business objectives.  I don't see that shown very clearly on your model below.  As to the use of agents, they are built in as the filters I show on the pyramid include technical and human elements.  While I see my model as being very clear on relationships and processes, i don't get that with your model below.  All that said, models help understanding so which ever model works for you is fine.  Note, though that I do think my model showing how large IoT and Big Data is with respect to regular data, information, and knowledge is a crucial point to make to those trying to understand why big data and IoT is so difficult to manage and for showing how it integrates into KM...murray

Just wondering, are you biased against traditional models and have to use something different?


-----Original Message-----
From: Stephen Bounds km@... [sikmleaders]
To: sikmleaders
Sent: Sat, Oct 27, 2018 1:25 pm
Subject: Re: [sikmleaders] Is anyone actually using AI? [1 Attachment]

[Attachment(s) from Stephen Bounds included below]

Hi Murray,
Thanks for your linked article. I'm curious about why you are persisting with the knowledge pyramid.
Adding extra layers of data at the bottom really just highlights the weakness of the DIKW model to me. Wouldn't it be much simpler to use a hierarchical agent model? ie
This still reflects the a system's use of multiple agents to achieve data processing, but doesn't artificially force things into a rigid pyramid structure.
To return to Matt's question, I think many of the AI uses in KM are very subtle and typically not thought of as "KM". I agree with others that mostly we are using AI as a recommendation engine (whether for search results or taxonomy classification). It's very rare that we provide AI agents with significant autonomy in actual decision-making. The closest I know would be the use of AVR to send user's to the correct support agent -- and it could be argued that this is just another form of recommendation in any case.
Cheers,
Stephen.
====================================
Stephen Bounds
Executive, Information Management
Cordelta
E: stephen.bounds@...
M: 0401 829 096
====================================
On 27/10/2018 8:01 AM, Murray Jennex murphjen@... [sikmleaders] wrote:
 
basically I see AI and related technologies as knowledge discovery filters on large data sets such as IoT and Big Data.  My revised knowledge pyramid:

Jennex, M.E., (2017).  “Big Data, the Internet of Things and the Revised Knowledge Pyramid,” Data Base for Advances in Information Systems, 48(4), pp. 69-79.

shows AI, machine learning, analytics, and other technologies as filters on real world data to create KM knowledge.  The research I mentioned earlier on Identifying victims of human sex trafficking:

Hultgren, M.; Whitney, J.; Jennex, M.E.; Elkins, A. (2018).  A Knowledge Management Approach for Assisting in Identifying Victims of Human Sex Trafficking, Communications of the Association for Information Systems, volume 42, article 23, pp. 602-620.  Available at http://aisel.aisnet.org/cais/vol42/iss1/23/

Whitney, J., Jennex, M.E., Elkins, A., Frost, E., (2018).  “Don’t Want to Get Caught? Don’t Say It: The Use of EMOJIS in Online Human Sex Trafficking Ads.”  51st Hawaii International Conference on System Sciences, HICSS51, IEEE Computer Society, January 2018.

are applications of this model.  Our current work below on how to do an automated census is also an application of this model.  Basically I look at the above model as a guide to creating KM Strategy and the knowledge content process by helping to identify needed knowledge and the tools/filters needed to obtain that knowledge

Kelly, J., Jennex, M.E., Abhari, K., Durcikova, A., and Frost, E., (2019). Data in the Wild: A KM Approach to doing a Census Without Asking Anyone and the Issue of Privacy.  52nd Hawaii International Conference on System Sciences, HICSS52, IEEE Computer Society, January 2019.

Another area being investigated is the use of AI tools in content/concept mapping.  The tools are a huge advantage in looking at large numbers of document (unstructured data) and extracting terms and concepts and creating linkages.  A rudimentary example of this with respect to bigram plot analysis is:

Jennex, M.E.; Dittes, S.; Smolnik, S.; Croasdell, D.; King, D.: Knowledge, Innovation, and Entrepreneurial Systems at HICSS, Communications of the Association for Information Systems, In Press  (accepted, finalized, waiting to be published)

So while many organizations may not be using AI tools, I expect they will be real soon in their KM programs. (let me know if you want to see any of these papers)....murray jennex


-----Original Message-----
From: Matt Moore innotecture@... [sikmleaders]
To: Yahoo! Inc.
Sent: Thu, Oct 25, 2018 8:15 pm
Subject: [sikmleaders] Is anyone actually using AI?

Hi,

Is anyone using "Artificial Intelligence" as part of their Knowledge Management programs? I will let you define that term however you wish.

This is to prep for a talk that I am giving in a few months. My impression is that very few people are using this on a day-to-day basis but I am very curious to find out if they are.

Regards,

Matt


------------------------------------
Posted by: Matt Moore <innotecture@...>
------------------------------------


------------------------------------

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<*> To visit your group on the web, go to:

<*> Your email settings:
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Murray Jennex
 

and as to managerial models, guess who has the money!  the managers so ensuring they understand what they are buying and why is probably more valuable then creating some obtuse model for the km person.  you still didn't really explain why a model that is NOT understood is still valuable while a model that IS understood isn't.  Why would a agent model be better?  I don't see it, I just see some model being used that is different.


-----Original Message-----
From: Stephen Bounds km@... [sikmleaders]
To: sikmleaders
Sent: Sat, Oct 27, 2018 11:50 pm
Subject: Re: [sikmleaders] Is anyone actually using AI?



Hi Murray,
Whether models are understood or not is neither here nor there. Something can be widely understood but wrong (see: 19th century aether and miasma).
While my preference is always to adopt existing models where possible, it is essential that we critically assess them and ensure they have a sound theoretical basis for their adoption.
DIKW has multiple flaws, not least that it lacks rigor around the definitions of data, information, knowledge and wisdom. I'll just quote Patrick Lambe verbatim since he said it all really well about a decade ago:
It’s important to understand the origins of a model to understand what it was designed for. The DIKW model emerged out of the struggles of computer science and information science through the late 1970s and early 1980s to legitimise themselves as strategic disciplines for the enterprise. For the data managers, the struggle was to get their organisations to treat data as a strategic resource, so establishing a relationship to information that fed decisions based on knowledge made a lot of sense. For the information managers the “downwards” link to data gave them a structure to work from, and the “upwards” link to knowledge gave them legitimacy in the eyes of senior management.

So while it had utility for data and information managers, the hierarchy was never designed to accommodate the far more complex world uncovered by knowledge management, and as Dave [Snowden] points out, it completely fails to acknowledge the naturalistic ways that data, information and knowledge interact. For example, it does not reflect the fact that data is a very small subset of repeatable information, abstracted and structured for mechanical processing based on knowledge. Data is the product of a knowledge-driven, purposeful piece of design work. The DIKW model implies the opposite, that knowledge is the product of a series of operations upon data. The model also completely fails to account for the sea of knowledge activity in an enterprise which is never informationalised or structured as data. In the natural world, data is the product of a very small component of knowledge activity.

From the data manager’s point of view, the problem in the enterprise is “we have all of this data sitting around, think of what we could do with it if we could figure out how to squeeze insight out of it”. While this is a legitimate question, the knowledge manager has discovered rather painfully, that you need to go back to the contexts that created the data and the knowledge activities the data supports, in order to figure out how the data can be manipulated for greater advantage. You can’t get there by performing a series of logical transformations on the data to create information, and then another series of operations to create knowledge.

So DIKW is a managerial model intended to explain how data can be leveraged as an enterprise resource. It has no practical value for guiding action beyond the D-I interface where it has limited value, explains almost nothing about knowledge, and its references to wisdom have always been completely without substantive or actionable content.
Any attempt to implement a management plan through the lens of big data is just as reductive and flawed as other attempts to understand organisations through a data funnel. Use of complex adaptive systems theory and agent-based models of behaviour (eg AKI cycles) may not lend themselves to a single slide, but I believe they offer a far more promising and robust way to explain systems failure and success.
Cheers,
Stephen.
====================================
Stephen Bounds
Executive, Information Management
Cordelta
E: stephen.bounds@...
M: 0401 829 096
====================================
On 28/10/2018 11:30 AM, Murray Jennex murphjen@... [sikmleaders] wrote:
 
I persist with the pyramid model as it is a known visualization that all understand.  The real value of my model is when you look at in conjunction with the inverted pyramid reflecting reality, i.e. that we've created vast amounts of knowledge and wisdom off of relatively small amounts of data until the advent of IoT and Big Data and that the purpose of KM is to focus organizations on that knowledge and actionable intelligence needed to achieve business objectives.  I don't see that shown very clearly on your model below.  As to the use of agents, they are built in as the filters I show on the pyramid include technical and human elements.  While I see my model as being very clear on relationships and processes, i don't get that with your model below.  All that said, models help understanding so which ever model works for you is fine.  Note, though that I do think my model showing how large IoT and Big Data is with respect to regular data, information, and knowledge is a crucial point to make to those trying to understand why big data and IoT is so difficult to manage and for showing how it integrates into KM...murray

Just wondering, are you biased against traditional models and have to use something different?


-----Original Message-----
From: Stephen Bounds km@... [sikmleaders]
To: sikmleaders
Sent: Sat, Oct 27, 2018 1:25 pm
Subject: Re: [sikmleaders] Is anyone actually using AI? [1 Attachment]

[Attachment(s) from Stephen Bounds included below]

Hi Murray,
Thanks for your linked article. I'm curious about why you are persisting with the knowledge pyramid.
Adding extra layers of data at the bottom really just highlights the weakness of the DIKW model to me. Wouldn't it be much simpler to use a hierarchical agent model? ie
This still reflects the a system's use of multiple agents to achieve data processing, but doesn't artificially force things into a rigid pyramid structure.
To return to Matt's question, I think many of the AI uses in KM are very subtle and typically not thought of as "KM". I agree with others that mostly we are using AI as a recommendation engine (whether for search results or taxonomy classification). It's very rare that we provide AI agents with significant autonomy in actual decision-making. The closest I know would be the use of AVR to send user's to the correct support agent -- and it could be argued that this is just another form of recommendation in any case.
Cheers,
Stephen.
====================================
Stephen Bounds
Executive, Information Management
Cordelta
E: stephen.bounds@...
M: 0401 829 096
====================================
On 27/10/2018 8:01 AM, Murray Jennex murphjen@... [sikmleaders] wrote:
 
basically I see AI and related technologies as knowledge discovery filters on large data sets such as IoT and Big Data.  My revised knowledge pyramid:

Jennex, M.E., (2017).  “Big Data, the Internet of Things and the Revised Knowledge Pyramid,” Data Base for Advances in Information Systems, 48(4), pp. 69-79.

shows AI, machine learning, analytics, and other technologies as filters on real world data to create KM knowledge.  The research I mentioned earlier on Identifying victims of human sex trafficking:

Hultgren, M.; Whitney, J.; Jennex, M.E.; Elkins, A. (2018).  A Knowledge Management Approach for Assisting in Identifying Victims of Human Sex Trafficking, Communications of the Association for Information Systems, volume 42, article 23, pp. 602-620.  Available at http://aisel.aisnet.org/cais/vol42/iss1/23/

Whitney, J., Jennex, M.E., Elkins, A., Frost, E., (2018).  “Don’t Want to Get Caught? Don’t Say It: The Use of EMOJIS in Online Human Sex Trafficking Ads.”  51st Hawaii International Conference on System Sciences, HICSS51, IEEE Computer Society, January 2018.

are applications of this model.  Our current work below on how to do an automated census is also an application of this model.  Basically I look at the above model as a guide to creating KM Strategy and the knowledge content process by helping to identify needed knowledge and the tools/filters needed to obtain that knowledge

Kelly, J., Jennex, M.E., Abhari, K., Durcikova, A., and Frost, E., (2019). Data in the Wild: A KM Approach to doing a Census Without Asking Anyone and the Issue of Privacy.  52nd Hawaii International Conference on System Sciences, HICSS52, IEEE Computer Society, January 2019.

Another area being investigated is the use of AI tools in content/concept mapping.  The tools are a huge advantage in looking at large numbers of document (unstructured data) and extracting terms and concepts and creating linkages.  A rudimentary example of this with respect to bigram plot analysis is:

Jennex, M.E.; Dittes, S.; Smolnik, S.; Croasdell, D.; King, D.: Knowledge, Innovation, and Entrepreneurial Systems at HICSS, Communications of the Association for Information Systems, In Press  (accepted, finalized, waiting to be published)

So while many organizations may not be using AI tools, I expect they will be real soon in their KM programs. (let me know if you want to see any of these papers)....murray jennex


-----Original Message-----
From: Matt Moore innotecture@... [sikmleaders]
To: Yahoo! Inc.
Sent: Thu, Oct 25, 2018 8:15 pm
Subject: [sikmleaders] Is anyone actually using AI?

Hi,

Is anyone using "Artificial Intelligence" as part of their Knowledge Management programs? I will let you define that term however you wish.

This is to prep for a talk that I am giving in a few months. My impression is that very few people are using this on a day-to-day basis but I am very curious to find out if they are.

Regards,

Matt


------------------------------------
Posted by: Matt Moore <innotecture@...>
------------------------------------


------------------------------------

Yahoo Groups Links

<*> To visit your group on the web, go to:

<*> Your email settings:
    Individual Email | Traditional

<*> To change settings online go to:
    (Yahoo! ID required)

<*> To change settings via email:

<*> To unsubscribe from this group, send an email to:

<*> Your use of Yahoo Groups is subject to:








Beatriz Benezra
 

Hi,
I´m finishing my Master in Data Science. My research is about Knowledge Data Discovery (KDD). KDD is about extract valid data patterns that are insights for the business to start the construction of new Knowledge. Data mining and IA algorithms are just part of the entire KDD process. When you think about apply KDD in a Data warehouse (data warehouse holds 100% composed by business data) then outcomes are entirely related to business. As well, you can use external data to generate more different patterns. Going further, is possible to combine structured and unstructured data (like images, texts, etc) to improve even more the outcomes. By the business side, KDD process matches with the innovation process. With the advantage that you don’t need to enroll all the organization. A multidisciplinary team can validate the patters.
That is a very simplify explanation for KDD. The process is interactive and iterative and a bit more complex.
Hope helped!
Regards,

Beatriz Benezra
About me: https://www.linkedin.com/in/beatriz-benezra-dehtear-hcmp-mba-pm-g-belt-8046098/


De: sikmleaders@yahoogroups.com [mailto:sikmleaders@yahoogroups.com]
Enviada em: sábado, 27 de outubro de 2018 23:31
Para: sikmleaders@yahoogroups.com
Assunto: Re: [sikmleaders] Is anyone actually using AI?


Hi Stephen,

Responding to your mail to Murray. The DIKW model does not change. Technology has helped in mining more data. We are generating huge data as compared to earlier days, which is resulting in much more information and better knowledge. This has lead to better insight and understanding of any system, process or phenomenon including 'purchasing behaviour'.

IOT for example is just replacing humans and allowing machines to talk to each other and make decisions. For example instead of me taking a decision to buy my dairy products, my fridge takes the decision. IOT is able to generate lot of data which is being used by companies like ‘Amazon’ to become more knowledgeable about the customers. IOT is a means to create large amount of data. Hence the image that you had shared in the email is not a correct representation.

Regarding ‘AI uses of KM being subtle”, what I found is that AI uses of KM are not getting bundled under AI. Its mainly because those who are doing are not KM practitioners and they do not realise that they are leveraging knowledge. For example along with suggesting agents to resolve an incident, the tool itself can resolve incidents. There are cases where the tool uses pattern mapping both at problem and resolution level and understands the solution and starts applying. However most examples that I have come across are based on classifiers (unsupervised learning or pattern mapping).

In many cases we are not allowing machines to take a decision because of legal issues. In health sector, machines are able to predict diseases much better than doctors. But they are being used as enablers than decision makers.

Regards

Randhir

On Sun, Oct 28, 2018 at 6:00 AM Murray Jennex murphjen@aol.com<mailto:murphjen@aol.com> [sikmleaders] <sikmleaders@yahoogroups.com<mailto:sikmleaders@yahoogroups.com>> wrote:

[Attachment(s) from Murray Jennex included below]

I persist with the pyramid model as it is a known visualization that all understand. The real value of my model is when you look at in conjunction with the inverted pyramid reflecting reality, i.e. that we've created vast amounts of knowledge and wisdom off of relatively small amounts of data until the advent of IoT and Big Data and that the purpose of KM is to focus organizations on that knowledge and actionable intelligence needed to achieve business objectives. I don't see that shown very clearly on your model below. As to the use of agents, they are built in as the filters I show on the pyramid include technical and human elements. While I see my model as being very clear on relationships and processes, i don't get that with your model below. All that said, models help understanding so which ever model works for you is fine. Note, though that I do think my model showing how large IoT and Big Data is with respect to regular data, information, and knowledge is a crucial point to make to those trying to understand why big data and IoT is so difficult to manage and for showing how it integrates into KM...murray

Just wondering, are you biased against traditional models and have to use something different?

-----Original Message-----
From: Stephen Bounds km@bounds.net.au<mailto:km@bounds.net.au> [sikmleaders] <sikmleaders@yahoogroups.com<mailto:sikmleaders@yahoogroups.com>>
To: sikmleaders <sikmleaders@yahoogroups.com<mailto:sikmleaders@yahoogroups.com>>
Sent: Sat, Oct 27, 2018 1:25 pm
Subject: Re: [sikmleaders] Is anyone actually using AI? [1 Attachment]
[Attachment(s) from Stephen Bounds included below]
Hi Murray,
Thanks for your linked article. I'm curious about why you are persisting with the knowledge pyramid.
Adding extra layers of data at the bottom really just highlights the weakness of the DIKW model to me. Wouldn't it be much simpler to use a hierarchical agent model? ie
Erro! O nome de arquivo não foi especificado.
This still reflects the a system's use of multiple agents to achieve data processing, but doesn't artificially force things into a rigid pyramid structure.
To return to Matt's question, I think many of the AI uses in KM are very subtle and typically not thought of as "KM". I agree with others that mostly we are using AI as a recommendation engine (whether for search results or taxonomy classification). It's very rare that we provide AI agents with significant autonomy in actual decision-making. The closest I know would be the use of AVR to send user's to the correct support agent -- and it could be argued that this is just another form of recommendation in any case.
Cheers,
Stephen.

====================================

Stephen Bounds

Executive, Information Management

Cordelta

E: stephen.bounds@cordelta.com<mailto:stephen.bounds@cordelta.com>

M: 0401 829 096

====================================
On 27/10/2018 8:01 AM, Murray Jennex murphjen@aol.com<mailto:murphjen@aol.com> [sikmleaders] wrote:

basically I see AI and related technologies as knowledge discovery filters on large data sets such as IoT and Big Data. My revised knowledge pyramid:

Jennex, M.E., (2017). “Big Data, the Internet of Things and the Revised Knowledge Pyramid,” Data Base for Advances in Information Systems, 48(4), pp. 69-79.

shows AI, machine learning, analytics, and other technologies as filters on real world data to create KM knowledge. The research I mentioned earlier on Identifying victims of human sex trafficking:

Hultgren, M.; Whitney, J.; Jennex, M.E.; Elkins, A. (2018). A Knowledge Management Approach for Assisting in Identifying Victims of Human Sex Trafficking, Communications of the Association for Information Systems, volume 42, article 23, pp. 602-620. Available at http://aisel.aisnet.org/cais/vol42/iss1/23/<http://aisel..aisnet.org/cais/vol42/iss1/23/>

Whitney, J., Jennex, M.E., Elkins, A., Frost, E., (2018). “Don’t Want to Get Caught? Don’t Say It: The Use of EMOJIS in Online Human Sex Trafficking Ads.” 51st Hawaii International Conference on System Sciences, HICSS51, IEEE Computer Society, January 2018.

are applications of this model. Our current work below on how to do an automated census is also an application of this model. Basically I look at the above model as a guide to creating KM Strategy and the knowledge content process by helping to identify needed knowledge and the tools/filters needed to obtain that knowledge

Kelly, J., Jennex, M.E., Abhari, K., Durcikova, A., and Frost, E., (2019). Data in the Wild: A KM Approach to doing a Census Without Asking Anyone and the Issue of Privacy. 52nd Hawaii International Conference on System Sciences, HICSS52, IEEE Computer Society, January 2019.

Another area being investigated is the use of AI tools in content/concept mapping. The tools are a huge advantage in looking at large numbers of document (unstructured data) and extracting terms and concepts and creating linkages. A rudimentary example of this with respect to bigram plot analysis is:

Jennex, M.E.; Dittes, S.; Smolnik, S.; Croasdell, D.; King, D.: Knowledge, Innovation, and Entrepreneurial Systems at HICSS, Communications of the Association for Information Systems, In Press (accepted, finalized, waiting to be published)

So while many organizations may not be using AI tools, I expect they will be real soon in their KM programs. (let me know if you want to see any of these papers)....murray jennex

-----Original Message-----
From: Matt Moore innotecture@yahoo.com<mailto:innotecture@yahoo.com> [sikmleaders] <sikmleaders@yahoogroups.com><mailto:sikmleaders@yahoogroups.com>
To: Yahoo! Inc. <sikmleaders@yahoogroups.com><mailto:sikmleaders@yahoogroups.com>
Sent: Thu, Oct 25, 2018 8:15 pm
Subject: [sikmleaders] Is anyone actually using AI?
Hi,

Is anyone using "Artificial Intelligence" as part of their Knowledge Management programs? I will let you define that term however you wish.

This is to prep for a talk that I am giving in a few months. My impression is that very few people are using this on a day-to-day basis but I am very curious to find out if they are.

Regards,

Matt


------------------------------------
Posted by: Matt Moore <innotecture@yahoo.com<mailto:innotecture@yahoo.com>>
------------------------------------


------------------------------------

Yahoo Groups Links







[https://ipmcdn.avast.com/images/icons/icon-envelope-tick-round-orange-animated-no-repeat-v1.gif]<https://www.avast.com/sig-email?utm_medium=email&utm_source=link&utm_campaign=sig-email&utm_content=emailclient> Livre de vírus. www.avast.com<https://www.avast.com/sig-email?utm_medium=email&utm_source=link&utm_campaign=sig-email&utm_content=emailclient>.


Sandra Lopez
 

In the case of the models, I have used to model of the particular dynamic knowledge of the company and to apply the best strategies of km according this

Enviado desde mi iPhone

El 27/10/2018, a la(s) 3:25 p. m., Stephen Bounds km@... [sikmleaders] <sikmleaders@...> escribió:

 

Hi Murray,

Thanks for your linked article. I'm curious about why you are persisting with the knowledge pyramid.

Adding extra layers of data at the bottom really just highlights the weakness of the DIKW model to me. Wouldn't it be much simpler to use a hierarchical agent model? ie

This still reflects the a system's use of multiple agents to achieve data processing, but doesn't artificially force things into a rigid pyramid structure.

To return to Matt's question, I think many of the AI uses in KM are very subtle and typically not thought of as "KM". I agree with others that mostly we are using AI as a recommendation engine (whether for search results or taxonomy classification). It's very rare that we provide AI agents with significant autonomy in actual decision-making. The closest I know would be the use of AVR to send user's to the correct support agent -- and it could be argued that this is just another form of recommendation in any case.

Cheers,
Stephen.

====================================
Stephen Bounds
Executive, Information Management
Cordelta
E: stephen.bounds@...
M: 0401 829 096
====================================
On 27/10/2018 8:01 AM, Murray Jennex murphjen@... [sikmleaders] wrote:
 

basically I see AI and related technologies as knowledge discovery filters on large data sets such as IoT and Big Data.  My revised knowledge pyramid:


Jennex, M.E., (2017).  “Big Data, the Internet of Things and the Revised Knowledge Pyramid,” Data Base for Advances in Information Systems, 48(4), pp. 69-79.

shows AI, machine learning, analytics, and other technologies as filters on real world data to create KM knowledge.  The research I mentioned earlier on Identifying victims of human sex trafficking:

Hultgren, M.; Whitney, J.; Jennex, M.E.; Elkins, A. (2018).  A Knowledge Management Approach for Assisting in Identifying Victims of Human Sex Trafficking, Communications of the Association for Information Systems, volume 42, article 23, pp. 602-620.  Available at http://aisel.aisnet.org/cais/vol42/iss1/23/

Whitney, J., Jennex, M.E., Elkins, A., Frost, E., (2018).  “Don’t Want to Get Caught? Don’t Say It: The Use of EMOJIS in Online Human Sex Trafficking Ads.”  51st Hawaii International Conference on System Sciences, HICSS51, IEEE Computer Society, January 2018.

are applications of this model.  Our current work below on how to do an automated census is also an application of this model.  Basically I look at the above model as a guide to creating KM Strategy and the knowledge content process by helping to identify needed knowledge and the tools/filters needed to obtain that knowledge

Kelly, J., Jennex, M.E., Abhari, K., Durcikova, A., and Frost, E., (2019). Data in the Wild: A KM Approach to doing a Census Without Asking Anyone and the Issue of Privacy.  52nd Hawaii International Conference on System Sciences, HICSS52, IEEE Computer Society, January 2019.

Another area being investigated is the use of AI tools in content/concept mapping.  The tools are a huge advantage in looking at large numbers of document (unstructured data) and extracting terms and concepts and creating linkages.  A rudimentary example of this with respect to bigram plot analysis is:

Jennex, M.E.; Dittes, S.; Smolnik, S.; Croasdell, D.; King, D.: Knowledge, Innovation, and Entrepreneurial Systems at HICSS, Communications of the Association for Information Systems, In Press  (accepted, finalized, waiting to be published)

So while many organizations may not be using AI tools, I expect they will be real soon in their KM programs. (let me know if you want to see any of these papers)....murray jennex


-----Original Message-----
From: Matt Moore innotecture@... [sikmleaders]
To: Yahoo! Inc.
Sent: Thu, Oct 25, 2018 8:15 pm
Subject: [sikmleaders] Is anyone actually using AI?

Hi,

Is anyone using "Artificial Intelligence" as part of their Knowledge Management programs? I will let you define that term however you wish.

This is to prep for a talk that I am giving in a few months. My impression is that very few people are using this on a day-to-day basis but I am very curious to find out if they are.

Regards,

Matt


------------------------------------
Posted by: Matt Moore <innotecture@...>
------------------------------------


------------------------------------

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Stephen Bounds
 

Hi Murray,

Obviously a model must be understood to be valuable. But this is a necessary prerequisite for usefulness, not a sufficient one. My point is just that GIGO applies: a bad model means that your predictions about organisational outcomes will be wrong, except by accident.

I see KM as a multidisciplinary science and as such, we shouldn't be driven primarily by who has the money but by a search for the truth. I have nothing against management tools and methods that build upon the scientific evidence base for KM; but unfortunately, too much of the history of KM is littered with models that are pure invention and magical thinking from an evidence point of view.

For example, I have some issues with how Cynefin is often misused as a "scientific" model, but I can't argue with its merits as a management tool. It provides useful and tangible strategies to managers, while retaining a sound basis in complexity theory.

On the other hand DIKW is -- at best -- a description of a process methodology for creating management reports using definitions for data, information, knowledge and wisdom that are tightly aligned to the methodology. It is a self-fulfilling prophecy with terms defined in a way that have little external rigour or application.

At worst, DIKW sells a damaging misconception. There is an implication that it is possible to apply a unitary, command and control-inspired approach to knowledge creation and application within organisations that simply isn't borne out by the evidence of how they operate in practice. Yes, I did read through your paper but I still do not believe that your revised knowledge pyramid addresses this fundamental problem.

Cheers,
Stephen.

====================================
Stephen Bounds
Executive, Information Management
Cordelta
E: stephen.bounds@cordelta.com
M: 0401 829 096
====================================

On 28/10/2018 5:57 PM, Murray Jennex murphjen@aol.com [sikmleaders] wrote:

and as to managerial models, guess who has the money!  the managers so ensuring they understand what they are buying and why is probably more valuable then creating some obtuse model for the km person.  you still didn't really explain why a model that is NOT understood is still valuable while a model that IS understood isn't. Why would a agent model be better?  I don't see it, I just see some model being used that is different.


-----Original Message-----
From: Stephen Bounds km@bounds.net.au [sikmleaders] <sikmleaders@yahoogroups.com>
To: sikmleaders <sikmleaders@yahoogroups.com>
Sent: Sat, Oct 27, 2018 11:50 pm
Subject: Re: [sikmleaders] Is anyone actually using AI?



Hi Murray,
Whether models are _*understood*_ or not is neither here nor there. Something can be widely understood but wrong (see: 19th century aether and miasma).
While my preference is always to adopt existing models where possible, it is essential that we critically assess them and ensure they have a sound theoretical basis for their adoption.
DIKW has multiple flaws, not least that it lacks rigor around the definitions of data, information, knowledge and wisdom. I'll just quote Patrick Lambe verbatim since he said it all really well <http://www.greenchameleon.com/gc/blog_detail/from_data_with_love/> about a decade ago:

It’s important to understand the origins of a model to
understand what it was designed for. The DIKW model emerged
out of the struggles of computer science and information
science through the late 1970s and early 1980s to legitimise
themselves as strategic disciplines for the enterprise. For
the data managers, the struggle was to get their organisations
to treat data as a strategic resource, so establishing a
relationship to information that fed decisions based on
knowledge made a lot of sense. For the information managers
the “downwards” link to data gave them a structure to work
from, and the “upwards” link to knowledge gave them legitimacy
in the eyes of senior management.

So while it had utility for data and information managers, the
hierarchy was never designed to accommodate the far more
complex world uncovered by knowledge management, and as Dave
[Snowden] points out, it completely fails to acknowledge the
naturalistic ways that data, information and knowledge
interact. For example, it does not reflect the fact that data
is a very small subset of repeatable information, abstracted
and structured for mechanical processing based on knowledge.
Data is the product of a knowledge-driven, purposeful piece of
design work. The DIKW model implies the opposite, that
knowledge is the product of a series of operations upon data.
The model also completely fails to account for the sea of
knowledge activity in an enterprise which is never
informationalised or structured as data. In the natural world,
data is the product of a very small component of knowledge
activity.

From the data manager’s point of view, the problem in the
enterprise is “we have all of this data sitting around, think
of what we could do with it if we could figure out how to
squeeze insight out of it”. While this is a legitimate
question, the knowledge manager has discovered rather
painfully, that you need to go back to the contexts that
created the data and the knowledge activities the data
supports, in order to figure out how the data can be
manipulated for greater advantage. You can’t get there by
performing a series of logical transformations on the data to
create information, and then another series of operations to
create knowledge.

So DIKW is a managerial model intended to explain how data can
be leveraged as an enterprise resource. It has no practical
value for guiding action beyond the D-I interface where it has
limited value, explains almost nothing about knowledge, and
its references to wisdom have always been completely without
substantive or actionable content.

Any attempt to implement a management plan through the lens of big data is just as reductive and flawed as other attempts to understand organisations through a data funnel. Use of complex adaptive systems theory and agent-based models of behaviour (eg AKI cycles <https://realkm.com/2016/01/07/a-model-for-understanding-knowledge-systems/>) may not lend themselves to a single slide, but I believe they offer a far more promising and robust way to explain systems failure and success.
Cheers,
Stephen.
====================================
Stephen Bounds
Executive, Information Management
Cordelta
E:stephen.bounds@cordelta.com <mailto:stephen.bounds@cordelta.com>
M: 0401 829 096
====================================
On 28/10/2018 11:30 AM, Murray Jennex murphjen@aol.com <mailto:murphjen@aol.com> [sikmleaders] wrote:
I persist with the pyramid model as it is a known visualization that all understand. The real value of my model is when you look at in conjunction with the inverted pyramid reflecting reality, i.e. that we've created vast amounts of knowledge and wisdom off of relatively small amounts of data until the advent of IoT and Big Data and that the purpose of KM is to focus organizations on that knowledge and actionable intelligence needed to achieve business objectives.  I don't see that shown very clearly on your model below.  As to the use of agents, they are built in as the filters I show on the pyramid include technical and human elements.  While I see my model as being very clear on relationships and processes, i don't get that with your model below.  All that said, models help understanding so which ever model works for you is fine. Note, though that I do think my model showing how large IoT and Big Data is with respect to regular data, information, and knowledge is a crucial point to make to those trying to understand why big data and IoT is so difficult to manage and for showing how it integrates into KM...murray

Just wondering, are you biased against traditional models and have to use something different?


-----Original Message-----
From: Stephen Bounds km@bounds.net.au <mailto:km@bounds.net.au> [sikmleaders] <sikmleaders@yahoogroups.com> <mailto:sikmleaders@yahoogroups.com>
To: sikmleaders <sikmleaders@yahoogroups.com> <mailto:sikmleaders@yahoogroups.com>
Sent: Sat, Oct 27, 2018 1:25 pm
Subject: Re: [sikmleaders] Is anyone actually using AI? [1 Attachment]

[Attachment(s) <#TopText> from Stephen Bounds included below]

Hi Murray,
Thanks for your linked article. I'm curious about why you are persisting with the knowledge pyramid.
Adding extra layers of data at the bottom really just highlights the weakness of the DIKW model to me. Wouldn't it be much simpler to use a hierarchical agent model? ie
This still reflects the a system's use of multiple agents to achieve data processing, but doesn't artificially force things into a rigid pyramid structure.
To return to Matt's question, I think many of the AI uses in KM are very subtle and typically not thought of as "KM". I agree with others that mostly we are using AI as a recommendation engine (whether for search results or taxonomy classification). It's very rare that we provide AI agents with significant autonomy in actual decision-making. The closest I know would be the use of AVR to send user's to the correct support agent -- and it could be argued that this is just another form of recommendation in any case.
Cheers,
Stephen.
====================================
Stephen Bounds
Executive, Information Management
Cordelta
E:stephen.bounds@cordelta.com <mailto:stephen.bounds@cordelta.com>
M: 0401 829 096
====================================
On 27/10/2018 8:01 AM, Murray Jennex murphjen@aol.com <mailto:murphjen@aol.com> [sikmleaders] wrote:
basically I see AI and related technologies as knowledge discovery filters on large data sets such as IoT and Big Data.  My revised knowledge pyramid:

Jennex, M.E., (2017).  “Big Data, the Internet of Things and the Revised Knowledge Pyramid,” Data Base for Advances in Information Systems, 48(4), pp. 69-79.

shows AI, machine learning, analytics, and other technologies as filters on real world data to create KM knowledge.  The research I mentioned earlier on Identifying victims of human sex trafficking:

Hultgren, M.; Whitney, J.; Jennex, M.E.; Elkins, A. (2018).  A Knowledge Management Approach for Assisting in Identifying Victims of Human Sex Trafficking, Communications of the Association for Information Systems, volume 42, article 23, pp. 602-620.  Available at http://aisel.aisnet.org/cais/vol42/iss1/23/

Whitney, J., Jennex, M.E., Elkins, A., Frost, E., (2018).  “Don’t Want to Get Caught? Don’t Say It: The Use of EMOJIS in Online Human Sex Trafficking Ads.”  51^st Hawaii International Conference on System Sciences, HICSS51, IEEE Computer Society, January 2018.

are applications of this model.  Our current work below on how to do an automated census is also an application of this model. Basically I look at the above model as a guide to creating KM Strategy and the knowledge content process by helping to identify needed knowledge and the tools/filters needed to obtain that knowledge

Kelly, J., Jennex, M.E., Abhari, K., Durcikova, A., and Frost, E., (2019). Data in the Wild: A KM Approach to doing a Census Without Asking Anyone and the Issue of Privacy.  52^nd Hawaii International Conference on System Sciences, HICSS52, IEEE Computer Society, January 2019.

Another area being investigated is the use of AI tools in content/concept mapping.  The tools are a huge advantage in looking at large numbers of document (unstructured data) and extracting terms and concepts and creating linkages.  A rudimentary example of this with respect to bigram plot analysis is:

Jennex, M.E.; Dittes, S.; Smolnik, S.; Croasdell, D.; King, D.: Knowledge, Innovation, and Entrepreneurial Systems at HICSS, Communications of the Association for Information Systems, In Press  (accepted, finalized, waiting to be published)

So while many organizations may not be using AI tools, I expect they will be real soon in their KM programs. (let me know if you want to see any of these papers)....murray jennex


-----Original Message-----
From: Matt Moore innotecture@yahoo.com <mailto:innotecture@yahoo.com> [sikmleaders] <sikmleaders@yahoogroups.com> <mailto:sikmleaders@yahoogroups.com>
To: Yahoo! Inc. <sikmleaders@yahoogroups.com> <mailto:sikmleaders@yahoogroups.com>
Sent: Thu, Oct 25, 2018 8:15 pm
Subject: [sikmleaders] Is anyone actually using AI?

Hi,

Is anyone using "Artificial Intelligence" as part of their Knowledge Management programs? I will let you define that term however you wish.

This is to prep for a talk that I am giving in a few months. My impression is that very few people are using this on a day-to-day basis but I am very curious to find out if they are.

Regards,

Matt


------------------------------------
Posted by: Matt Moore <innotecture@yahoo.com <mailto:innotecture@yahoo.com>>
------------------------------------


------------------------------------

Yahoo Groups Links


sikmleaders-fullfeatured@yahoogroups.com <mailto:sikmleaders-fullfeatured@yahoogroups.com>









Murray Jennex
 

a couple of points Stephen,
first this sounds like a personal attack against me and I'm not sure how I annoyed you.second, it doesn't appear that you have even looked at my model or the papers that go with it

-----Original Message-----
From: Stephen Bounds km@bounds.net.au [sikmleaders] <sikmleaders@yahoogroups.com>
To: sikmleaders <sikmleaders@yahoogroups.com>
Sent: Mon, Oct 29, 2018 2:04 pm
Subject: Re: [sikmleaders] The case for/against DIKW

#yiv2620843843 #yiv2620843843 -- #yiv2620843843 .yiv2620843843ygrp-photo-title{ clear:both;font-size:smaller;min-height:15px;overflow:hidden;text-align:center;width:75px;} #yiv2620843843 div.yiv2620843843ygrp-photo{ background-position:center;background-repeat:no-repeat;background-color:white;border:1px solid black;min-height:62px;width:62px;} #yiv2620843843 div.yiv2620843843photo-title a, #yiv2620843843 div.yiv2620843843photo-title a:active, #yiv2620843843 div.yiv2620843843photo-title a:hover, #yiv2620843843 div.yiv2620843843photo-title a:visited { text-decoration:none; } #yiv2620843843 div.yiv2620843843attach-table div.yiv2620843843attach-row { clear:both;} #yiv2620843843 div.yiv2620843843attach-table div.yiv2620843843attach-row div { float:left;} #yiv2620843843 p { clear:both;padding:15px 0 3px 0;overflow:hidden;} #yiv2620843843 div.yiv2620843843ygrp-file { width:30px;} #yiv2620843843 div.yiv2620843843attach-table div.yiv2620843843attach-row div div a { text-decoration:none;} #yiv2620843843 div.yiv2620843843attach-table div.yiv2620843843attach-row div div span { font-weight:normal;} #yiv2620843843 div.yiv2620843843ygrp-file-title { font-weight:bold;} #yiv2620843843 #yiv2620843843

Hi Murray, Obviously a model must be understood to be valuable. But this is a necessary prerequisite for usefulness, not a sufficient one. My point is just that GIGO applies: a bad model means that your predictions about organisational outcomes will be wrong, except by accident.
I see KM as a multidisciplinary science and as such, we shouldn't be driven primarily by who has the money but by a search for the truth. I have nothing against management tools and methods that build upon the scientific evidence base for KM; but unfortunately, too much of the history of KM is littered with models that are pure invention and magical thinking from an evidence point of view.
For example, I have some issues with how Cynefin is often misused as a "scientific" model, but I can't argue with its merits as a management tool. It provides useful and tangible strategies to managers, while retaining a sound basis in complexity theory. On the other hand DIKW is -- at best -- a description of a process methodology for creating management reports using definitions for data, information, knowledge and wisdom that are tightly aligned to the methodology. It is a self-fulfilling prophecy with terms defined in a way that have little external rigour or application.
At worst, DIKW sells a damaging misconception. There is an implication that it is possible to apply a unitary, command and control-inspired approach to knowledge creation and application within organisations that simply isn't borne out by the evidence of how they operate in practice. Yes, I did read through your paper but I still do not believe that your revised knowledge pyramid addresses this fundamental problem.
Cheers,
Stephen.
====================================
Stephen Bounds
Executive, Information Management
Cordelta
E: stephen.bounds@cordelta.com
M: 0401 829 096
==================================== On 28/10/2018 5:57 PM, Murray Jennex murphjen@aol.com [sikmleaders] wrote:


  and as to managerial models, guess who has the money!  the managers so ensuring they understand what they are buying and why is probably more valuable then creating some obtuse model for the km person.  you still didn't really explain why a model that is NOT understood is still valuable while a model that IS understood isn't.  Why would a agent model be better?  I don't see it, I just see some model being used that is different.


-----Original Message-----
From: Stephen Bounds km@bounds.net.au [sikmleaders] <sikmleaders@yahoogroups.com>
To: sikmleaders <sikmleaders@yahoogroups.com>
Sent: Sat, Oct 27, 2018 11:50 pm
Subject: Re: [sikmleaders] Is anyone actually using AI?



Hi Murray, Whether models are understood or not is neither here nor there. Something can be widely understood but wrong (see: 19th century aether and miasma).
While my preference is always to adopt existing models where possible, it is essential that we critically assess them and ensure they have a sound theoretical basis for their adoption. DIKW has multiple flaws, not least that it lacks rigor around the definitions of data, information, knowledge and wisdom. I'll just quote Patrick Lambe verbatim since he said it all really well about a decade ago:

It’s important to understand the origins of a model to understand what it was designed for. The DIKW model emerged out of the struggles of computer science and information science through the late 1970s and early 1980s to legitimise themselves as strategic disciplines for the enterprise. For the data managers, the struggle was to get their organisations to treat data as a strategic resource, so establishing a relationship to information that fed decisions based on knowledge made a lot of sense. For the information managers the “downwards” link to data gave them a structure to work from, and the “upwards” link to knowledge gave them legitimacy in the eyes of senior management.

So while it had utility for data and information managers, the hierarchy was never designed to accommodate the far more complex world uncovered by knowledge management, and as Dave [Snowden] points out, it completely fails to acknowledge the naturalistic ways that data, information and knowledge interact. For example, it does not reflect the fact that data is a very small subset of repeatable information, abstracted and structured for mechanical processing based on knowledge. Data is the product of a knowledge-driven, purposeful piece of design work. The DIKW model implies the opposite, that knowledge is the product of a series of operations upon data. The model also completely fails to account for the sea of knowledge activity in an enterprise which is never informationalised or structured as data. In the natural world, data is the product of a very small component of knowledge activity.

From the data manager’s point of view, the problem in the enterprise is “we have all of this data sitting around, think of what we could do with it if we could figure out how to squeeze insight out of it”. While this is a legitimate question, the knowledge manager has discovered rather painfully, that you need to go back to the contexts that created the data and the knowledge activities the data supports, in order to figure out how the data can be manipulated for greater advantage. You can’t get there by performing a series of logical transformations on the data to create information, and then another series of operations to create knowledge.

So DIKW is a managerial model intended to explain how data can be leveraged as an enterprise resource. It has no practical value for guiding action beyond the D-I interface where it has limited value, explains almost nothing about knowledge, and its references to wisdom have always been completely without substantive or actionable content.


Any attempt to implement a management plan through the lens of big data is just as reductive and flawed as other attempts to understand organisations through a data funnel. Use of complex adaptive systems theory and agent-based models of behaviour (eg AKI cycles) may not lend themselves to a single slide, but I believe they offer a far more promising and robust way to explain systems failure and success.
Cheers,
Stephen.
====================================
Stephen Bounds
Executive, Information Management
Cordelta
E: stephen.bounds@cordelta.com
M: 0401 829 096
==================================== On 28/10/2018 11:30 AM, Murray Jennex murphjen@aol.com [sikmleaders] wrote:


  I persist with the pyramid model as it is a known visualization that all understand.  The real value of my model is when you look at in conjunction with the inverted pyramid reflecting reality, i.e. that we've created vast amounts of knowledge and wisdom off of relatively small amounts of data until the advent of IoT and Big Data and that the purpose of KM is to focus organizations on that knowledge and actionable intelligence needed to achieve business objectives.  I don't see that shown very clearly on your model below.  As to the use of agents, they are built in as the filters I show on the pyramid include technical and human elements.  While I see my model as being very clear on relationships and processes, i don't get that with your model below.  All that said, models help understanding so which ever model works for you is fine.  Note, though that I do think my model showing how large IoT and Big Data is with respect to regular data, information, and knowledge is a crucial point to make to those trying to understand why big data and IoT is so difficult to manage and for showing how it integrates into KM...murray

Just wondering, are you biased against traditional models and have to use something different?


-----Original Message-----
From: Stephen Bounds km@bounds.net.au [sikmleaders] <sikmleaders@yahoogroups.com>
To: sikmleaders <sikmleaders@yahoogroups.com>
Sent: Sat, Oct 27, 2018 1:25 pm
Subject: Re: [sikmleaders] Is anyone actually using AI? [1 Attachment]

[Attachment(s) from Stephen Bounds included below]

Hi Murray, Thanks for your linked article. I'm curious about why you are persisting with the knowledge pyramid. Adding extra layers of data at the bottom really just highlights the weakness of the DIKW model to me. Wouldn't it be much simpler to use a hierarchical agent model? ie
This still reflects the a system's use of multiple agents to achieve data processing, but doesn't artificially force things into a rigid pyramid structure. To return to Matt's question, I think many of the AI uses in KM are very subtle and typically not thought of as "KM". I agree with others that mostly we are using AI as a recommendation engine (whether for search results or taxonomy classification). It's very rare that we provide AI agents with significant autonomy in actual decision-making. The closest I know would be the use of AVR to send user's to the correct support agent -- and it could be argued that this is just another form of recommendation in any case.
Cheers,
Stephen.
====================================
Stephen Bounds
Executive, Information Management
Cordelta
E: stephen.bounds@cordelta.com
M: 0401 829 096
==================================== On 27/10/2018 8:01 AM, Murray Jennex murphjen@aol.com [sikmleaders] wrote:


  basically I see AI and related technologies as knowledge discovery filters on large data sets such as IoT and Big Data.  My revised knowledge pyramid:
Jennex, M.E., (2017).  “Big Data, the Internet of Things and the Revised Knowledge Pyramid,” Data Base for Advances in Information Systems, 48(4), pp. 69-79.
shows AI, machine learning, analytics, and other technologies as filters on real world data to create KM knowledge.  The research I mentioned earlier on Identifying victims of human sex trafficking:
Hultgren, M.; Whitney, J.; Jennex, M.E.; Elkins, A. (2018).  A Knowledge Management Approach for Assisting in Identifying Victims of Human Sex Trafficking, Communications of the Association for Information Systems, volume 42, article 23, pp. 602-620.  Available at http://aisel.aisnet.org/cais/vol42/iss1/23/
Whitney, J., Jennex, M.E., Elkins, A., Frost, E., (2018).  “Don’t Want to Get Caught? Don’t Say It: The Use of EMOJIS in Online Human Sex Trafficking Ads.”  51st Hawaii International Conference on System Sciences, HICSS51, IEEE Computer Society, January 2018.
are applications of this model.  Our current work below on how to do an automated census is also an application of this model.  Basically I look at the above model as a guide to creating KM Strategy and the knowledge content process by helping to identify needed knowledge and the tools/filters needed to obtain that knowledge
Kelly, J., Jennex, M.E., Abhari, K., Durcikova, A., and Frost, E., (2019). Data in the Wild: A KM Approach to doing a Census Without Asking Anyone and the Issue of Privacy.  52nd Hawaii International Conference on System Sciences, HICSS52, IEEE Computer Society, January 2019.
Another area being investigated is the use of AI tools in content/concept mapping.  The tools are a huge advantage in looking at large numbers of document (unstructured data) and extracting terms and concepts and creating linkages.  A rudimentary example of this with respect to bigram plot analysis is:
Jennex, M.E.; Dittes, S.; Smolnik, S.; Croasdell, D.; King, D.: Knowledge, Innovation, and Entrepreneurial Systems at HICSS, Communications of the Association for Information Systems, In Press  (accepted, finalized, waiting to be published)
So while many organizations may not be using AI tools, I expect they will be real soon in their KM programs. (let me know if you want to see any of these papers)....murray jennex


-----Original Message-----
From: Matt Moore innotecture@yahoo.com [sikmleaders] <sikmleaders@yahoogroups.com>
To: Yahoo! Inc. <sikmleaders@yahoogroups.com>
Sent: Thu, Oct 25, 2018 8:15 pm
Subject: [sikmleaders] Is anyone actually using AI?

Hi,

Is anyone using "Artificial Intelligence" as part of their Knowledge Management programs? I will let you define that term however you wish.

This is to prep for a talk that I am giving in a few months. My impression is that very few people are using this on a day-to-day basis but I am very curious to find out if they are.

Regards,

Matt


------------------------------------
Posted by: Matt Moore <innotecture@yahoo.com>
------------------------------------


------------------------------------

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Murray Jennex
 

sorry, had a fat finger
third, I'm not sure you understand much about DIKW, this was originally a taxonomy model and nothing else.  I agree that as a taxonomy it has little value other than to make some implications.  My paper details a complete rework of dikw to make it a process model as well as a relationship model as well as an explanation on the difference between KM and the regular world, as a way of explaining where dikw comes from, as a guide to the transformation processes between dikw, and much more
take a look at it, you might actually find it useful and enjoyable....murray

-----Original Message-----
From: Stephen Bounds km@bounds.net.au [sikmleaders] <sikmleaders@yahoogroups.com>
To: sikmleaders <sikmleaders@yahoogroups.com>
Sent: Mon, Oct 29, 2018 2:04 pm
Subject: Re: [sikmleaders] The case for/against DIKW

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Hi Murray, Obviously a model must be understood to be valuable. But this is a necessary prerequisite for usefulness, not a sufficient one. My point is just that GIGO applies: a bad model means that your predictions about organisational outcomes will be wrong, except by accident.
I see KM as a multidisciplinary science and as such, we shouldn't be driven primarily by who has the money but by a search for the truth. I have nothing against management tools and methods that build upon the scientific evidence base for KM; but unfortunately, too much of the history of KM is littered with models that are pure invention and magical thinking from an evidence point of view.
For example, I have some issues with how Cynefin is often misused as a "scientific" model, but I can't argue with its merits as a management tool. It provides useful and tangible strategies to managers, while retaining a sound basis in complexity theory. On the other hand DIKW is -- at best -- a description of a process methodology for creating management reports using definitions for data, information, knowledge and wisdom that are tightly aligned to the methodology. It is a self-fulfilling prophecy with terms defined in a way that have little external rigour or application.
At worst, DIKW sells a damaging misconception. There is an implication that it is possible to apply a unitary, command and control-inspired approach to knowledge creation and application within organisations that simply isn't borne out by the evidence of how they operate in practice. Yes, I did read through your paper but I still do not believe that your revised knowledge pyramid addresses this fundamental problem.
Cheers,
Stephen.
====================================
Stephen Bounds
Executive, Information Management
Cordelta
E: stephen.bounds@cordelta.com
M: 0401 829 096
==================================== On 28/10/2018 5:57 PM, Murray Jennex murphjen@aol.com [sikmleaders] wrote:


  and as to managerial models, guess who has the money!  the managers so ensuring they understand what they are buying and why is probably more valuable then creating some obtuse model for the km person.  you still didn't really explain why a model that is NOT understood is still valuable while a model that IS understood isn't.  Why would a agent model be better?  I don't see it, I just see some model being used that is different.


-----Original Message-----
From: Stephen Bounds km@bounds.net.au [sikmleaders] <sikmleaders@yahoogroups.com>
To: sikmleaders <sikmleaders@yahoogroups.com>
Sent: Sat, Oct 27, 2018 11:50 pm
Subject: Re: [sikmleaders] Is anyone actually using AI?



Hi Murray, Whether models are understood or not is neither here nor there. Something can be widely understood but wrong (see: 19th century aether and miasma).
While my preference is always to adopt existing models where possible, it is essential that we critically assess them and ensure they have a sound theoretical basis for their adoption. DIKW has multiple flaws, not least that it lacks rigor around the definitions of data, information, knowledge and wisdom. I'll just quote Patrick Lambe verbatim since he said it all really well about a decade ago:

It’s important to understand the origins of a model to understand what it was designed for. The DIKW model emerged out of the struggles of computer science and information science through the late 1970s and early 1980s to legitimise themselves as strategic disciplines for the enterprise. For the data managers, the struggle was to get their organisations to treat data as a strategic resource, so establishing a relationship to information that fed decisions based on knowledge made a lot of sense. For the information managers the “downwards” link to data gave them a structure to work from, and the “upwards” link to knowledge gave them legitimacy in the eyes of senior management.

So while it had utility for data and information managers, the hierarchy was never designed to accommodate the far more complex world uncovered by knowledge management, and as Dave [Snowden] points out, it completely fails to acknowledge the naturalistic ways that data, information and knowledge interact. For example, it does not reflect the fact that data is a very small subset of repeatable information, abstracted and structured for mechanical processing based on knowledge. Data is the product of a knowledge-driven, purposeful piece of design work. The DIKW model implies the opposite, that knowledge is the product of a series of operations upon data. The model also completely fails to account for the sea of knowledge activity in an enterprise which is never informationalised or structured as data. In the natural world, data is the product of a very small component of knowledge activity.

From the data manager’s point of view, the problem in the enterprise is “we have all of this data sitting around, think of what we could do with it if we could figure out how to squeeze insight out of it”. While this is a legitimate question, the knowledge manager has discovered rather painfully, that you need to go back to the contexts that created the data and the knowledge activities the data supports, in order to figure out how the data can be manipulated for greater advantage. You can’t get there by performing a series of logical transformations on the data to create information, and then another series of operations to create knowledge.

So DIKW is a managerial model intended to explain how data can be leveraged as an enterprise resource. It has no practical value for guiding action beyond the D-I interface where it has limited value, explains almost nothing about knowledge, and its references to wisdom have always been completely without substantive or actionable content.


Any attempt to implement a management plan through the lens of big data is just as reductive and flawed as other attempts to understand organisations through a data funnel. Use of complex adaptive systems theory and agent-based models of behaviour (eg AKI cycles) may not lend themselves to a single slide, but I believe they offer a far more promising and robust way to explain systems failure and success.
Cheers,
Stephen.
====================================
Stephen Bounds
Executive, Information Management
Cordelta
E: stephen.bounds@cordelta.com
M: 0401 829 096
==================================== On 28/10/2018 11:30 AM, Murray Jennex murphjen@aol.com [sikmleaders] wrote:


  I persist with the pyramid model as it is a known visualization that all understand.  The real value of my model is when you look at in conjunction with the inverted pyramid reflecting reality, i.e. that we've created vast amounts of knowledge and wisdom off of relatively small amounts of data until the advent of IoT and Big Data and that the purpose of KM is to focus organizations on that knowledge and actionable intelligence needed to achieve business objectives.  I don't see that shown very clearly on your model below.  As to the use of agents, they are built in as the filters I show on the pyramid include technical and human elements.  While I see my model as being very clear on relationships and processes, i don't get that with your model below.  All that said, models help understanding so which ever model works for you is fine.  Note, though that I do think my model showing how large IoT and Big Data is with respect to regular data, information, and knowledge is a crucial point to make to those trying to understand why big data and IoT is so difficult to manage and for showing how it integrates into KM...murray

Just wondering, are you biased against traditional models and have to use something different?


-----Original Message-----
From: Stephen Bounds km@bounds.net.au [sikmleaders] <sikmleaders@yahoogroups.com>
To: sikmleaders <sikmleaders@yahoogroups.com>
Sent: Sat, Oct 27, 2018 1:25 pm
Subject: Re: [sikmleaders] Is anyone actually using AI? [1 Attachment]

[Attachment(s) from Stephen Bounds included below]

Hi Murray, Thanks for your linked article. I'm curious about why you are persisting with the knowledge pyramid. Adding extra layers of data at the bottom really just highlights the weakness of the DIKW model to me. Wouldn't it be much simpler to use a hierarchical agent model? ie
This still reflects the a system's use of multiple agents to achieve data processing, but doesn't artificially force things into a rigid pyramid structure. To return to Matt's question, I think many of the AI uses in KM are very subtle and typically not thought of as "KM". I agree with others that mostly we are using AI as a recommendation engine (whether for search results or taxonomy classification). It's very rare that we provide AI agents with significant autonomy in actual decision-making. The closest I know would be the use of AVR to send user's to the correct support agent -- and it could be argued that this is just another form of recommendation in any case.
Cheers,
Stephen.
====================================
Stephen Bounds
Executive, Information Management
Cordelta
E: stephen.bounds@cordelta.com
M: 0401 829 096
==================================== On 27/10/2018 8:01 AM, Murray Jennex murphjen@aol.com [sikmleaders] wrote:


  basically I see AI and related technologies as knowledge discovery filters on large data sets such as IoT and Big Data.  My revised knowledge pyramid:
Jennex, M.E., (2017).  “Big Data, the Internet of Things and the Revised Knowledge Pyramid,” Data Base for Advances in Information Systems, 48(4), pp. 69-79.
shows AI, machine learning, analytics, and other technologies as filters on real world data to create KM knowledge.  The research I mentioned earlier on Identifying victims of human sex trafficking:
Hultgren, M.; Whitney, J.; Jennex, M.E.; Elkins, A. (2018).  A Knowledge Management Approach for Assisting in Identifying Victims of Human Sex Trafficking, Communications of the Association for Information Systems, volume 42, article 23, pp. 602-620.  Available at http://aisel.aisnet.org/cais/vol42/iss1/23/
Whitney, J., Jennex, M.E., Elkins, A., Frost, E., (2018).  “Don’t Want to Get Caught? Don’t Say It: The Use of EMOJIS in Online Human Sex Trafficking Ads.”  51st Hawaii International Conference on System Sciences, HICSS51, IEEE Computer Society, January 2018.
are applications of this model.  Our current work below on how to do an automated census is also an application of this model.  Basically I look at the above model as a guide to creating KM Strategy and the knowledge content process by helping to identify needed knowledge and the tools/filters needed to obtain that knowledge
Kelly, J., Jennex, M.E., Abhari, K., Durcikova, A., and Frost, E., (2019). Data in the Wild: A KM Approach to doing a Census Without Asking Anyone and the Issue of Privacy.  52nd Hawaii International Conference on System Sciences, HICSS52, IEEE Computer Society, January 2019.
Another area being investigated is the use of AI tools in content/concept mapping.  The tools are a huge advantage in looking at large numbers of document (unstructured data) and extracting terms and concepts and creating linkages.  A rudimentary example of this with respect to bigram plot analysis is:
Jennex, M.E.; Dittes, S.; Smolnik, S.; Croasdell, D.; King, D.: Knowledge, Innovation, and Entrepreneurial Systems at HICSS, Communications of the Association for Information Systems, In Press  (accepted, finalized, waiting to be published)
So while many organizations may not be using AI tools, I expect they will be real soon in their KM programs. (let me know if you want to see any of these papers)....murray jennex


-----Original Message-----
From: Matt Moore innotecture@yahoo.com [sikmleaders] <sikmleaders@yahoogroups.com>
To: Yahoo! Inc. <sikmleaders@yahoogroups.com>
Sent: Thu, Oct 25, 2018 8:15 pm
Subject: [sikmleaders] Is anyone actually using AI?

Hi,

Is anyone using "Artificial Intelligence" as part of their Knowledge Management programs? I will let you define that term however you wish.

This is to prep for a talk that I am giving in a few months. My impression is that very few people are using this on a day-to-day basis but I am very curious to find out if they are.

Regards,

Matt


------------------------------------
Posted by: Matt Moore <innotecture@yahoo.com>
------------------------------------


------------------------------------

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Stephen Bounds
 

Hi Murray,

I always try to play the ball and not the man -- I have nothing against you personally at all. I do admit that I tend to get on my soapbox whenever DIKW is brought up.

No point rehashing the arguments once again. If you don't feel I am engaging in a substantial critique of your position then there's not much more to be said.

Regards,
Stephen.

====================================
Stephen Bounds
Executive, Information Management
Cordelta
E: stephen.bounds@cordelta.com
M: 0401 829 096
====================================

On 30/10/2018 3:50 PM, Murray Jennex murphjen@aol.com [sikmleaders] wrote:

sorry, had a fat finger


third, I'm not sure you understand much about DIKW, this was originally a taxonomy model and nothing else. I agree that as a taxonomy it has little value other than to make some implications.  My paper details a complete rework of dikw to make it a process model as well as a relationship model as well as an explanation on the difference between KM and the regular world, as a way of explaining where dikw comes from, as a guide to the transformation processes between dikw, and much more

take a look at it, you might actually find it useful and enjoyable....murray


-----Original Message-----
From: Stephen Bounds km@bounds.net.au [sikmleaders] <sikmleaders@yahoogroups.com>
To: sikmleaders <sikmleaders@yahoogroups.com>
Sent: Mon, Oct 29, 2018 2:04 pm
Subject: Re: [sikmleaders] The case for/against DIKW



Hi Murray,
Obviously a model must be understood to be valuable. But this is a necessary prerequisite for usefulness, not a sufficient one. My point is just that GIGO applies: a bad model means that your predictions about organisational outcomes will be wrong, except by accident.
I see KM as a multidisciplinary science and as such, we shouldn't be driven primarily by who has the money but by a search for the truth. I have nothing against management tools and methods that build upon the scientific evidence base for KM; but unfortunately, too much of the history of KM is littered with models that are pure invention and magical thinking from an evidence point of view.
For example, I have some issues with how Cynefin is often misused as a "scientific" model, but I can't argue with its merits as a management tool. It provides useful and tangible strategies to managers, while retaining a sound basis in complexity theory.
On the other hand DIKW is -- at best -- a description of a process methodology for creating management reports using definitions for data, information, knowledge and wisdom that are tightly aligned to the methodology. It is a self-fulfilling prophecy with terms defined in a way that have little external rigour or application.
At worst, DIKW sells a damaging misconception. There is an implication that it is possible to apply a unitary, command and control-inspired approach to knowledge creation and application within organisations that simply isn't borne out by the evidence of how they operate in practice. Yes, I did read through your paper but I still do not believe that your revised knowledge pyramid addresses this fundamental problem.
Cheers,
Stephen.
====================================
Stephen Bounds
Executive, Information Management
Cordelta
E:stephen.bounds@cordelta.com <mailto:stephen.bounds@cordelta.com>
M: 0401 829 096
====================================
On 28/10/2018 5:57 PM, Murray Jennex murphjen@aol.com <mailto:murphjen@aol.com> [sikmleaders] wrote:
and as to managerial models, guess who has the money!  the managers so ensuring they understand what they are buying and why is probably more valuable then creating some obtuse model for the km person.  you still didn't really explain why a model that is NOT understood is still valuable while a model that IS understood isn't.  Why would a agent model be better?  I don't see it, I just see some model being used that is different.


-----Original Message-----
From: Stephen Bounds km@bounds.net.au <mailto:km@bounds.net.au> [sikmleaders] <sikmleaders@yahoogroups.com> <mailto:sikmleaders@yahoogroups.com>
To: sikmleaders <sikmleaders@yahoogroups.com> <mailto:sikmleaders@yahoogroups.com>
Sent: Sat, Oct 27, 2018 11:50 pm
Subject: Re: [sikmleaders] Is anyone actually using AI?



Hi Murray,
Whether models are _*understood*_ or not is neither here nor there. Something can be widely understood but wrong (see: 19th century aether and miasma).
While my preference is always to adopt existing models where possible, it is essential that we critically assess them and ensure they have a sound theoretical basis for their adoption.
DIKW has multiple flaws, not least that it lacks rigor around the definitions of data, information, knowledge and wisdom. I'll just quote Patrick Lambe verbatim since he said it all really well <http://www.greenchameleon.com/gc/blog_detail/from_data_with_love/> about a decade ago:

It’s important to understand the origins of a model to
understand what it was designed for. The DIKW model emerged
out of the struggles of computer science and information
science through the late 1970s and early 1980s to legitimise
themselves as strategic disciplines for the enterprise. For
the data managers, the struggle was to get their organisations
to treat data as a strategic resource, so establishing a
relationship to information that fed decisions based on
knowledge made a lot of sense. For the information managers
the “downwards” link to data gave them a structure to work
from, and the “upwards” link to knowledge gave them legitimacy
in the eyes of senior management.

So while it had utility for data and information managers, the
hierarchy was never designed to accommodate the far more
complex world uncovered by knowledge management, and as Dave
[Snowden] points out, it completely fails to acknowledge the
naturalistic ways that data, information and knowledge
interact. For example, it does not reflect the fact that data
is a very small subset of repeatable information, abstracted
and structured for mechanical processing based on knowledge.
Data is the product of a knowledge-driven, purposeful piece of
design work. The DIKW model implies the opposite, that
knowledge is the product of a series of operations upon data.
The model also completely fails to account for the sea of
knowledge activity in an enterprise which is never
informationalised or structured as data. In the natural world,
data is the product of a very small component of knowledge
activity.

From the data manager’s point of view, the problem in the
enterprise is “we have all of this data sitting around, think
of what we could do with it if we could figure out how to
squeeze insight out of it”. While this is a legitimate
question, the knowledge manager has discovered rather
painfully, that you need to go back to the contexts that
created the data and the knowledge activities the data
supports, in order to figure out how the data can be
manipulated for greater advantage. You can’t get there by
performing a series of logical transformations on the data to
create information, and then another series of operations to
create knowledge.

So DIKW is a managerial model intended to explain how data can
be leveraged as an enterprise resource. It has no practical
value for guiding action beyond the D-I interface where it has
limited value, explains almost nothing about knowledge, and
its references to wisdom have always been completely without
substantive or actionable content.

Any attempt to implement a management plan through the lens of big data is just as reductive and flawed as other attempts to understand organisations through a data funnel. Use of complex adaptive systems theory and agent-based models of behaviour (eg AKI cycles <https://realkm.com/2016/01/07/a-model-for-understanding-knowledge-systems/>) may not lend themselves to a single slide, but I believe they offer a far more promising and robust way to explain systems failure and success.
Cheers,
Stephen.
====================================
Stephen Bounds
Executive, Information Management
Cordelta
E:stephen.bounds@cordelta.com <mailto:stephen.bounds@cordelta.com>
M: 0401 829 096
====================================
On 28/10/2018 11:30 AM, Murray Jennex murphjen@aol.com <mailto:murphjen@aol.com> [sikmleaders] wrote:
I persist with the pyramid model as it is a known visualization that all understand.  The real value of my model is when you look at in conjunction with the inverted pyramid reflecting reality, i.e. that we've created vast amounts of knowledge and wisdom off of relatively small amounts of data until the advent of IoT and Big Data and that the purpose of KM is to focus organizations on that knowledge and actionable intelligence needed to achieve business objectives.  I don't see that shown very clearly on your model below.  As to the use of agents, they are built in as the filters I show on the pyramid include technical and human elements.  While I see my model as being very clear on relationships and processes, i don't get that with your model below.  All that said, models help understanding so which ever model works for you is fine.  Note, though that I do think my model showing how large IoT and Big Data is with respect to regular data, information, and knowledge is a crucial point to make to those trying to understand why big data and IoT is so difficult to manage and for showing how it integrates into KM...murray

Just wondering, are you biased against traditional models and have to use something different?


-----Original Message-----
From: Stephen Bounds km@bounds.net.au <mailto:km@bounds.net..au> [sikmleaders] <sikmleaders@yahoogroups.com> <mailto:sikmleaders@yahoogroups.com>
To: sikmleaders <sikmleaders@yahoogroups.com> <mailto:sikmleaders@yahoogroups.com>
Sent: Sat, Oct 27, 2018 1:25 pm
Subject: Re: [sikmleaders] Is anyone actually using AI? [1 Attachment]

[Attachment(s) <#TopText> from Stephen Bounds included below]

Hi Murray,
Thanks for your linked article. I'm curious about why you are persisting with the knowledge pyramid.
Adding extra layers of data at the bottom really just highlights the weakness of the DIKW model to me. Wouldn't it be much simpler to use a hierarchical agent model? ie
This still reflects the a system's use of multiple agents to achieve data processing, but doesn't artificially force things into a rigid pyramid structure.
To return to Matt's question, I think many of the AI uses in KM are very subtle and typically not thought of as "KM". I agree with others that mostly we are using AI as a recommendation engine (whether for search results or taxonomy classification). It's very rare that we provide AI agents with significant autonomy in actual decision-making. The closest I know would be the use of AVR to send user's to the correct support agent -- and it could be argued that this is just another form of recommendation in any case.
Cheers,
Stephen.
====================================
Stephen Bounds
Executive, Information Management
Cordelta
E:stephen.bounds@cordelta.com <mailto:stephen.bounds@cordelta.com>
M: 0401 829 096
====================================
On 27/10/2018 8:01 AM, Murray Jennex murphjen@aol.com <mailto:murphjen@aol.com> [sikmleaders] wrote:
basically I see AI and related technologies as knowledge discovery filters on large data sets such as IoT and Big Data.  My revised knowledge pyramid:

Jennex, M.E., (2017).  “Big Data, the Internet of Things and the Revised Knowledge Pyramid,” Data Base for Advances in Information Systems, 48(4), pp. 69-79.

shows AI, machine learning, analytics, and other technologies as filters on real world data to create KM knowledge.  The research I mentioned earlier on Identifying victims of human sex trafficking:

Hultgren, M.; Whitney, J.; Jennex, M.E.; Elkins, A. (2018).  A Knowledge Management Approach for Assisting in Identifying Victims of Human Sex Trafficking, Communications of the Association for Information Systems, volume 42, article 23, pp. 602-620. Available at http://aisel.aisnet.org/cais/vol42/iss1/23/

Whitney, J., Jennex, M.E., Elkins, A., Frost, E., (2018).  “Don’t Want to Get Caught? Don’t Say It: The Use of EMOJIS in Online Human Sex Trafficking Ads.”  51^st Hawaii International Conference on System Sciences, HICSS51, IEEE Computer Society, January 2018.

are applications of this model.  Our current work below on how to do an automated census is also an application of this model. Basically I look at the above model as a guide to creating KM Strategy and the knowledge content process by helping to identify needed knowledge and the tools/filters needed to obtain that knowledge

Kelly, J., Jennex, M.E., Abhari, K., Durcikova, A., and Frost, E., (2019). Data in the Wild: A KM Approach to doing a Census Without Asking Anyone and the Issue of Privacy.  52^nd Hawaii International Conference on System Sciences, HICSS52, IEEE Computer Society, January 2019.

Another area being investigated is the use of AI tools in content/concept mapping. The tools are a huge advantage in looking at large numbers of document (unstructured data) and extracting terms and concepts and creating linkages.  A rudimentary example of this with respect to bigram plot analysis is:

Jennex, M.E.; Dittes, S.; Smolnik, S.; Croasdell, D.; King, D.: Knowledge, Innovation, and Entrepreneurial Systems at HICSS, Communications of the Association for Information Systems, In Press  (accepted, finalized, waiting to be published)

So while many organizations may not be using AI tools, I expect they will be real soon in their KM programs. (let me know if you want to see any of these papers)....murray jennex


-----Original Message-----
From: Matt Moore innotecture@yahoo.com <mailto:innotecture@yahoo.com> [sikmleaders] <sikmleaders@yahoogroups.com> <mailto:sikmleaders@yahoogroups.com>
To: Yahoo! Inc. <sikmleaders@yahoogroups.com> <mailto:sikmleaders@yahoogroups.com>
Sent: Thu, Oct 25, 2018 8:15 pm
Subject: [sikmleaders] Is anyone actually using AI?

Hi,

Is anyone using "Artificial Intelligence" as part of their Knowledge Management programs? I will let you define that term however you wish.

This is to prep for a talk that I am giving in a few months. My impression is that very few people are using this on a day-to-day basis but I am very curious to find out if they are.

Regards,

Matt


------------------------------------
Posted by: Matt Moore <innotecture@yahoo.com <mailto:innotecture@yahoo.com>>
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Kim Tillano
 

My company is in the pilot phase of rolling out an AI chatbot by MoveWorks.  It is set to search our existing knowledgebases and answer questions as well as automatically submit tickets/requests and other automated actions.  I have not yet seen it in action, but it is my understanding that it works very well.