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Knowledge Maturity #maturity

Dennis Thomas
 

Hello Madeleine Du Toit, 

We are a technology company focused on the KM industry.   The more advanced queries we get are from companies that want to grow.  Their concerns are transferring organizational knowledge.  They have ERP systems (Enterprise Resource Planning) infrastructures, but the end-to-end business processes (the business intelligence) of those systems is hidden in data structures which in general, is unavailable to their workforce for knowledge transfer purposes.

If I were to define an "end-state,” I would say it is the human accessible, understandable, and usable  how-to, why, and what-if knowledge that is represented within their existing end-to-end business procedures,  tasks and processes, along with any dependent, contingent, or adhoc relationships required to represent the overall breadth and depth of those end-to-end knowledge structures. 

And organization may have several end-to-end business processes, such as:  Hire to Retire; Acquire to Retire; Plan to Inventory; Quote to Cash; Market to Order;  Idea to Offering; Prospect to Customer; Customer to Retention; etc.  These end-to-end procedures are core to every business.  It’s where real-world business intelligence resides.  The stuff that people have in their heads. 

We use this very language to help clarify the KM issue and to direct the conversation.  In most cases, referring to these terms generates a “blinding flash of the obvious” response from potential customers sitting on the opposite side of the table.   It's what practical business people understand because it’s  what business owners and operators have spent their careers learning and perfecting. 

Once the end-to-end discussion point has been made, accepted, and acknowledged, the next step is to correlate the end-to-end modeling process to their goals and objectives.  The beauty of this approach, for us, is that each end-to-end KM process can be defined in advance, achieved in milestone fashion, and successfully concluded with an end-state result.  Then, on to priority #2, #3, #4, etc.

Of course, it doesn’t hurt to have a cognitive technology that has the capacity to model the complexity of end-to-end business procedures and processes.  I know there are a few out there.

Also, keep in mind that Conversational AI is here now.  It is also called NLU (Natural Language Understanding).   So if the discussion centers on Tier One, Tier Two Customer Support, Digital Assistants, Smart Agents, Smart Assistants, or Smart Chatbots, etc. know that non-hype versions are coming out on the market, but they are few and far between.  

Good luck!

Dennis L Thomas
(810) 662-5199
dlthomas@...
IQStrategix.com

Leveraging Organizational Knowledge 


On Nov 2, 2022, 1:36 PM -0400, Madeleine Du Toit via groups.io <mdutoit@...>, wrote:

Hi, 
I'm currently assisting an organisation with managing their project knowledge. They are looking to me to define an end-state. Something they can work towards. They keep on throwing Knowledge Maturity into the mix..... what would mature knowledge look like? 
I know of APQC's Knowledge Maturity framework but that looks more at KM as a capability. Any ideas on where to look or what to use to define "mature knowledge"? I'm kind of leaning towards - it depends on what you want, but maybe you have some ideas. 

Appreciate the input

Madeleine Du Toit
 

Thank you all so much for your thoughtful input. It has certainly given me many angles to consider. 

From: main@SIKM.groups.io <main@SIKM.groups.io> on behalf of Dennis Thomas <dlthomas@...>
Sent: Friday, November 4, 2022 6:37:06 PM
To: main@SIKM.groups.io <main@SIKM.groups.io>
Subject: Re: [SIKM] Knowledge Maturity #maturity
 
Hello Madeleine Du Toit, 

We are a technology company focused on the KM industry.   The more advanced queries we get are from companies that want to grow.  Their concerns are transferring organizational knowledge.  They have ERP systems (Enterprise Resource Planning) infrastructures, but the end-to-end business processes (the business intelligence) of those systems is hidden in data structures which in general, is unavailable to their workforce for knowledge transfer purposes.

If I were to define an "end-state,” I would say it is the human accessible, understandable, and usable  how-to, why, and what-if knowledge that is represented within their existing end-to-end business procedures,  tasks and processes, along with any dependent, contingent, or adhoc relationships required to represent the overall breadth and depth of those end-to-end knowledge structures. 

And organization may have several end-to-end business processes, such as:  Hire to Retire; Acquire to Retire; Plan to Inventory; Quote to Cash; Market to Order;  Idea to Offering; Prospect to Customer; Customer to Retention; etc.  These end-to-end procedures are core to every business.  It’s where real-world business intelligence resides.  The stuff that people have in their heads. 

We use this very language to help clarify the KM issue and to direct the conversation.  In most cases, referring to these terms generates a “blinding flash of the obvious” response from potential customers sitting on the opposite side of the table.   It's what practical business people understand because it’s  what business owners and operators have spent their careers learning and perfecting. 

Once the end-to-end discussion point has been made, accepted, and acknowledged, the next step is to correlate the end-to-end modeling process to their goals and objectives.  The beauty of this approach, for us, is that each end-to-end KM process can be defined in advance, achieved in milestone fashion, and successfully concluded with an end-state result.  Then, on to priority #2, #3, #4, etc.

Of course, it doesn’t hurt to have a cognitive technology that has the capacity to model the complexity of end-to-end business procedures and processes.  I know there are a few out there.

Also, keep in mind that Conversational AI is here now.  It is also called NLU (Natural Language Understanding).   So if the discussion centers on Tier One, Tier Two Customer Support, Digital Assistants, Smart Agents, Smart Assistants, or Smart Chatbots, etc. know that non-hype versions are coming out on the market, but they are few and far between.  

Good luck!

Dennis L Thomas
(810) 662-5199
dlthomas@...
IQStrategix.com

Leveraging Organizational Knowledge 


On Nov 2, 2022, 1:36 PM -0400, Madeleine Du Toit via groups.io <mdutoit@...>, wrote:
Hi, 
I'm currently assisting an organisation with managing their project knowledge. They are looking to me to define an end-state. Something they can work towards. They keep on throwing Knowledge Maturity into the mix..... what would mature knowledge look like? 
I know of APQC's Knowledge Maturity framework but that looks more at KM as a capability. Any ideas on where to look or what to use to define "mature knowledge"? I'm kind of leaning towards - it depends on what you want, but maybe you have some ideas. 

Appreciate the input

Guillermo A. Galdamez
 

Hi Madeleine,

I am happy to see the amount of conversation and engagement your question generated. Thanks for asking!

I'll try to contribute without being redundant to what others have previously written.

In my experience, when organizations use terms like "mature" to describe their end state for knowledge they are generally referring to several things:

  1. Knowledge is consistently captured and stored. As opposed to knowledge only existing within people's heads, key information and helpful facts are recorded somewhere in a deliberate, systemic way. It doesn't have to be a single, central location -- it can be several places, as long as each of these places are fit for purpose.
  2. Knowledge is categorized and organized in a predictable way. Once knowledge is captured, it should be organized in such a way that it is easy for people to make sense of what they are looking at. This also ties in to the next point.
  3. Knowledge is easy to find. People want to reference knowledge in order to do something else. They generally want to get the knowledge quickly so that they can move on with their task. 
  4. Knowledge is reliable. Knowledge is curated and updated consistently to ensure that it is trustworthy. Folks don't want to spend time validating what they've found.
  5. Knowledge is complete and interconnected. Knowledge gives a full picture of the situation, people don't want to spend time piecing together bits and pieces from multiple sources to understand what is going on.
  6. Knowledge is secure and protected. This may be more relevant in some settings than others, but generally you want to make sure that only the right people can access sensitive information.
Sadly, I don't have a single resource for a definition of mature knowledge that I can point you to, however, the following blog post may come close: "NERDy Content for the Enterprise". [full disclosure: my boss wrote that]

Beyond helping your organization define an end goal for their knowledge, I would encourage you to help them define the outcomes that they want to get out of it. From the perspective of project management this could be a variety of things:
  • Improving future project performance
  • Reducing costs / increasing profitability
  • Preserving/enhancing stakeholder relationships
  • Refining and promoting emerging approaches/practices

Keeping the above in mind, you can begin to prioritize your knowledge management efforts.  (I also love guiding folks to this blog post to give them an idea of what their first step may be: '6 Questions to Help Determine Where to Start Your KM Transformation')

I hope you find this helpful!  Feel free to reach out if you have any questions.

Best,

Guillermo


On Wed, Nov 2, 2022 at 9:36 PM Madeleine Du Toit via groups.io <mdutoit=iqbusiness.net@groups.io> wrote:
Hi, 
I'm currently assisting an organisation with managing their project knowledge. They are looking to me to define an end-state. Something they can work towards. They keep on throwing Knowledge Maturity into the mix..... what would mature knowledge look like? 
I know of APQC's Knowledge Maturity framework but that looks more at KM as a capability. Any ideas on where to look or what to use to define "mature knowledge"? I'm kind of leaning towards - it depends on what you want, but maybe you have some ideas. 

Appreciate the input

Barbara Fillip
 

In fact, since this appears to be born out of a concern for project knowledge, it may need to be interpreted specifically in that context.  This article from PMI looks at the connections between project management maturity models and knowledge management maturity models, eventually developing a model that combines them:  Management of project knowledge at various maturity levels in PMO, a theoretical framework, https://www.pmi.org/learning/library/management-project-knowledge-maturity-levels-8928

Best,
Barbara Fillip

On Wed, Nov 2, 2022 at 8:10 PM Tom Olney <tolney@...> wrote:

Here’s a link to the PMI website re: knowledge management. Perhaps this will give you some good information:

Project management knowledge management (pmi.org)

 

Tom Olney – PMP, CKM

PSCU

 

From: main@SIKM.groups.io <main@SIKM.groups.io> on behalf of Madeleine Du Toit via groups.io <mdutoit=iqbusiness.net@groups.io>
Date: Wednesday, November 2, 2022 at 1:36 PM
To: main@SIKM.groups.io <main@SIKM.groups.io>
Subject: [EXTERNAL] [SIKM] Knowledge Maturity #maturity

Hi, 
I'm currently assisting an organisation with managing their project knowledge. They are looking to me to define an end-state. Something they can work towards. They keep on throwing Knowledge Maturity into the mix..... what would mature knowledge look like? 
I know of APQC's Knowledge Maturity framework but that looks more at KM as a capability. Any ideas on where to look or what to use to define "mature knowledge"? I'm kind of leaning towards - it depends on what you want, but maybe you have some ideas. 

Appreciate the input

CAUTION: This email originated outside of PSCU. Do not click links or open attachments unless you recognize the sender and know the content is safe. If in doubt, use the Phish Alert button at the top of your Outlook toolbar to report suspicious emails.

 
Stan Garfield
 
Edited

Here is the updated diagram with greater resolution.
Tom Short
 

I’d ‘like’ Tim’s reply 10 or 100 times more if I could. 

KM shouldn’t be about getting better at KM. It should be about improving business outcomes, which Drucker described as either increasing productivity or increasing innovation. 

That all said, the idea of a maturity model is not a new one - it’s been explored at length by many, including several people in this august group. 

I took the liberty of googling it for your inquirer. 

 

https://www.google.com/search?q=knowledge+management+maturity+model&ie=UTF-8&oe=UTF-8&hl=en-us&client=safari

(Ok, I admit to some snark-irony there).
--
-Tom
--

Tom Short Consulting
TSC
+1 415 300 7457

All of my previous SIKM Posts

Madeleine Du Toit
 

Hi all, 

Thanks again for all your valuable inputs to help me solve this. I ended up adapting the APQC model and using your end state of value as key in assessing the knowledge.
What this means practically is that we identified key areas that the knowledge would be used for. In this particular case, we decided on 3 Objectives:
* Project Onboarding
* Problem Resolution (when problems arise in live and troubleshooting is required)
* Further Enhancements 

We will then assess the knowledge available to achieve each noted objective using the maturity model. (APQC I hope my slight adaptation is allowed).
(I was thinking of also categorising the knowledge as tacit and explicit so that targeted KM activities can be further enhanced to improve the maturity of the knowledge.)


I think it will work for us but welcome your thoughts and further input. 
Dave Snowden
 
Edited

Well to be a contrary voice.  I think the APQC scales might be better termed a scale of ossification.  It’s also worth remembering that Nonaka failed to recognise Polanyi’s key point that no explicit knowledge can exist without a tacit component.    
 
To give a simple illustration.  Informal networks are far better than formal systems in knowledge discovery especially under conditions of uncertainty.  So a key measure of KM program success is the density and cross silo reach of informal networks.   Stage one is more resilient than stage four. 

Prof Dave Snowden
Cynefin Centre & Cognitive Edge
11 Pro
Please excuse predictive text errors and typos

On 14 Nov 2022, at 01:43, Madeleine Du Toit via groups.io <mdutoit@...> wrote:

Hi all, 

Thanks again for all your valuable inputs to help me solve this. I ended up adapting the APQC model and using your end state of value as key in assessing the knowledge.
What this means practically is that we identified key areas that the knowledge would be used for. In this particular case, we decided on 3 Objectives:
* Project Onboarding
* Problem Resolution (when problems arise in live and troubleshooting is required)
* Further Enhancements 

We will then assess the knowledge available to achieve each noted objective using the maturity model. (APQC I hope my slight adaptation is allowed).
(I was thinking of also categorising the knowledge as tacit and explicit so that targeted KM activities can be further enhanced to improve the maturity of the knowledge.)
Knowledge Maturity.png

I think it will work for us but welcome your thoughts and further input. 
Patrick Lambe
 

<<Stage one is more resilient than stage four.>>

That’s a very interesting observation Dave. 

Would this be better represented as an additive framework rather than a progression framework as in a traditional maturity model? i.e. All layers need to be in place for the knowledge ecosystem to function effectively? The balance of investment across the layers to be dependent on the contextual needs.

P

Patrick Lambe
Partner
Straits Knowledge

phone:  +65 98528511

web:  www.straitsknowledge.com
resources:  www.greenchameleon.com
knowledge mapping:  www.aithinsoftware.com


On 14 Nov 2022, at 2:57 AM, Dave Snowden via groups.io <snowded@...> wrote:

Well to be a contrary voice.  I think the APQC scales might be better termed a scale of ossification.  It’s also worth remembering that Nonaka failed to recognise Polanyi’s key point that no explicit knowledge can exist without a tacit component.    

To give a simple illustration.  Informal networks are far better than formal systems in knowledge discovery especially under conditions of uncertainty.  So a key measure of KM program success is the density and cross solo reach of informal networks.   Stage one is more resilient than stage four. 

Prof Dave Snowden
Cynefin Centre & Cognitive Edge
11 Pro
Please excuse predictive text errors and typos

On 14 Nov 2022, at 01:43, Madeleine Du Toit via groups.io <mdutoit@...> wrote:

Hi all, 

Thanks again for all your valuable inputs to help me solve this. I ended up adapting the APQC model and using your end state of value as key in assessing the knowledge.
What this means practically is that we identified key areas that the knowledge would be used for. In this particular case, we decided on 3 Objectives:
* Project Onboarding
* Problem Resolution (when problems arise in live and troubleshooting is required)
* Further Enhancements 

We will then assess the knowledge available to achieve each noted objective using the maturity model. (APQC I hope my slight adaptation is allowed).
(I was thinking of also categorising the knowledge as tacit and explicit so that targeted KM activities can be further enhanced to improve the maturity of the knowledge.)
<Knowledge Maturity.png>

I think it will work for us but welcome your thoughts and further input. 

Dave Snowden
 

I’m going to write a blog post on this and Nancy’s four stages this week.  Aspects not additive would be my initial response.  But overall I think maturity models are flawed as a concept 

Prof Dave Snowden
Cynefin Centre & Cognitive Edge
11 Pro
Please excuse predictive text errors and typos

On 14 Nov 2022, at 02:50, Patrick Lambe <plambe@...> wrote:

<<Stage one is more resilient than stage four.>>

That’s a very interesting observation Dave. 

Would this be better represented as an additive framework rather than a progression framework as in a traditional maturity model? i.e. All layers need to be in place for the knowledge ecosystem to function effectively? The balance of investment across the layers to be dependent on the contextual needs.

P

Patrick Lambe
Partner
Straits Knowledge

phone:  +65 98528511

web:  www.straitsknowledge.com
resources:  www.greenchameleon.com
knowledge mapping:  www.aithinsoftware.com

SK18th_Anniv2020_emailfooter (2).jpg

On 14 Nov 2022, at 2:57 AM, Dave Snowden via groups.io <snowded@...> wrote:

Well to be a contrary voice.  I think the APQC scales might be better termed a scale of ossification.  It’s also worth remembering that Nonaka failed to recognise Polanyi’s key point that no explicit knowledge can exist without a tacit component.    

To give a simple illustration.  Informal networks are far better than formal systems in knowledge discovery especially under conditions of uncertainty.  So a key measure of KM program success is the density and cross solo reach of informal networks.   Stage one is more resilient than stage four. 

Prof Dave Snowden
Cynefin Centre & Cognitive Edge
11 Pro
Please excuse predictive text errors and typos

On 14 Nov 2022, at 01:43, Madeleine Du Toit via groups.io <mdutoit@...> wrote:

Hi all, 

Thanks again for all your valuable inputs to help me solve this. I ended up adapting the APQC model and using your end state of value as key in assessing the knowledge.
What this means practically is that we identified key areas that the knowledge would be used for. In this particular case, we decided on 3 Objectives:
* Project Onboarding
* Problem Resolution (when problems arise in live and troubleshooting is required)
* Further Enhancements 

We will then assess the knowledge available to achieve each noted objective using the maturity model. (APQC I hope my slight adaptation is allowed).
(I was thinking of also categorising the knowledge as tacit and explicit so that targeted KM activities can be further enhanced to improve the maturity of the knowledge.)
<Knowledge Maturity.png>

I think it will work for us but welcome your thoughts and further input. 

Patrick Lambe
 

OK look forward it - keep us posted.

I’m also sceptical about maturity models for a number of reasons, some of them posted here:

We were persuaded once to develop a maturity model by a client, but we developed it more as a means of collective self evaluation of capabilities. And in general I’m much more comfortable thinking about capabilities than maturity.

P

Patrick Lambe
Partner
Straits Knowledge

phone:  +65 98528511

web:  www.straitsknowledge.com
resources:  www.greenchameleon.com
knowledge mapping:  www.aithinsoftware.com


On 14 Nov 2022, at 4:00 AM, Dave Snowden via groups.io <snowded@...> wrote:

I’m going to write a blog post on this and Nancy’s four stages this week.  Aspects not additive would be my initial response.  But overall I think maturity models are flawed as a concept 

Prof Dave Snowden
Cynefin Centre & Cognitive Edge
11 Pro
Please excuse predictive text errors and typos

On 14 Nov 2022, at 02:50, Patrick Lambe <plambe@...> wrote:

<<Stage one is more resilient than stage four.>>

That’s a very interesting observation Dave. 

Would this be better represented as an additive framework rather than a progression framework as in a traditional maturity model? i.e. All layers need to be in place for the knowledge ecosystem to function effectively? The balance of investment across the layers to be dependent on the contextual needs.

P

Patrick Lambe
Partner
Straits Knowledge

phone:  +65 98528511

web:  www.straitsknowledge.com
resources:  www.greenchameleon.com
knowledge mapping:  www.aithinsoftware.com

<SK18th_Anniv2020_emailfooter (2).jpg>

On 14 Nov 2022, at 2:57 AM, Dave Snowden via groups.io <snowded@...> wrote:

Well to be a contrary voice.  I think the APQC scales might be better termed a scale of ossification.  It’s also worth remembering that Nonaka failed to recognise Polanyi’s key point that no explicit knowledge can exist without a tacit component.    

To give a simple illustration.  Informal networks are far better than formal systems in knowledge discovery especially under conditions of uncertainty.  So a key measure of KM program success is the density and cross solo reach of informal networks.   Stage one is more resilient than stage four. 

Prof Dave Snowden
Cynefin Centre & Cognitive Edge
11 Pro
Please excuse predictive text errors and typos

On 14 Nov 2022, at 01:43, Madeleine Du Toit via groups.io <mdutoit@...> wrote:

Hi all, 

Thanks again for all your valuable inputs to help me solve this. I ended up adapting the APQC model and using your end state of value as key in assessing the knowledge.
What this means practically is that we identified key areas that the knowledge would be used for. In this particular case, we decided on 3 Objectives:
* Project Onboarding
* Problem Resolution (when problems arise in live and troubleshooting is required)
* Further Enhancements 

We will then assess the knowledge available to achieve each noted objective using the maturity model. (APQC I hope my slight adaptation is allowed).
(I was thinking of also categorising the knowledge as tacit and explicit so that targeted KM activities can be further enhanced to improve the maturity of the knowledge.)
<Knowledge Maturity.png>

I think it will work for us but welcome your thoughts and further input. 


Matt Finch
 

Following on from Dave's comments, there's definitely something to scratch at about this four-stage model...

In level 1, knowledge is random and the objective is unattainable - so I'm not sure it can entirely be described as resilient. (What exactly resiles under such conditions?).
And level 5 is characterised by embedded processes and active use of knowledge to innovate and improve - which doesn't preclude flexibility in the face of uncertainty...

Maybe it's about flexibility more than formality? The problem with Stage 4 is the standardisation and the predictability (which implies that the context remains sufficiently stable that rigid standards maintain value, and that tomorrow's conditions can be reliably anticipated based on past experience...).


M.

On 14 Nov 2022, at 07:57, Dave Snowden via groups.io <snowded@...> wrote:

Well to be a contrary voice.  I think the APQC scales might be better termed a scale of ossification.  It’s also worth remembering that Nonaka failed to recognise Polanyi’s key point that no explicit knowledge can exist without a tacit component.    

To give a simple illustration.  Informal networks are far better than formal systems in knowledge discovery especially under conditions of uncertainty.  So a key measure of KM program success is the density and cross solo reach of informal networks.   Stage one is more resilient than stage four. 

Prof Dave Snowden
Cynefin Centre & Cognitive Edge
11 Pro
Please excuse predictive text errors and typos

On 14 Nov 2022, at 01:43, Madeleine Du Toit via groups.io <mdutoit@...> wrote:

Hi all, 

Thanks again for all your valuable inputs to help me solve this. I ended up adapting the APQC model and using your end state of value as key in assessing the knowledge.
What this means practically is that we identified key areas that the knowledge would be used for. In this particular case, we decided on 3 Objectives:
* Project Onboarding
* Problem Resolution (when problems arise in live and troubleshooting is required)
* Further Enhancements 

We will then assess the knowledge available to achieve each noted objective using the maturity model. (APQC I hope my slight adaptation is allowed).
(I was thinking of also categorising the knowledge as tacit and explicit so that targeted KM activities can be further enhanced to improve the maturity of the knowledge.)
<Knowledge Maturity.png>

I think it will work for us but welcome your thoughts and further input. 

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