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Storytelling & Knowledge Capture

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  • Jake Madden
POSTED IN: Cognitive Wingman

Storytelling & Knowledge Capture


PART 1 -

How can STORIES be used to capture, analyze, interpret and ultimately use organizational knowledge?


Digging a little deeper here – this is a follow up blog to last year (April 2017) on Digital Twins & Digital Threads: A Holistic Approach to Asset Management that covered

· Human Insights & Tribal Knowledge

· asset-centric knowledge capture

· ROI and Risk Management – with Digital Twin & Digital Threads

· https://dreamtolearn.com/ryan/data_analytics_viz/111


Why do we care?  Here's one example from the United States Nuclear Regulatory Commission (NRC)

“The United States Nuclear Regulatory Commission (NRC) has practiced the capturing, preservation, sharing, and use of organizational knowledge long before the term Knowledge Management (KM) came into common use. However, it was not until 2006 with the establishment of a formal KM program with a clear system of governance that the NRC initiated a more structured and systematic approach to KM. The primary impetus at the time was the changing demographics of NRC’s workforce and the recognition that a significant percentage of NRC’s highly skilled and knowledge able workforce was poised for retirement; plus the NRC was in a rapid growth mode in which large numbers of new employees were being hired and needed to become qualified as license reviewers and inspectors. It was a high priority to capture and preserve the knowledge of our aging workforce and transfer it to others, especially to the newly hired employees.”



Let’s start by unpacking Storytelling into four chunks








Here we cast the net wide.  Gathering information from documents, transcripts, or anywhere where there is a story to be told.  Engaging the humans in the organization.  Interviewing.  Listening.  Could also be transcribing audio from conference calls or incidents  - or a fireside-chat style recording while drinking a beer with a veteran.  


The stories will vary.  Some will contain organizational knowledge.  Others touch on culture, traditions or hierarchy.  Others may be some lessons learned the hard way or home truths. 


This can be thought of as a human-API (hAPI?) – to tap, solicit, mine or infer information



Once the raw materials are at hand – the central themes and ideas can be examined.  Some stories with structure will surface – with characters, plot, and outcomes.  Others may be fragments. 

The Characters are key – human elements – that may contain archetypes, identities, emotions and personalities.  An analysis step may seek to surface relevant factors like “power distance” between characters.    I.e. For a story with a CEO interacting with an intern, the power distance might be an integral part.

Analysis can also include standard signal extraction – NLU/NLP/NLC and concept extraction.  Story types, “shape” and topology.   Story staking and mapping.  Ultimately the analysis helps move information the way up the DIKW flow – Data > Information > Knowledge and Wisdom . 




Reading the tea leaves is next.  This may include human touch – at least in early days.  

Tagging and Marking.  Pattern analysis (e.g. clustering and weights)

For the more ambitious – the platonic form of story, or key concepts can be distilled.  The essence of the story.  This may include semiotic or symbolic represenations, and/or metaphor.

The interpretation of the information should be tied back to KPIs for the organization and mission flags – i.e. start to connect the dots to ‘why does this matter?’



Lastly – what is to be done with the stories, knowledge and insights?

The Human Computer Interrace (natural language, often verbal ) is key – to understand how the knowledge may be used.  And useful.

The solution may generate useful guidance – uses may include, training, facilitation, deictions support, or solicitation of guidance.

Alerts and Assistance – system alerts or reminders may be part of usefulness too





Real World Examples:

  1. Nuclear - multi-generational knowledge transfer (risk)
  2. Energy & Transportation - any other mulit-decade knowledge transfer (rolling stock, signal switching, oil & gas platforms, infrastructure
  3. Therapy - PTSD / Dementia - Story clustering to find best fit.
  4. Education - finding most resonant flavors of storys - for each learner TYPE
  5. etc..



Use Case (Railways)

I had the opportunity to interview a respected railway executive and retired project director. He has nearly 50 years of experience with the design, delivery and operation of a large and complex railway systems.

 He told me a very interesting story of how his organization got exceptional value out of its rolling stock. KPIs almost unbelievable to people outside the company. Although the typical asset life span of rolling stock was 30 years; his organization has safely extended it to 40, and in some cases nearly 50 years. Keep in mind some of these rail cars were built just after I was born, and are still safely in operation today.

His railway was able to do this because of a unique combination of company culture; tribal knowledge; great (old school) record keeping; and top-shelf engineering and maintenance teams. In this case, quality data and knowledge of the rolling stock (and refurbishment investment), resulted in asset life extension of more than 60%...



Structure of Myth (Levi-Strauss)--Symbols & Society https://www.youtube.com/watch?v=42QwA0NOTX4



PART 2 - Reflections

My friend Chris Noessel https://twitter.com/chrisnoessel?lang=en and I spoke to a customer on this topic last week - and expanded on this idea.  I really enjoyed working with Chris on Storytelling about Storytelling - fresh perspectives.

Storytelling, considering...
- Community
- Purpose & Value
- Typology
- Methods


We talked about WHISKEY JACK - a fictional character who has been working in the energy sector for decades.  About to retire.  Knows the history of assets, knows what works from the 'official' engineering manuals - but also has developed a WEALTH OF KNOWELDGE (hard won) from years of adapting to extreme conditions.  We asked "when Jack Retires, how much valuable knowledge LEAKS out the door?  HOw might we slow the knowledge leaking?" - (hint: it involves buying Jack drinks and talking to him.. getting the kinds of stories you might not get in a formal exit interview, or engineering notes)


We talked about PURPOSE of stories..


He went deep on TYPOLOGY in stories ("study of or analysis or classification based on types or categories") - picking up on this concept https://www.theatlantic.com/technology/archive/2016/07/the-six-main-arcs-in-storytelling-identified-by-a-computer/490733/  but understanding that there are DOZENS of Typologies - depending on perspectives and objectives of stakeholders...

   and the loop from Individuals with a common purpose, and how that evolves to collective intelligence (some implicit) that nests, back to individuals...


AI / Technology is not a Silver Bullet

We also cautioned that TECHNOLOGY, in many use cases may only be a small part of a project - especially in early stages of a project.  For example - if stakholders are resistant to speaking, or there are few stories of practical use - no amount of technology can surface signal where it does not exist.  

In short - AI might be a powerful catalyst, but it's not a silver bullet - and it's just one part.


I love this "old school" image if you dont konw StoryCorps - check out background here https://en.wikipedia.org/wiki/StoryCorps - the organization has a "mission is to record, preserve, and share the stories of Americans from all backgrounds and beliefs" - and this photo could be from 2 years ago, or 20 years ago...


Unpacking management tasks in Storytelling Projects..

1. Capturing
2. Prompting
3. Analyzing
4. Disseminating
5. Organizational  change: habituation


Anyway - we're planning on co-creating a proper blog - but just wanted to get the key points down so I dont forget - and as a memory trigger..


to be continued...

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About the Author

Ryan Anderson

Ryan Anderson

Hi! I like to play with data, analytics and hack around with robots and gadgets in my garage. Lately I've been learning about machine learning.

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Cognitive Wingman - Never Walk Alone

Created: December 20, 2016


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