Narrative enhanced practice 2 of 2

As promised I did try and get all of this into a process map last night while getting through the last three episodes of The English (worth watching and the linked review is a good summary).  One of the reasons for that is that the type of system I am describing pretty much exemplifies what I call messy coherence and I picked the banner illustration to make that point,  Converging footsteps in the snow contrast with yesterday’s single footprint in the sand.   If you design a knowledge management (KM) system correctly then its a lot of objects and a lot of interactions so that any application can emerge.  Too many KM approaches, whatever the stated intent, end up creating a system based on how the designers think people should work.  Better to create opportunities for contextual interaction within a fairly basic design and then put in more effort if stable patterns emerge.  In that respect, quite a  few Agile people need to think less about clearing backlogs and more about architecture, less about their being agile in development and more about creating systems in which agility is enabled.

So given that principle, it’s going to be a lot easier to define the objects and interactions and make some suggestions about patterns that could emerge, interestingly I could hand-draw those last night in conversations with myself while (Spoiler alert) contemplating the potential horror of Emily Blunt’s face erupting with syphilitic ulcers.  The essence of designing decision support systems, and I’ve been doing that for forty years now, is to keep things simple in execution but allow complexity to emerge.  Hence actors and artefacts with defined interactions lead it to that.

One thing up front, everything I am talking about here could be done with technologies other than our SenseMaker®.  I happen to think that the high abstraction of our metadata lends itself to abduction and serendipitous discovery which means a better capacity for innovation, as well as discovery but what I am going to outline here, is not dependent on its use.  That said SenseMaker® Genba, which is currently in beta has been designed with this type of application in mind and with the parallel development of open APIs that will allow integration with existing operational systems.

I’m using objects as a catch-all term, with a nod to object orientation;  yes I could get into inheritance and polymorphism, and I suspect both will be a crucial aspect of long-term use, but for the moment I’m keeping things simple.  So they fall into two categories as follows:

Artefacts

1. Best practice documents, guidelines, manuals etc.  I’m assuming that these are online and available to actors and can be tagged with links by page, paragraph, phrase etc.  Any such document will generally be under version control and have some form of authority.
2. Micro-narratives in digital form are either stored as is or with metadata.  In SenseMaker® we allow combinations of written, aural and visual with metadata from different perspectives.  So a nurse can take a picture of a patient, record the patient’s story and add written material; the combination can be signified by the patient and the nurse and downstream by other actors.  When we do this work we also focus on motivating people by engagement in a new programme or by removing a bureaucratic task they dislike.  This is key you do not want to add any burden,
3. As a special case in sensitive or secure environments, original documents may be available only in summary form or as metadata.  That means if they are discovered then they can be requested in context.  Far too much work goes into data scrubbing and integration when human interaction may suffice.

Actors

1. Individuals as individual
2. Roles
3. Role combinations
4. Teams
5. Ad hoc groups possibly linked to activities

Interactions

1. Read any artefact and add a comment and interpretation
2. Links any aspect of any artefact to any other artefact at any level (word, paragraph etc)
3. Link any interaction to other actors to trigger response and awareness>

My intent should now be becoming clearer and I should add that this type of approach will also require some basic reporting and statistical tools – we have used Tableau as well as R and Excel in our own work.  You will also need technical support to monitor use, provide assistance with reporting and access and keep a note of repeating patterns that might justify additional automation.

So let’s take a couple of uses cases to illustrate how this works:

1. Using new employees as journal keepers and journalists during their induction period.  They are also given access to senior staff to capture their stories and experience.  As a part of their induction, they have to learn best and good practices so they are encouraged to tag their own and other people’s narratives into the said document and this can be done by multiple cohorts over time.  The results can then be searched -by cohort group only, by cultural or organisation groups, by age or whatever.  I may only want to know how people like me interpreted the material or I may seek contrasts including using that to gain an understanding of different cultures.
2. As a lessons learning programme on projects and other activities, capturing my own stories and that of other actors (such as users for an Agile team) and tagging them into documents as well as constructing new material for retrospectives reviews and the like.  Material captured in the field under fire is more honest and also has higher utility in contextualising more traditional material.  It also (per my post of yesterday) allows a smoother transition between purely tacit and purely explicit knowledge.

Photo by Daniele Levis Pelusi on Unsplash