SCENARIO-BASED MODELLING: Storytelling our way to success. 1

“The soft stuff is always the hard stuff.”


Whoever said ‘the soft stuff is the hard stuff’ was right.  In fact, Douglas R. Conant, coauthor of TouchPoints: Creating Powerful Leadership Connections in the Smallest of Moments, when talking about an excerpt from The 3rd Alternative: Solving Life’s Most Difficult Problems, by Stephen R. Covey, goes on to note:

“In my 35-year corporate journey and my 60-year life journey, I have consistently found that the thorniest problems I face each day are soft stuff — problems of intention, understanding, communication, and interpersonal effectiveness — not hard stuff such as return on investment and other quantitative challenges. Inevitably, I have found myself needing to step back from the problem, listen more carefully, and frame the conflict more thoughtfully, while still finding a way to advance the corporate agenda empathetically. Most of the time, interestingly, this has led to a more promising path forward and a better relationship, which in turn has made the next conflict easier to deal with.”

Douglas R. Conant.

Conant is talking about the most pressing problem in modern organisations – making sense of stuff.

Sense Making

Companies today are awash with data.  Big data.  Small data.  Sharp data.  Fuzzy data.  Indeed, there are myriad software companies offering niche and bespoke software to help manage and analyse data.  Data, however is only one-dimensional.  To make sense of inforamtion is, essentially, to turn it into knowledge. To do this we need to contextualise it within the frameworks of our own understanding.  This is a phenomenally important point in sense-making; the notion of understanding something within the parameters of our own metal frameworks and it is something that most people can immediately recognise within their every day work.


Take, for instance, the building of a bridge.  The mental framework by which an accountant understands risks in building the bridge is uniquely different from the way an engineer understands the risks or indeed how a lawyer sees those very same risks.  Each was educated differently and the mental models they all use to conceptualise the same risks (for example)  leads to different understandings.  Knowledge has broad utility – it is polyvalent – but it needs to be contextualised before it can be caplitalised.

Knowledge has broad utility – it is polyvalent – but it needs to be contextualised before it can be caplitalised.

For instance, take again the same risk of a structural weakness within the new bridge.  The accountant will understand it as a financial problem, the engineer will understand it as a design issue and the lawyer will see some form of liability and warranty issue.  Ontologically, the ‘thing’ is the same but its context is different.  However, in order to make decisions based on their understanding, each person builds a ‘mental model’ to re-contextualise this new knowledge (with some additional information).

There is a problem.

Just like when we all learned to add fractions when we were 8, we have to have a ‘common denominator’ when we add models together.  I call this calibration, i.e. the art and science of creating a common denominator among models in order to combine and make sense of them.


Why do we need to calibrate?  Because trying to analyse vast amounts of the same type of information only increases information overload.  It is a key tenent of Knowledge Management that increasing variation decreases overload.

It is a key tenent of Knowledge Management that increasing variation decreases overload.

We know this to be intuitively correct.  We know that staring at reams and reams of data on a spreadsheet will not lead to an epiphany.  The clouds will not part and the trumpets will not blare and no shepherd in the sky will point the right way.  Overload and confusion occurs when one has too much of the same kind of information.  Making sense of something requires more variety.  In fact, overload only increases puzzlement due to the amount of uncertainty and imprecision in the data.  This, in turn, leads to greater deliberation which then leads to increased emotional arousal.  The ensuing ‘management hysteria’ is all too easily recognisable.  It leads to much more cost growth as senior management spend time and energy trying to make sense of a problem and it also leads to further strategic risk and lost opportunity as these same people don’t do their own jobs whilst trying to make sense of it.


In order to make sense, therefore, we need to aggregate and analyse disparate, calibrated models.  In other words, we need to look at the information from a variety of different perspectives through a variety of lenses.  The notion that IT companies would have us believe, that we can simply pour a load of wild data into a big tech hopper and have it spit out answers like some modern Delphic oracle is absurd.

The notion that IT companies would have us believe, that we can simply pour a load of wild data into a big tech hopper and have it spit out answers like some modern Delphic oracle is absurd.

Information still needs a lot of structural similarity if it’s to be calibrated and analysed by both technology and our own brains.

The diagram below gives an outline as to how this is done but it is only part of the equation.  Once the data is analysed and valid inferences are made then we still are only partially on our way to better understanding.  We still need those inferences to be contextualised and explained back to us in order for the answers to crystalise.  For example, in our model of a bridge, we may make valid inferences of engineering problems based on a detailed analysis of the schedule and the Earned Value but we still don’t know it that’s correct.


As an accountant or lawyer, therefore, in order to make sense of the technical risks we need the engineers to play back our inferences in our own language.  The easiest way to do this is through storytelling.  Storytelling is a new take on an old phenomenon.  It is the rediscovery of possibly the oldest practice of knowledge management – a practice which has come to the fore out of necessity and due to the abysmal failure of IT in this field.

Scenario-Based Model Development copy

Using our diagram above in our fictitious example, we can see how the Legal and Finance teams, armed with new analysis-based  information, seek to understand how the programme may be recovered.   They themselves have nowhere near enough contextual information or technical understanding of either the makeup or execution of such a complex programme but they do know it isn’t going according to plan.

So, with new analysis they engage the Project Managers in a series of detailed conversations whereby the technical experts tell their ‘stories’ of how they intend to right-side the ailing project.

Notice the key differentiator between a bedtime story and a business story – DETAIL!  Asking a broad generalised question typically elicits a stormy response.  Being non-specific is either adversarial or leaves too much room to evade the question altogether.  Engaging in specific narratives around particular scenarios (backed up by their S-curves) forces the managers to contextualise the right information in the right way.

From an organisational perspective, specific scenario-based storytelling forces manages into a positive, inquistive and non-adversarial narrative on how they are going to make things work without having to painfully translate technical data.  Done right, scenario based modelling is an ideal way to squeeze the most out of human capital without massive IT spends.






Give Me Back My Silo! The problems with cross-functional collaboration Reply

Collaboration requires trust.  Collaboration often requires risk with new relationships.  Collaboration requires doing something new.  In short, people do not like to collaborate.  People do not like to share.  People like safe relationships with others from the same educational background.  They collaborate with those who  share the same problem solving paradigms, issue identification and decision making criteria.  Knowledge and knowledge-intensive work is intensely hierarchical.  People guard their secrets and their weaknesses.  So, although the business may look towards de-layered flexible working structures as a cost saving measure, people do not necessarily follow.  Knowledge is and has always been a powerful means to embed and entrench power within an organisational hierarchy. 

Collaboration, however, is worth money.  In 1969 Peter Drucker noted that sharing and managing knowledge is “essential to organisational success” because it ensures sustainable competitive advantage.  How then do businesses best help employees engage and collaborate in a meaningful way which creates business value?quopte

Larry Prusak notes that large organisations performing complex tasks have been around since about 1860.  So far the corporate community has had over 160 years to solve this problem and we seem no nearer to it.  He posits that it seems so hard because, essentially, it is not possible.  There is no science behind it only heuristics.  Solutions are not algorithmic but rather anecdotally commonsensical.


The problem is that cross-functional collaboration is counter-intuitive.  It is not a normal feeling to share intra-discipline information with other functions around the business.  Complex ideas are not easily understood or translated outside professional groups.  Geologists and pharmacists can only explain an important discovery if it has immediately recognisable business value.  Lawyers can only explain the value of an idea if it averts imminent loss.  Manufacturers articulate the value of a process where it creates savings or improves revenue.  Everything else is esoteric gibberish. 

Thought leaders such as Prusak and Drucker well note that the answer to the standard problems of intra-disciplinary collaboration is to create well supported Communities-of-Practices (CoP).  Practices, they note, are best kept to about 200 members who meet about 2-3 times per year.  In the diagram below, such CoPs may extend beyond the walls of the organisation and may even include other competitors. 

To misquote Shoeless Joe Jackson in “Field of Dreams:  ‘Build it and they will not necessarily come.’  Increasing networks and their IT support will not necessarily produce any tangible return.  Most likely it will needlessly absorb organisational time and money only to increase the personal fiefdoms and create further bottlenecks and over-centralisation.  Such networks will help to increase the volume of knowledge within an organisation and problem solving but, critically, it will not help convert it it into business value.  In order to realise business value knowledge must cross functional boundaries.   

xFunctional Collaboration


Knowledge will flow relatively freely within disciplines.  CoPs  address the problem of collaboration for such knowledge generation and problem solving.   Knowledge does, however, need to be forced across functional boundaries.  Commercial teams will not be drawn to good ideas.  They will not recognise them.  Workflows are essential to implement collaboration across organisational functions.  The key to implementing cross-functional (as opposed to hierarchical) workflows is to embed them in governance.  People will collaborate across functions if they cannot achieve their goals or get their work done.  The difficulty with any governance, however, is making sure that these ‘gates’ do not become bottlenecks.  Although the onus may be on the professions to present their work for approvals, so to is there a burden on the executive silos to understand and approve in a timely manner.  In this way there should be no burden on the operational disciplines to present their work on an ad hoc basis.  The presentational form must be agreed between professional and executive branches to understand.  There is no sense in letting commercial elements of the business dumb down sophisticated ideas.   Only when both parts of the organisation come together in mutual understanding is there truly any sense of collaboration across functions.

In the end trust will always trump value when it comes to the exchange of knowledge.   Indeed, businesses must actually increase organisational silos and insulate them rather than fight them.   The solution is not to break down the silos but to build them up.  People like safe relationships.  They like to feel warm and closeted from ‘outsiders’.  The key is to increase their sense of importance through revenue generating communities of practice in order to increase the volume of knowledge in an organisation.  Only then should the business increase the pace of converting knowledge to revenue through cross-functional workflows strictly embedded in the corporate governance.