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.






Protecting Information: a cascading approach to information security Reply

There is no easy way to protect corporate information.  Protecting government information is easy because they have their own networks.  Life in commercial society is somewhat more different but if businesses follow these 6 steps they will be better off:

  1. DEFINE. Don’t protect everything.  It costs too much and it’s a waste of time.  Define what is intellectual property (patents, trademarks etc).  This is the stuff that (a) is legally protectable, and (b) it is what the market will pay for (i.e. it isn’t an intangible asset – it has dollar value).  Intangible assets which are collectively seen as valuable are classed as intellectual capital.  Everything else is either supporting information or junk.  
  2. DETERMINE.  Determine what goes where as part of your internal processes and workflows.  Remember, it gets used if it’s part of the workflow.  Proper IP should reside on closed systems with certain roles acting as guardians, e.g. in-house counsel, financial comptroller etc).  Intellectual capital, things such as frameworks, processes, analytical methods should sit on systems with role based access privileges  so that repeated access (e.g. for screenshots) is noted. Printing and downloading should be limited and part of a defined process.  Thin client technology helps but the most important means of guarding this stuff is to make it compartmentalised (i.e. various levels of decomposition etc) so that it’s hard to gather it all together it once yet easy enough to use as a reference tool for team use.
  3. DEVELOP.  Keep developing your intellectual capital.  It’s less worthwhile stealing information which is outdated.  Moreover, make sure that development is cross-functional and multi-disciplinary.  This is akin to holding the encryption key to your intellectual capital.  If only a few central people know how the framework all works together then even if it is taken by former employees they will, at least, be unable to build on it.
  4. IDENTIFY.  Identify the people who are going to access this sort of information.  Now build these roles and enforce them with internal business processes and physical security measures to make this work.
  5. INSPECT.  Tag your information and gain access to employee hard drives.  There is no way around it.  Be subtle about how you approach knowledge workers and develop socially enforceable norms around the use of corporate proprietary information.
  6. INVEST.  For intellectual capital works invest in a great means of display.  If you’re afraid of other firms ripping of your frameworks or processes then get a graphic artist to create excellent visual representations.  Then you can protect that image through contracts with employees and clients.  Any use outside of your parameters can be met with a solicitor’s letter.

Most importantly, invest in your people and invest in the development of new knowledge.  If they want to take it, they will but nothing secures information like happy employees and few will want to steal outdated information which they can’t build on.

The Value of Information Reply


Information is expensive, of that there is no doubt.  The cost of information technology as a percentage of revenue remains high despite falling capital costs and the cost to maintain specialised management skills to sort and interpret the incredible volume of information.  The question is whether information is actually financially valuable.  Companies spend a large amount on managing information but what return do they see?  What is the value-added figure for information?

Information Value-Added = (Total Revenues + Intellectual Property + Intellectual Capital) – (operations value-added – capital value-added – all purchases of materials, energy and services).

This is to say that once all labour, expenses and capital (that is not part of an information system) is accounted for, the cost is subtracted from the total of gross revenues (plus IP).  In other words, it is the part of the profit which is not directly accounted for by operations or increased capital value.  It is profit which is attained purely through better managerial decision making.  This might be achieving better terms and conditions in the supply of a product or it might be in the reduction of insurance costs on a given contract.


Note that I include the term ‘intellectual capital’.  I define intellectual capital as an information asset which, if valued, would increase the value of the firm.  This is not to say that the information asset itself has value (such as patents and trademarks which may be bought and sold and therefore are IP) but rather information such as mailing lists, customer preferences, methodologies, databases etc.  These are generally valued in a business as goodwill but ideally should be valued separately.


Information value as an index can be calculated through the ratio of Information value-added divided by information costs (see previous blog). So, in any given business unit the value of its information is the additional money earned from management’s better decision making.  If this results in better operations then this is ‘operations value-added’ and should not be included.  However, increased revenue not directly attributable to operations should be included as ‘information value-added’.


The standard answer is no.  In most companies and business units gross revenues (plus IP/IC) may increase but unless unit labour costs and technology costs are kept in check then the overall productivity of information is limited.  In many managerial accounting case studies the value of information is counted as being gross revenue less the cost of IT (where the analysis takes place in a non-operational function such as purchasing).   However, with reductions in IT capital costs over the years one would assume that the ROIC from IT is great.  By adding associated increased labour costs, however, the story is different.  Year on year declines are evident.  The story is clear – information is no longer adding much value in business.


In order to achieve greater value from corporate information a business must do 2 things:

Firstly, reduce per unit managerial labour costs.  Instead of merely reducing head count the per unit cost of management should be reduced.  In this way the company is working cheaper not harder.  Overall headcount should be the focus of operational process performance enhancements and not structural adjustments.

Secondly, increase profitability from managerial, non-operational decision making (because operational decisions are subject to their own dynamics).  With a renewed focus on (non-operational) decisions which increase profit or reduce costs businesses will find that their ratio of earnings:information cost indices increase favourably.


In order to achieve a greater return on invested capital companies seek to ‘sweat the assets’.  However, labour costs associated with processing and managing information will always rise faster than capital expenditure for IT (as well as associated operational expenditure for service costs of cloud computing).  Sweating IT assets is of limited value since they depreciate so quickly that they have no value virtually as soon as they are purchased.  In fact, increasing the value of information will largely come from increased revenue from higher management performance not from lower IT costs.  So, in order to achieve a greater return on information companies should seek to ‘sweat the management’.