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

“The soft stuff is always the hard stuff.”

Unknown.

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.

Contextualisation

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.

Calibration

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.

De-Mystifying

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.

Storytelling

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.

 

 

 

 

 

Leveraging social media data analytics to improve M&A – Lexology Reply

Social media part 3: leveraging social media data analytics to improve M&A – Lexology.

Data Analytics for M&AThere is no question that social media has a role to play in M&A activity.  In a recent survey by Toronto based international law firm Fasken Martineau; respondents reported that they were not only using social media to communicate deals but also for research and due diligence .

  • 36%  for research.
  • 48% for investigation.
  • 72% like LinkedIn to research personalities.  Only 50% said the same of Facebook.
  • 78% disclose transactions through Facebook and 44% use LinkedIn.

More importantly

  • 77% said they have no social media strategy, and
  • 65% said they have no intention of developing one.

DATA v SENTIMENT

There are 2 benefits of social media: (i) expert opinion, and (ii) trending sentiment.  Much of the hype behind the trends in Big Data are about connecting these 2 powerful elements with the hard numbers around corporate valuations.  For instance, an acquirer may wish balance valuations with the trending market sentiment and the opinions of experts.  An extreme example of this is the Facebook IPO where market hype vastly outweighed traditional valuations.  So much so that Warren Buffet said that he had no idea how the valuation was so high and that he just couldn’t value companies like this.  Social media is often seen as an echo chamber; a small community talking to itself.  Posts range from  self-aggrandisement to advertising puffery with very few hard facts and figures in between.  What few figures are there may be real, may be fictional or may be somewhere in between.  The value is in aggregating these figures but the algorithms needed to ensure that the right weighting is placed against the right number based on author, time etc (let alone how they account for hearsay).  the maths behind this is hard enough let alone the semantic interpretation by computers.  Needless to say, when this sort of calculating can be done it will be worthwhile for many and will be, initially, a very, very expensive service.

The moral of the story is  – BEWARE!

Social media remains useful for advertising and developing an extended network of sector contacts in order to deepen one’s contextual market knowledge.  However, as an analytical tool, to my mind, it is still out there with witchcraft.