Sense-Making

Making Sense of Challenging Issues

As leaders we all face a range of challenging issues where our current approaches are not often yielding the outcomes we desire.

Whether we are facing understanding our customers changing needs, trying to deliver our objectives for environment impacts and sustainability or are actively involved in tackling challenging issues with our communities our world is full of wicked problems.

Whilst we can engineer solutions that fix a machine and have an increasing array of clever, faster, and smarter technology at our fingertips many of us will still struggle in devising solutions to wicked challenges. These challenges have no obvious solution, things are messy and interconnected and multiple factors, groups, individuals, and perspectives are at play.

We either try what we have done before expecting different results, are beaten into inaction, attempt to seek more information before acting or worse of all invest in a silver bullet that has alleged has solved this challenge elsewhere (agnostic of the context or unique characteristics of the situation).

A more helpful solution is to both “act to understand” and get “better information” rather than necessarily more data. Sense-making is both gathering “better information” and taking small actions to get rapid feedback. This in turn leads to the discovery of the next useful piece of information.

“None of us is as smart as all of us.” Ken Blanchard

Better Information
So, what is “better information”?
Having spent many years in corporate performance enhancement, the critical factors in my experience are:
Relevant
Information is only as good as it the decision it supports. Its needs to be relevant to the context and fit for purpose.
Concise
Information needs to be digestible whilst retaining meaning. Filters and summaries should be reviewed to ensure bias doesn’t creep in and salient messages are not obscured.
Reliable
Information that is “generally accurate” is to me preferred over “precisely wrong”. The more hands and time information passes through the less reliable and useful it will be (even if reconciled to within an inch of its life).
Timely
Information that relates to the last year many months after that year has ended is the domain of auditors. Information is better when it is taken closer to the event and shared quickly. What we remember rapidly deteriorates over time.
There can be numerous other qualities but if you collect the above you have, better information that is to the point, credible and is presented in a timely manner

But I thought we got a lot of information?

Cleverer minds can point to the scientific basis for many of the failings in existing approaches, but the following is a brief critique based on personal and real-world experiences.

Big Data

It is undeniable that the Googles, Amazon and social media companies can use big data to target their users using clever algorithms and a mass of computing power.

However, in the real world just getting everything isn’t always effective. I know an organisation that was convinced that it could save many dollars by capturing detailed asset information. It overlooked that the fact that its suppliers could not provide that detail. However, even if they had been able to get the data their procurement processes would have prevented them getting economies of scale they sort.

Thus, big data may not be relevant, reliable, or concise (it can be timely if enough technical resource is available)

Thick Data

I will use this term to describe large research projects, one to one interviewing and large-scale desk top analysis of data.

Generally, this fails on the concise and timely fronts immediately. Done well it can provide relevant and perhaps new information, but reliability is often down to the quality of the people conducting the process. It is widely accepted that personal biases are difficult to mitigate.

I have been a party to this style of process as an eager participant to understanding a organisations willingness to change. The conclusion was that the executive sponsor was in fact a roadblock to that change. No decision was reached, or action taken and proved to be a very costly process.

Thin Data (Surveys / Questionnaires / Polls)

We love surveys, hardly a day goes by without an email on how much I enjoyed an interaction with a company or a LinkedIn poll.

They can be relevant but asking me on a scale of 5 how likely I am to recommend your company to a friend for a small purchase that hasn’t been delivered yet doesn’t provide much useful feedback.

They often seem to miss the definition of concise - either I get a single line of various smiley faces or 152 questions for a peer review of a colleague.

Unless very well-designed reliability is questionable. I know what answer you want me to give and have a pretty good idea I am not going to click the dissatisfied option which will then force me to type why I didn’t like your service!

The Alternative

My preferred term here is “Rich Data”.

This pairs contextual information with some relevant metrics. At JP Morgan we approached this by focusing first on key metrics then relentlessly tracking down the context. The combination of qualitative and quantitative (or relative measures) is key and allowed us to weave that into a story and pictures of how we were performing. Similarly, a good story supported by relevant metrics provides the framework to detect weak signals – those subtle changes in narrative that outline how the underlying environment is changing.

Sensemaker®

Sensemaker is an engagement process and framework to collate personal lived experiences by narrative and evaluate factors which provide context and make sense of that narrative.

Those stories and factors can be mapped, and that landscape considered to generate actions and interventions to produce more stories like this and less stories like that. It is effectively a probe seeking to discover what is “really” there devoid of our assumptions, bias, or preconceptions.

Without delving into the detail of anthro-complexity, complex adaptive systems or the Cynefin framework the process of developing a probe introduces key elements of these concepts to the team. In my personal experience the experience of designing a probe breaks down traditional ways of thinking and shifts those involved towards being more “open to experience”. This enables them to become more comfortable with being uncomfortable which is essential to make progress on ambiguous and complex issues.

Typically, the design and development process involve:

framing the issue or topic to be explored
developing potentially relevant factors to be explored
creating relative measures for those factors (signifiers)
distribution of the probe
review and analysis (requiring a degree of technical expertise)
considering the information to create “safe to try” actions

Signifiers

Signifiers differ from typical linear rating scales in surveys. These supplement simpler survey collection data.

Whereas a simple rating scale would measure a single dimension a “Dyad” is a sliding scale rating the relative importance of 2 competing factors:

Here we get the sense that the relative importance of changing scope versus time pressures.

A further feature is the “Triad”.

Triads are used to measure the relative importance of 3 completing dimensions relevant to the narrative. Cognitively they are considered more sophisticated and informative than “tick all that apply” and more concise that rating all 3 items individually. They also provide a level of importance that would need a “rate these in order of importance” survey question. Thus, they provide twin benefits of stimulating higher cognitive processes and condensing several questions into a single response.

Here we get the sense that unrealistic expectations along with a degree of poor management contribute more to the outcome illustrated by the story rather than a perceived lack of skills.

Once the probe has been completed a range of analytics are available. These are dynamic and can be used to drill through to the detailed story (useful for outliers), show clusters of responses indicating the factors at play and provide the source for further probes and actions.

Here we can sense that there is the opportunity to generate less stories illustrated in the bottom left and more like those closer to the centre. We also can gain the sense from the outliers as to perhaps so specific interventions that might prevent a similar story (bottom right) emerging in future.

This enables us to facilitate change shifting from blame (what went wrong with project x) to how can we work together to create the outcomes illustrated across this range of projects.

On a large transformation a few insignificant improvements were identified in response to the question “how your department do better?”. Asking “what could other departments do better?” provoked a deluge of suggestions. The above approach seeks to break down a them versus us thinking.

Summary
SIn a nutshell, for more challenging or complex [1] issues Sensemaker® provides more useful information to inform decisions than traditional approaches.
The analogy is that if we were interested in discovering the culture of another country we could:
• Wade through guidebooks, internet searches etc (big data)
• Send a journalist to live with the locals for a year (thick data)
• Take a mini break (thin data)
• Visit people in their own homes and listen to their stories (rich data)
Sensemaker® takes us into our communities, provides true engagement and enables intelligent decision making in a complex world. In my experience a client initiated a probe into the design of new play spaces by using Sensemaker® with children by working with schools. Not only did this provide incredible insight, but the final solution was also both more economic and saw greater use.
Getting Started
If you are exhausted with your traditional approaches, have wicked challenges to resolve and are open to considering a new approach that leverages your communities and are over silver bullet solutions contact us

Notes
Dudley Bevins & Associates (NZ) Limited partners with the Cynefin Centre Research (NZ) Limited.
Sensemaker® is registered trademark of the Cynefin Centre. All trademarks are acknowledged.

[1] “If the evidence supports conflicting hypotheses about what to do and you can’t resolve those conflicts in the time available for decision making then it’s complex.” Dave Snowdon