5 Golden Rules for Flexible Project Management

I previously wrote about methods to improve two aspects of project management

1. Knowledge capture, and
2. Communication

A focus on the importance of capturing knowledge and improved communication was inspired by a recent publication by Raconteur (Project Management, raconteur.net, #0376, 22\05\2016). Within this Raconteur publication was a piece entitled “The Five Golden Rules of Project Flexibility” (p.4), and provides the inspiration for this article.

Flexibility is essential for sustainable success, but for human beings, it can be very difficult to think and behave with flexibility. Below, I’ll outline some reasons behind this difficulty, before revealing Raconteur’s 5 Golden Rules.

Mining the Tacit Database

If an organisation is trying to use experience as a resource to learn and develop, what’s an effective approach? Every organisation has a “tacit database”, the experience based skills and reasoning people use every day to effectively complete tasks, solve problems, and innovate. The tacit database, however, is combined of taken for granted rules of thumb (heuristics), meaning that people do impressive things, but struggle to explain how they did it.  This leaves an organisation knowing far more than it can say– the tacit database is frequently a hidden asset.

Why Instructions Rarely Get Followed

All situations involve change. Yet most instructions, plans and procedures are static, and bear little resemblance to how frontline workers actually perform and behave. This could be because most instructions, plans and procedures assume that frontline workers passively process data. Instead, frontline workers interact with data in dynamic ways which adapt plans and instructions to meet the challenges of specific situations. This can leave what actually works well in an organisation invisible.

Getting Answers from Unusual Places

Sometimes the answer to a problem comes from an unusual place that is right in front of us. This article covers how we can potentially transfer expertise from one disciplinary subject to another.

Many organisations contain expertise which stretches across multiple disciplines. For example, construction, engineering, research and development all contain reservoirs of expertise particular to their discipline’s training, experience, and cultural sense making. All these disciplines apply their expertise to design, deliver and problem solve during the completion of tasks. Problem solving, and dealing with tough non routine cases, are generally the points where expertise becomes most active and innovation takes place (Taleb, 2012, Klein, 2014).

Linking Research and Evaluation To Decision Making and Planning

Making a decision is the sum of some form of analysis. Fast, slow, statistical, intuitive or any combination of factors leads a person to reach a conclusion and make a decision. Since making a decision means letting go of other options, it is a process which frequently becomes bottlenecked. Fear of letting go of the wrong option, of making a mistake, or simply trying to find the perfect answer can trap a person in permanent analysis, or even worse, permanent data collection. This situation can become particularly acute when a person or group that designs plans is separated from the people who deliver and receive the consequences of plans. The relative isolation reduces feedback loops with the environment and increases guess work, stress and uncertainty.

Evaluation, Time, Money and Methods

Whenever you are tasked or contracted to carry out an evaluation of a project, particularly in health, there are nearly always 2 immediate challenges

• Investment- the amount of money and time available to carry the evaluation out
• Access- the availability of relevant people and populations to provide data

These issues immediately restrict the capacity of a researcher to conduct fieldwork. Time spent out of the office interviewing respondents is time consuming. Factor in the time taken to organise interviews and other data collection points, and very quickly you have either burned through the budget or are left with an inadequate amount of data as time runs out. Designing and conducting research methods to scale is always a problem, the gap between the “theoretically desirable and the practically possible”. But funders pay for results so it’s a problem that requires solving.

A method of scaling projects is to train non-researchers from within target populations, or people who have easy access to the target populations, to collect the data. This works best with larger scale projects, so you avoid skews with small numbers (for example, only 10 total respondents all interviewed together in the same room). And for similar reasons the approach works best across multiple independent sites, because multiple sites (contexts) stand a better chance of locating genuine reoccurring themes and also insight specific to local context. Below is a recent example from the method angle.

I’ve recently completed a project where I was part of a team where we collected qualitative data from over 250 respondents. The data was collected by non-researchers from across multiple sites and the project was focused on assessing priority areas for strategic decision making. The design of the data collection helped us deliver to budget and on time, and importantly, produce analysis the funder was happy with.

When designing the methods for use by the non-researchers, we looked at other domains where good quality data had to be collected rapidly, and, was not collected by research professionals. The domain which we used was wild firefighting, which I examined through the work of Weick and Sutcliffe (2007). The chaotic nature of a wild fire means firefighters have to debrief rapidly, pass on learning, identify vital cues and focus attention on what is most important. These outcomes were exactly what we were looking to achieve through our data collection. To ensure these conditions are met there exist interview schedules which are designed to collect data from firefighters as they leave the field.

I adapted one of the fire fighter interview schedules (Weick and Sutcliffe, 2007) for the non-researchers in our project to use in both interviews and focus groups. The questions were structured to collect anticipation-what people think could and should happen next, and also reflection-what has gone wrong specifically in the past. These type of questions are useful for separating out needs from wants and setting priorities. Most of all, these type of questions enable strategic decision making to be improved via insight.

For our team and project, the adapted interview schedule worked very well, better than we could have hoped. We had piloted it and stress tested it before it went “live”, and this allowed us to make further adjustments. But most significantly, it was very easy for non-researchers to understand the underlying logic of the schedule and so make adjustments independently, depending on the context and flow, and for them to use practically in the field. This should have been no surprise, the interview schedule which we adapted was designed specifically to achieve these outcomes.

Reading

Weick, K. E., & Sutcliffe, K. M. (2007). Managing the Unexpected: Resilient Performance in and Age of Uncertainty, Second Edition. San Francisco, CA: Jossey-Bass

The Benefits of Letting Go

Here is a thought which may or may not challenge. A lot of time, resource and effort is placed into managing change, but significantly less time, resource and effort is placed into the process of “letting go”. This is what I mean-

For decades research has been carried out on the confirmation bias (Kahneman, 2011 provides a good resource). The confirmation bias is about how once human beings reach a conclusion, form an opinion, create a plausible story they selectively search for information to prove the conclusion correct, whilst disregarding and explaining away any information which contradicts it. In other words, human beings have a tough time letting go.

Change management as a discipline and a subject is fraught with problems, many of which are based on resistance to change, of people (the followers) not being able to let go of a belief which forms a barrier. Change management is equally, maybe more significantly, fraught with leaders who couldn’t let go of an idea which was demonstrably bad, frequently in the face of overwhelming evidence.

You could examine the UK government’s Poll Tax proposal, you could examine New Coke, or you could examine the accounts of Enron for years before the fall. All of these examples have one thing in common-they all involved people unwilling to let go of something, both leaders and followers.

Gary Klein’s latest book, Seeing What Others Don’t (2014) suggests that one way people gain insight is to suddenly let go of a belief which they was holding them back. In other words, these people unlocked some form of the confirmation bias by removing something. The result is adaption, insight and innovation. For a company this could mean a more flexible approach to change and greater levels of innovation. It could also mean picking up risks faster and responding quickly.

A resource for learning how to let go is to examine people who operate on the edges of uncertainty. An example would be a climber. A climber has to start out with a plan, and this plan, because potentially their life is at stake, needs to be built around the question “what could go wrong”? This immediately focuses the mind and what needs to be let go.

Once the plan is executed, feedback is immediate; each action produces data on how well the plan is working. Because feedback is immediate, adjustments and adaptions can be made. These adjustments may deviate significantly from the original plan, but holding onto it in the light of contradictions would be fatal.

In certain professions, hobbies and activities the participants simply have to know how to let go in order to survive and succeed. By exploring the mental models these people use to frequently let go we can learn a lot about how to approach change, innovation, uncertainty and risk.

Reading

Kahneman, D. (2011) Thinking Fast and Slow. Penguin
Klein, G. (2014) Seeing What Others Don’t. The Remarkable Ways We Gain Insight. Notable Books.

Innovation as Recovery

Innovation is potentially the most counter intuitive concept you come across. To me innovation is the ability to spot links between different domains and put them together, Nassim N Taleb (2014) provides the perfect example- taking wheels and putting them on suitcases. Klein’s (2014) recent work on insight narrows down three potential routes to innovation-
1- Making a connection between two different domains
2- Spotting a contradiction in a connection
3- Discarding a connection out of creative desperation

I’d like to focus on the third point, creative desperation. Creative desperation is illustrated by situations where following routine would have meant catastrophe, something different HAD to be tried. Weick (2003) discuss the Mann Gulch disaster where a wildfire became so out of control the attending firefighters had to run for their lives. The fire spread so fast that running was only delaying the inevitable. The firefighter foreman, Wager Dodge, stopped, set the ground around him on fire and lay in the ash; the fire blazed around him, but the escape fire worked, he survived, unburnt. The tragedy produced a new go to method for firefighters faced with an out of control wildfire which could not be out run.

The disruption of routine can produce incredible feats of improvisation and innovation, and this is the counter intuitive element of innovation. Threats to innovation are what most societies struggle for-stability, routine, degrees of certainty. It’s difficult to take the risks needed for innovation when everything is going to plan; it’s even more difficult to be bothered. But imagine a society where everything was stable and routine, or a society led to believe everything was stable and routine, and then suddenly it was hit by some unexpected event. This would be a society poorly set up to innovate out of trouble, to turn the disruption into something better.

Back in 1922, philosopher John Dewey defined life as “interruptions and recoveries”, and this is probably a better way for governments, organisations and individuals to measure success- not how long they can maintain faux stability but rather how well they recover from interruptions. Recovery is where innovation, the development of something better, can and should take place.

Reading
Taleb, N.N (2014) Anti Fragile- Things that Gain from Disorder. Penguin
Weick, K.E. (2003) Positive Organizing and Organizational Tragedy in Positive Organizational Scholarship: Foundations of a New Discipline. Dutton, R. and Quinn, J. (Eds) pp.66-80.
Klein, G. (2014) Seeing What Others Don’t: the remarkable ways we gain insight. Public Affairs.

Echo Breaker-Innovation

Echo Breaker Innovation is about a single question-how does an academic idea add value to a business? It’s a question that’s tried to be answered with big ticket sales, marketing, networking events all with limited to no results. It’s a tough question to answer and Nassim Taleb (2013) has empirically demonstrated that no university, anywhere, has ever produced anything new or productive. However, we’re going to try and answer the question by a) taking on the risk commercially b) reframing the question as a problem which has already been answered before.

Ok, a) taking on the risk commercially- we’ve formed an R & D company, Echo Breaker Research and Analysis Ltd, whose future is going to be dependent on answering this question effectively; this site will chart the progress. B) Reframing the question- getting an academic idea to add value into a business isn’t a sales\ marketing issue, it’s a knowledge acquisition issue.

The field of natural decision making faces a central challenge- how does an expert perform so effortlessly yet finds it so hard to articulate how they do it? The reason is tacit and semi tacit knowledge-things people do without even thinking about, and things people do which they feel is barely worth mentioning. This knowledge is the ingredients of expert sense making but incredibly difficult to get at. Traditional methods like interviews and questionnaires can extract limited amounts of data, but don’t allow complete access to how experts make sense of situations-especially challenging, non-routine events.

You can imagine how this issue has caused a lot of headaches for software developers, products on which success can literally be life or death. As a result, a range of methods have been developed to illicit this knowledge from experts (see Crandell et al, 2006, Rugg et al, 2013).
Getting ideas from one domain effectively to another is innovation; I think Taleb’s (2012) example of wheels on suit cases is an excellent example. To achieve this, the structure of one domain must become transparent so cross links can be identified with the structure of another domain. The structure of a domain is culturally constructed meaning its messy and tacit\ semi tacit. Discovering the structure of a domain is essentially the same problem as trying to elicit tacit and semi tacit knowledge from an expert.

So, you have two domains, the domain of the academic idea (it’s creator, the institution, the subject discipline for example) and the domain of a potential commercial sector (it’s risks, culture, and how value gets added for example). To get the idea from the one domain to the other domain means identifying the relative structures and then making the cross links-literally, effectively translating the idea. This may change the idea significantly, but the change is aimed at adding genuine value not maintaining an abstract integrity.

This is the challenge- to apply knowledge acquisition research to solve innovation problems because they are essentially the problem. We’ll post our case studies here.

 

References

Taleb, N.N. (2013) Antifragile: Things that Gain from Disorder. Penguin

Crandall, B. Klein, G. Hoffman, B. (2006) Working Minds: A Practitioner’s Guide to Cognitive Task Analysis. Bradford Books

Rugg, G. (2013) Blind Spot: Why We Fail to See the Solution Right in Front of Us: How Finding a Solution to One of the World’s Greatest Mysteries. Harperone

 

Why Research Decision Making?

It’s a common question when that is what you do. Researching decision making is fascinating, but you don’t do it just because you like the topic- although that helps. I research decision making because it yields so much valuable information about people, processes, cultures and organisations; and perhaps most interesting- it tells you a lot about innovation and insight.

Investigating the decision making process provides a window into how people, collectively and individually, make sense of common and unique situations. It provides data on how they reason, what do they notice, what do they ignore, how do they prioritise, what type of errors do they make, their problem solving strategies, how do they identify or explain away errors, and how do they utilise their environment and resources. And most significantly, how do people and organisations do all these things when plans go wrong.

This data gives you access to the most effective “shadow skills”, skills which don’t feature in a manual but are highly effective, the short cuts which out perform statistics, the errors which get explained away-bias, and cross links between disciplines which can be used in problem solving and innovation. It also provides data on how effective procedures, organisational structures and cultures are in dynamic, problem solving situations. Overall, it provides you with the deep structure of an individual or collective.

The results of decision making research are designed to take this product of deep structure and use it to drive insight- a new broader perspective, which kicks thinking and problem solving out of a spiral. We use it for all types of projects, but recently it’s having particular value in this direction -taking ideas which have reached a dead end, but have great commercial potential (an academic research project for example), and applying decision research to kick out this dead end and create a “product” of commercial value.

This is why it works in this example- academic research can get stuck in a spiral, an impasse of domain constraints, time and capacity. It’s easy to see commercial potential but there is a real gap between this potential and fulfilling the user requirements of the commercial world. The gap isn’t addressed by big ticket sales or constantly tweaking one pagers, it’s addressed by research which brings together the reasoning of the two domains. The structure of reasoning is identified in decision making research, the sense making, error types, problem solving etc. Stripping the commercial and academic domains down to structures allows the cross links to be more easily identified- it allows one domain to gain insight from the other in a way which solves a problem and adds genuine value.