Is the Scrum Master best placed to be the meta-knowledge champion?

Business Psychology is evidence-based. In other works ‘good’ science. We move away from ‘fads and fashions’ and have an open-minded curiosity about what works; and carefully examine how it works and in what settings- using the scientific paradigm or range of methods to these ends.

So, with that in mind, this week a pod on the British Psychological Society (BPS) website caught my interest as it links to my previous blog, from last week, around moving teams from ‘good to great’. The BPS presenter, Christian Jarrett, does a genuinely fab job of bringing these insights to life.

Firstly, he interviews Dr Julija Mell (from the Essec Business School), and she says that meta-knowledge is “knowing who knows what in the team”. Of course, this does not have to be a Scrum Master although that type of role is quite well placed. You will be reminded that in most IT teams Scrum Masters do not have any line management responsibilities as they are by definition, a Coach. Being a really good Coach is an excellent example of a meta-knowledge champion. You ought to have the time, skills and knowledge to really get a sense of the breadth and depth of each individual. A decent Coach should get to know the team members experience, knowledge, training, interests and strengths. In this way, the evidence is encouraging that you significant leverage to underpin higher degrees of collaboration,   cooperation and team performance. Good news indeed!

But there’s an implicit point in here: and this is having the time, and Coaching training. Of course other roles might have those skills (e.g. a technical lead or a project manager) but this assumption should be tested!

This review of this applied research is fascinating. What we learn is that helping the team members to get to know what they each bring to the team (and I would advocate both technically and as team players too by using a strengths based approach) the team start to broaden their cognitive or knowledge ‘map’ of the total team. In psychological terms their decision-making and problem solving space enriches.

Christian Jarrett then turns his attention to the ‘extra miler’ or the team player that goes beyond the ‘call of duty’ for the team’s objectives. To explore this point he interviews Dr Alex Fradera who shares research that the ‘extra miler’ has a massive  influence (or what we might say a disproportionate positive impact) on the team. This is because they are influencing the team in a significant way; and the good news is that this seems to be a strength that can be carefully (i.e. psychologically) identifiable. And of course, with peer award and reward systems this distinct attribute should be one that is rightly celebrated.

However, there is also a counter-intuitive point too. (I love counter intuitive points as they underscore even more importantly why Psychology is a social science and not simply the latest book at the Airport!); and this is around distribution of star players or the ‘extra milers’. The evidence is that it is best not to have them all in one team; but rather distribute them across all your teams- such is the positive impact they can have. This is akin to the Pareto 80:20 Law. Why? The researchers suggest this is because they are in effect role modelling to the team. In this way, we have the right behaviours as well as the right outcomes. When we have performance appraisal systems set-up that address (perhaps even equally) behaviours and outcomes; this can be an important point. Each team would benefit from both a decent Scrum master or meta-knowledge Champion as well as an ‘extra miler’ for the reasons detailed above.

Lastly, Christian Jarrett then turns his attention to the physical space for the team to work and collaborate within. He asks Dr Katherine Greenaway (University of Queensland) for her advice. She gently warns against a 1920’s ‘lean stark minimalist’ approach as the research is that these are much less effective. Dr Greenaway shares how the relative meaning of the space is important. This team meaning enhances team outcomes such as creativity, productivity and sharing information. Her basic advice is to ask teams to decorate the space to make it ‘more like home’; to have an input into it; and to make it team-centric rather than the ‘bland, white, and corporate look that reminds us of Apple’.

This again is a fascinating evidence-based point.. It underscores the importance of space. It also means that problem-solving space for daily and weekly Stand-Ups should all be co-created by the team themselves in terms of colour, styles and memorabilia that they jointly contribute together. This is another important point; and one that might run counter to current Corporate trends and fashions?

So, in summary, the evidence is that moving from ‘good to great’ teams you would be do well to carefully consider these key three points:-

  1. A Scrum Master or Team Coach that is the meta-knowledge Champion across the various professions with the skills, time and training detailed
  2. Ensure that each team has an ‘extra-miler’
  3. Give permissions for the Team to co-create their own team space that is meaning for them

Take care Jason

NB:- Podcast Episode credits: Presented and produced by Dr Christian Jarrett. Mixing and editing Jeff Knowler. Vox pops Ella Rhodes. PsychCrunch theme music Catherine Loveday and Jeff Knowler. Additional music Zander Sehkri/Zeroday Productions (via Pond5). Art work Tim Grimshaw.

Data-driven estimation the power of the Monte Carlo Simulation

Nearly thirty years ago I worked in the Construction industry and one of my roles was to create estimates for how long a future novel piece of work would take in time/effort; resources and then attribute a risk profile and lastly the margin of return known as gross profit. At that point we have something like ten different teams. I say teams. We called them gangs. Whilst some of the gangs were relatively stable others were not given the fluid nature of the jobs in the pipeline team members needed to move for short periods of time. I would often seek to bring gangs together in response to the size, complexity and milestone (payment dates) as back then much of the work we did was earned value given key milestones.

How did we grow a successful business? There were many key factors that made the family business distinctive: branding; attitude to safety, strong leadership; performance-related pay and bonus schemes; highest standards of training; professional accreditation and many more. But for getting our estimates right? Monte Carlo was key. We were a deeply data-driven and data intelligent business.

Stated simply, the Monte Carlo simulation furnishes the decision-maker with a range of possible outcomes and the probabilities they will occur for any choice of action.. It shows the extreme possibilities—the outcomes of going for broke and for the most conservative decision—along with all possible consequences for middle-of-the-road decisions.

Stated briefly, Monte Carlo simulation provides a number of advantages over deterministic, or “single-point estimate” analysis including:

  • Probabilistic Results. Results show not only what could happen, but how likely each outcome is.
  • Graphical Results. Because of the data a Monte Carlo simulation generates, it’s easy to create graphs of different outcomes and their chances of occurrence.  This is important for communicating findings to other stakeholders.
  • Sensitivity Analysis. With just a few cases, deterministic analysis makes it difficult to see which variables impact the outcome the most.  In Monte Carlo simulation, it’s easy to see which inputs had the biggest effect on bottom-line results.
  • Scenario Analysis: see exactly which inputs had which values together when certain outcomes occurred.  This is invaluable for pursuing further analysis.
  • Correlation of Input model interdependent relationships between input variables.  It’s important for accuracy to represent how, in reality, when some factors goes up, others go up or down accordingly.

Of course, you do need a decent understanding of statistics. To this day I am still very grateful for my practical training back then, as well as completing advanced statistics such as multiple regression, analysis of variance and mixed analysis of variance when I completed my MSc in Organisational Psychology at Cardiff University.

One of the most exciting prospects of the current online Kanban tool is that we are starting a journey of moving from guess-estimates (data neutral based on faulty human reasoning and bias) to a data-driven science of estimation. And delight of delights? This tool has an in-build Monte Carlo simulation! Yes…dreams sometimes can come true…

Have a good weekend,

Jason