Prateek Singh
Head of Learning and Development, ProKanban.org
Fort Lauderdale, Florida, United States
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Prateek Singh has been leading and working on agile teams for two decades. Starting with XP, then Scrum and now working in a Kanban system, Prateek has gained a breadth and depth of knowledge regarding agile techniques, practices and implementation principles. Prateek has been training and coaching for teams regarding Kanban and Lean principles since 2014. Prateek has played the role of Software Engineer, Scrum Master, Manager of Software Engineering. Director of Engineering and Principal Agile Coach in his career. Prateek is currently and independent consultant as well as Head of Learning and Development at ProKanban.org.
Area of Expertise
Topics
Probabilistic Prediction : When Will It Be Done?
The very first question a customer asks us when we start work is - When will it be done? Traditional methods of answering this question are fraught with errors. The most common errors include heavy reliance on estimates and use of averages to give one deterministic answer. We are all aware that our world is not deterministic and each prediction has a probability of being right and a complementary probability of being wrong. In this session, we will use examples and a simple exercise to demonstrate a much easier method which can help make probabilistic predictions. These predictions can help teams have more informed conversations with their customers about their probability of completing a project on time and around the risk profiles of their projects. The audience will learn how with very little estimation and simple measurements they can better inform and equip teams, managers and customers with information about possible completion dates of the project. We will show how these techniques are actively being used to predict the completion of single items and a set of multiple items in the real world.
Lies, Damned Lies and Statistics
What was your GPA in college? Does the media have a liberal bias? How many successful features have you shipped in the past year? How can we be sure that we are getting the right answers to these questions? We all lie, or at-least don’t tell the full story in our everyday lives, especially when no one is looking. We all fall prey to “Social Desirability Bias”, where people adapt their answers, because they want to look more desirable to others, than they really are. What does that say about the usual way we get feedback? Surveys, user interviews, estimates? Come find out how Limiting WIP, working in small batches, optimizing for feedback and some decent data analysis can help us separate truth from lies.
Basketball, Baseball, Football and Agile Teams
Imagine you had a team of the best basketball players of the early 21st century. Lebron James, Dwayne Wade, Carmello Anthony, Tim Duncan, Amar’e Stoudemire and Allen Iverson amongst others. How do you think this team would perform in a tournament? There is a general assumption that if you want to get the best performance out of a team you have to hire the best talent available on to that team. We will look at research that shows that in many conditions this can be demonstrably false. We will explore the differences between individual-focused(Baseball) and team-focused(Basketball) activities and the difference individual talent makes. How do we use the lessons we learn from these sports to build better teams? How do these results translate at scale? We will also explore the definition of the label – Superstar. We will look at what Superstar means in terms of American Football and in terms of software teams. We will then talk about how we can redefine a superstar from a team perspective. Come join us as we draw parallels between on-field and in-office teams.
Coaching Wilt Chamberlain: Behavioural Economics and a Learning Culture
If you had two options, one that gave you an 88% chance of success and another that gave you about 50%, which one would you choose? Would you be surprised to know that teams and individuals routinely make choices that only give them a 50% probability of success? Are you trying to coach teams to use better decision-making models and watching them stick temporarily before they revert to old behaviours? In this talk, we will discuss why some coaching lessons and innovative approaches take hold while others don’t. We will explore lessons we can learn from the research in the field of Behavioral Economics. We will draw parallels with sports including Basketball and High-Jump to dig into reasons some proven practices are abandoned for inferior alternatives while others catch on. We will also look at real-life example of a failing Agile Transformation that was turned around by leveraging lessons from behavioural economics. We will also discuss how the lessons from these examples can be used to enhance coaching practices and create a culture of learning.
Don't be a Ditka
How good are you at estimating value?
Most teams make suboptimal prioritization/value decisions every day because they are forced to make those decisions under:
• Conditions of scarcity (not enough time, money, or people)
• Conditions of stress (customers want their requests handled right now and delivered yesterday)
• Conditions of uncertainty (imperfect information about their current state and future state)
These poor decisions adversely affect their ability to effectively, efficiently, and predictably deliver value to their customers. While teams will not be able to change these conditions, they can learn to make better decisions by embracing them.
We will explore these topics using the 1999 NFL draft as a backdrop. In that year, Mike Ditka famously was overconfident in his ability to estimate player value and disastrously bet the farm on one pick. We will explore the options Mike Ditka had (and every team has) in the NFL draft as well as prioritization options we have on a daily basis. We will expose some flaws in the commonly used prioritization methods (By Value, CD3, WSJF etc.). We will also propose a mode of working that helps with effective prioritization under conditions of scarcity, stress and uncertainty.
Inept and Inapt: Agile Teams Misbehaving Badly
Most Agile processes (Scrum, Kanban, etc.) are built upon the notion of empiricism. According to the Scrum Guide, “Empiricism asserts that knowledge comes from experience and making decisions based on what is known”. The implicit assumption here is that Agile teams are comprised of rational actors who will gather the right information, and--when presented with that right information--will always make the correct process decisions around how to perform/improve. However, we know that in reality Agile teams are comprised of humans--each of whom bring their own biases, are guided by their own beliefs, and otherwise behave “irrationally” from an empirical point of view. In this session, we will discuss how this irrational behaviour actually makes teams very predictable in the mistakes they will make and what you can do to nudge your team from inept and inapt towards inspect and adapt.
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