We recently had a SAFe Lean Portfolio Management course at the Nerd Republic. One of the topics of the course is the realization of business hypotheses based on epics. This is based on the Build-Measure-Learn concept from Eric Ries’ book The Lean Startup. Ries describes how developments can be measured “ forward” using so-called Leading Indicators. In the courses, we always find that although the theory is easy to understand, practical examples initially seem too abstract. Fortunately, it was English week during the last course and soccer matches provide an ideal illustrative example.
Business hypotheses
First, a brief outline of business hypotheses. A key milestone for companies on their path to SAFe business agility is the shift from traditional projects to epics as business hypotheses. Traditional project planning is characterized by a previously agreed scope. The required resources and time are then derived from this. It is possible to change the scope after the project has started, but this costs additional resources and time. In traditional project management, lagging indicators are used to determine whether a project is successful. These lag behind the event (lagging) and are hard results. They are usually measured in terms of return on investment, turnover, profit or sales figures.
In the agile world, we accept that it is impossible to look into the future with certainty. We don’t think about projects, we think about products and work with hypotheses that have to be continuously validated or falsified. From the very beginning of our work, we say goodbye to a fixed scope. We deliver product increments in small iterations and decide how the product is to be further developed, taking into account the time factor. The decision-making process is guided by leading indicators, which measure forward as soft results (leading).
Leading Indicator
Organizations are used to working with lagging indicators when implementing new products via projects. Products are developed over a long period of time and their value is only assessed on the basis of hard facts once they have been launched on the market. Leading Indicators, on the other hand, focus on what needs to happen for us to generate satisfactory Lagging Indicators with valuable products.
Let’s take the example of an online store that specializes in the sale of keys. If the turnover in our online store is our lagging indicator, we can only measure this with a time delay to our actions. A number of decisions and processes have to take place beforehand so that the customer buys the product and we generate any sales at all. For example, we need traffic on our site, a conversion and leads in order to persuade the customer to make a purchase decision and thus generate sales. These are our leading indicators. If we hypothesize that “people want to buy coloured keys with an engraved name in the online store”, we should not invest two years to produce keys in 400 different colors. Instead, we should take an iterative approach. In other words, start with a small online store offering red, green and blue keys. As we gradually develop the store – and our product – we always keep an eye on the leading indicators and try to validate or falsify our further developments as early and continuously as possible.
This approach represents a challenge. It involves actively examining our business hypothesis. We ask for drivers of our hypothesis that we can measure and that allow us to draw logical conclusions about cause and effect.
And now to the soccer game…
So much for the brief theory of leading and lagging indicators. Now we come to the real problem. How do both types of indicators manifest themselves in the real world? Let’s take a look at this using the example of a soccer match.
In classic project management, we would say: “Put the better team on the field and you will win.” Although there are teams that have a knack for winning, this statement is not a 100% logical conclusion. The lagging indicator would be the final score. The team wins 2:0, in which case our “best team” project runs as planned. Unfortunately, soccer also operates in a complex environment and things usually turn out differently than planned (otherwise it wouldn’t be any fun).
The coach must therefore work with leading indicators during the game, as there is no logical sequence of “better team = win”. During the game, the coach looks at different metrics. They take a look at tackling behavior, number of passes played, number of fouls, shots on goal, etc. None of these indicators leads logically to a win. In other words, no team wins simply because it has played more passes or won more duels. Rather, it is a constant search for combinations of indicators (e.g. tackles, shots on goal and passes received) that allow the outcome of the game to be predicted as reliably as possible.
In the business world, the kick-off can be understood as the first release. Then it’s time to let it run. Of course, the leading indicators in the first five minutes of the game are not valid enough. What conclusions should you be able to draw from zero goals scored? It’s the same with epics. They need some time to take effect. We have to give the market time to react. Over the course of the first half, the metrics become more concentrated and the coach has the opportunity for a retrospective, also known as a half-time break, in which they can make specific adjustments to their team’s play. They have a set of leading indicators at their disposal for this purpose. This objective data is combined with subjective impressions of the coach (or business owner) and the next half-time (or iteration) is planned.
Do we go in with an extra striker? Do we change the system of play? Do we change a player or do we let the ball run a little longer after the pressing phase? These are all changes that we can introduce into the ongoing experiment. This is not an end in itself, we provoke changes in our leading indicators that lead us to victory.
As in soccer, it sometimes happens in the development process that the desired result is not achieved. We believe in a new feature and are convinced that it will increase traffic to our site. But nothing happens after the release. We don’t see anything in the indicators. That doesn’t necessarily mean that the feature is bad. Maybe we added it at the wrong time or maybe it’s placed in such a way that the customer can’t find it. Not every substitution makes the wild card goal. Often enough, the viewer wonders what this change has achieved.
After the game, the players answer the reporters’ questions. The classic question is always: “What did you plan to do after half-time?”. Depending on the outcome of the game, the answer is formed and, especially in the case of a defeat, the answer is “We had a lot in mind, but we couldn’t bring it onto the field”.
A soccer match is a complex environment, especially when our opponent simply doesn’t want to play the way we thought they would. This has a lot in common with our complex business environment. We have to take a hypothesis-driven approach in our environment and constantly plan and conduct micro-experiments. Relying solely on downstream indicators is no longer enough. In order to make good decisions quickly, it is important to identify the right set of leading indicators. This does not mean that we will win every game, but it does give us the ability to react to constantly changing basic assumptions.
Want to learn more about Leading and Lagging Indicators? Why not attend one of our SAFe LPM courses at the Nerd Academy. We will be happy to show you how hypothesis-driven products are developed in lean portfolio management using epics. How your soccer team, in my case Borussia Mönchengladbach, will continue to (or ever…) be successful, we will unfortunately not be able to clarify.