Learning vs. ExecutionLearning what a product should look like and do, and building solid, shippable software are different concerns. Separating the two aspects and distinguishing between learning and execution helps you manage the stakeholder expectations, select the right research and validation techniques, and choose the right sprint goals. When we start developing a new product or new features, there are usually more unkowns than knowns, more things we don’t know than we know: We may not be clear on the user interaction, the user interface design, the product’s functionality, or the architecture and technology required to build the product. Our greatest challenge is therefore to deal with the uncertainty present, and the associated risks. As a consequence, the early sprints should focus on creating the relevant knowledge, and addressing the key risks. Selecting a testable idea (hypothesis), and running an experiment are great ways to achieve the necessary learning. As testing ideas results not only in success but also in failure, you should expect to fail in the early sprints. Your Product Canvas or backlog, and your architecture are likely to see bigger changes driven by the newly gained insights. As you acquire more knowledge, your focus should gradually shift from resolving uncertainty towards execution: building a product that is ready for general release. Rather than primarily testing ideas, you should now start completing features and incrementally adding new ones. Failure can still happen at this stage, but it is usually a sign that something has gone fundamentally wrong. Similarly, your Product Canvas or backlog should have started to stabilise and exhibit less volatility. The change of focus may also impact your Definition of Done: Throwaway prototypes used to test ideas quickly don’t have to have the same quality as software that will be shipped. You can now start ramping up the project, add new teams, and consider employing distributed teams. The following picture visualises the relationship between learning and execution for the development of a new product or a new product version: To understand how much learning and experimentation is required, consider the amount of innovation present and the technologies used: A brand-new product usually requires more experimentation than a product update; and a web app developed with standard technologies is faster and easier to create than an embedded system or a mainframe application (assuming that some market research or problem validation has already taken place).
BenefitsUnderstanding the relationship between learning and execution has three main benefits: First, it allows you to set and manage stakeholder expectations. It helpful for the stakeholders to understand that the early product increments are likely to be throwaway prototypes, and that failure is to be expected in the first few sprints. Second, it helps you choose the right sprint goals, and focus the work of the development team: Your early sprints should acquire the relevant knowledge by carrying out experiments. You later sprints should build features and get the software ready for general release. Third, it makes it easier to select the right research and validation techniques. You may want to work with user tests and product demos in your early sprints, and with releasing software to selected users in the later ones. Innovation – creating a new product or new features – involves uncertainty and risk. To build the right product with the right features quickly, you should make a concentrated effort in your first few sprints to quickly address the key risks and acquire the relevant knowledge. Then shift your focus to completing features and adding new ones by creating software that can be released.
You can learn more about learning and execution in Scrum with the following: