Validation Practices

• Generative research
• Backlog refinement
• Mobilisation
• Lean experimentation
• Prototyping
• Data driven decision making (DDDM)
• Value based prioritisation

Generative research

❓What is it

• Generative research sometimes referred to as discovery or exploratory research, is defined as “a method of research that helps researchers develop a deeper understanding of users in order to find opportunities for solutions or innovation.” Contrary to evaluative research, which is about evaluating our design and our product by using usability testing methods, the goal of generative research is to generate information about end users and explore the problem space related to our product.
• You are expected to produce a research plan, execute the research and deliver a summary findings identifying the users, their needs and their pain points. This is typically summarised in a research findings report which then is the key input to inform the discovery outcomes.

🔑 Key Benefits / Why is this important

• This practice enhances existing understanding by gathering rich and detailed information about users' behaviours, attitudes, motivations, and preferences. It enables an equal focus across functional, emotional and social needs of a user, while quantitative methods, such as Google Analytics only surfaces functional needs.
• It directly supports a key principle of outcome based ways of working, by forcing the team to spend more time exploring the problem before rushing to think about potential solutions.

🛠 Techniques supporting this practice

• User interviews
• Usability testing
• Empathy mapping
• Task oriented research

Backlog refinement

❓What is it

• Ensures there is enough work in the backlog that is defined, understood and estimated to meet the definition of ready for the team to accept it as a candidate in an upcoming sprint.
• Refining the backlog is lead by the product manager. They identify the highest priority candidates to take forward in the next one or two iterations.
• How many items should be fully refined and ready is usually agreed within the team, recognising there is a balance to maintain between developer effort expended in refinement and delivering the current sprint goal.
• As part of refinement, product managers should regularly review the whole backlog to identify the most valuable outcomes to work on. This on going activity should also see the product manager close backlog items that are no longer relevant
• For items sitting near the top of the prioritised backlog, each item is refined by completing these activities: defining the user story including acceptance criteria, collaborative discussion with developers likely to be involved with the user story to understand its value and requirements, estimation, splitting the story if deemed too large.

🔑 Key Benefits / Why is this important

• Helps ensure that the team understands the backlog items and is ready to start work on them in the next sprint planning meeting. It leads to more efficient sprint planning sessions and helps the team maintain a steady, sustainable pace of work

🛠 Techniques supporting this practice

• Cost of delay prioritisation
• Effort / Value 2*2 matrix prioritisation
• 3Cs
• Planning poker
• User story splitting
• BDD
• User story writing

Mobilisation

❓What is it

• Mobilisation is the process of assembling and preparing a team, it involves selecting the right members, defining roles, and ensuring they have the tools and knowledge to succeed from the outset.
• Team mobilisation is like setting sail; with the right crew, clear roles, and shared compass, the journey ahead becomes a collaborative adventure
• From the scope of the value proposition and identified solution options the skillsets can be identified for the cross functional team
• Before validation commences assemble the resources to establish a self empowered team and run a series of kick off events to agree the team mission and the team charter.

🔑 Key Benefits / Why is this important

• Clear role definition for all the core team members
• Optimal team composition through aligning the outcome roadmap and solution approach with required capabilities
• Establishing transparency and trust of how the team will work enables the team to create early momentum.
• Enhanced collaboration through creating a shared team charter

🛠 Techniques supporting this practice

• Team charter

Lean experimentation

❓What is it

• Lean experimentation is a practice rooted in the Lean Startup methodology, popularized by Eric Ries. The approach focuses on the idea of rapidly validating key assumptions and solution ideas through a process of building, measuring, and learning. The primary goal is to minimize the amount of time and resources spent on developing features or products that may not meet the needs of the target users.
• The key characteristics to adhere to are:
• Hypothesis-Driven: Lean experimentation starts with the formulation of hypotheses about customer needs and how a product or feature will meet them.
• Minimum Viable Product (MVP): The most basic version of the product that allows the team to test their hypotheses with the least effort.
• Iterative Testing: Short, rapid cycles of experimentation allow for continual validation or invalidation of hypotheses.
• Metrics Focused: Actionable metrics are identified beforehand and can be captured accurately to gauge the experiment’s success.
• Adaptation: After the experiment, the data is analysed to take key learnings from the experiment and adapt the product backlog and associated hypotheses accordingly to prioritise the next most valuable experiment.

🔑 Key Benefits / Why is this important

• Early validation of value
• Small bets to test key assumptions
• Fast feedback on user needs

🛠 Techniques supporting this practice

• Hypothesis creation
• Test and Learn
• Lo-fi prototyping
• A/B testing
• Card sorting
• Data Analysis

Prototyping

❓What is it

• Prototyping serves as a tangible or interactive representation of one or more solution options from the opportunities generated during the discovery phase. The main purpose of prototyping is to visualize how the final product will function and to test this with users. Prototyping has greatly diminished value when not tested with end users. The number of tests recommended falls in line with qualitative research numbers.
• Prototyping minimises the effort expended to validate satisfying the user needs before creating detailed UX designs and associated technical solution designs, and is a key aspect of both lean experimentation and design thinking methods.
• Enhancing a lo-fi prototype with a prototype specification helps the team and stakeholders to self-answer a number of inherent curiosities, e.g. “Under what conditions does a page element show as active or inactive?”. It removes uncertainty about how the design works and improves communication efficiency.
• Prototyping covers a range of fidelity, from paper prototypes to clickable wireframes. The approach taken is typically influenced by the product’s development stage.

🔑 Key Benefits / Why is this important

• Have a solid foundation from which to ideate towards improvements
• Direct interaction with a prototype allows users to provide immediate feedback, which is invaluable for product improvement
• Since prototypes are often low-fidelity, the cost of failure is low. This encourages experimentation and innovation at pace
• Improve time-to-market by minimizing the number of errors to correct before product release. Finding issues in UAT is far more expensive and time consuming to fix.

🛠 Techniques supporting this practice

• Lo-fi prototyping
• Test and Learn cards

Data driven decision making (DDDM)

❓What is it

• Decisions are made based on the analysis and interpretation of data rather than intuition or observation alone. This product practice capitalises on the increasing volume, variety, and velocity of data available to organisations today.
• One of the challenges in applying this practice is the foundational capability to capture and measure the right metrics accurately.
• The five activities that make up this practice are:
• Data Collection
• Data Analysis
• Data Interpretation
• Visualisation
• Decision Making

🔑 Key Benefits / Why is this important

• These insights lead to more informed choices, rather than relying on gut feeling or anecdotal evidence.
• This is particularly beneficial in progressing conversations with stakeholders holding contrasting viewpoints or objectives.
• Presenting an evidenced based recommendation removes any emotional or subjective aspects to the discussion.
• Data provides a metric to measure success, failure, and progress. It gives teams the ability to set benchmarks and evaluate performance against set goals, promoting a culture of empowerment, transparency and accountability.

🛠 Techniques supporting this practice

Value based prioritisation

❓What is it

• Ranking value can be highly subjective and biased. Without understanding and defining value, any prioritisation exercise will create a false sense of confidence.
• Reference the difference types of value, and identify which are more important than others. The value types are: financial, reputation, user satisfaction, growth and operational integrity. Reference known OKRs for the service or directorate to help assess the relative importance of each of these value types in conjunction with the evidenced needs of the end user. Once drafted, these should be discussed and agreed with key stakeholders involved with the service and product, and then baselined.
• In assessing user needs, there are a couple of simple

🔑 Key Benefits / Why is this important

• Optimises delivery of value as early as possible
• Creates a high degree of confidence and purpose within the team that they are working on the most valuable outcomes
• Transparent alignment

🛠 Techniques supporting this practice

• Value definition (complementary practice)
• Effort/Value 2*2 matrix prioritisation
• Cost of Delay prioritisation