Developing great people: Using Bloom’s Taxonomy to support learning and development

At SME Water, we’re proud to support UK water companies with data products that help monitor, manage, and reduce leakage. But the more we work with operational teams, analysts, and strategic leaders, the more we realise that better systems alone aren’t enough. 

Leakage performance doesn’t improve just because you install a new dashboard or sensor. It improves when people understand the data, use it effectively, and make good decisions—consistently. That’s why training, learning, and capability-building are central to how we deliver value. And while we’ve built strong foundations in this area through the delivery of the Academy alongside our data products and we continue to support our staff with their learning and development, we know there is more we can do. 

In this blog, we explore a powerful framework—Bloom’s Taxonomy—that we believe could help us design smarter, more impactful training across roles. From new field technicians to seasoned analysts and future data scientists, we’ll show how Bloom’s could help shape learning that truly delivers results. 

Before we look at how it applies to our work, let’s start with the basics. 

Bloom’s Taxonomy is a framework created by educational psychologist Benjamin Bloom and colleagues in the 1950s. It was designed to help teachers structure learning in a way that progresses from basic knowledge to complex thinking. A revised version in 2001, led by Lorin Anderson and David Krathwohl, gave us the modern form we use today. 

Bloom’s Taxonomy is a widely recognised hierarchical framework used by educators to classify and structure educational objectives according to their complexity. The taxonomy outlines three domains of learning: 

• Cognitive – intellectual skills and knowledge 
• Affective – emotions, attitudes, values 
• Psychomotor – physical skills 

We’ll focus primarily on the cognitive and affective domains, which are most relevant for developing technical expertise and professional behaviours. 

The Six Levels of Bloom’s Taxonomy in the cognitive domain are: 

1. Remember – Recall basic facts and concepts. 
2. Understand – Explain ideas and principles. 
3. Apply – Use knowledge in real situations. 
4. Analyse – Break down information and identify patterns. 
5. Evaluate – Make judgments based on evidence. 
6. Create – Build new ideas, models, or solutions. 

Figure 1 – Bloom’s Taxonomy in the cognitive domain. 

Each level builds on the last. For example, someone must understand a concept before they can apply it, and they must be able to analyse before they can evaluate or create. 

This hierarchy allows learning designers to build clear, structured pathways from novice to expert, helping learners not just know more—but think better. 

At first glance, Bloom’s might sound like something for classrooms or universities. But it is just as relevant in our world—where people are constantly asked to: 

• Interpret complex data. 
• Diagnose operational issues. 
• Make evidence-based decisions. 
• Communicate and justify actions. 
• Develop new processes or models. 

These are all higher-order cognitive skills. Too often, training in our industry focuses only on how to use the tool, not how to think with it. That is where Bloom’s Taxonomy can help. It gives us a way to structure training content, exercises, and learning and development plans that go beyond surface-level knowledge. It also helps us differentiate learning for different roles—from frontline staff to data specialists to middle and strategic managers. 

Let’s take a closer look at how this framework could support the development of high-performing leakage analysts and data professionals. 

Leakage analysts don’t just look at charts and dashboards. They interpret data, spot anomalies, recommend actions, and evaluate success. As they progress and gain experience, they may also look for patterns, innovate processes, and drive strategic insights. 

Here’s how Bloom’s can help shape training for this journey:

1. Remember – Building a Knowledge Base 

At this stage, learners need to memorise and recall key concepts. For leakage analysts, this might include:

• Key terminology and important definitions: District metered areas (DMA), minimum night flow (MNF), average zone night pressure (AZNP) etc. 
• Names of key systems, datasets and KPIs 
• Basics of flow, pressure, and acoustic data

Training methods:

Flashcards, glossaries, annotated dashboards, knowledge quizzes

2. Understand – Grasping the ‘why’.

 Here, we help analysts explain and interpret what they’re seeing: 

• Why is the nightline important? 
• What do changes in minimum night flow suggest? 
• What’s the relationship between flow and pressure? 

Training methods:

Short explainer videos, annotated dashboards, concept mapping, written reflections 

3. Apply – Using knowledge in context

Analysts begin to use their knowledge to perform real-world tasks, such as: 

• Filtering data for unusual patterns 
• Setting thresholds in dashboards 
• Creating basic reports from DMA trends 

Training methods: 

Scenario-based tasks, guided case studies, sandbox dashboards

4. Analyse – Breaking It down

This is where analytical thinking begins. Analysts start to draw insights and compare variables, like: 

• Analysing DMA trends across time 
• Comparing performance between zones 
• Linking weather events to leakage spikes 

Training methods: 

Real-life case investigations, data comparison exercises, peer-led reviews 

5. Evaluate – Making judgments

At this level, learners must critically assess options, justify recommendations, and conduct cost benefit analysis: 

• Choosing between interventions and review existing processes. 
• Explaining the likely root cause of an issue 
• Assessing the effectiveness of recent actions 

Training methods: 

Root cause templates, evaluation matrices, benefits case studies 

6. Create – Innovating and leading

Finally, experienced analysts or data scientists can begin to build new things: 

• Designing custom KPIs or models 
• Prototyping and automation tools 
• Recommending strategic changes to awareness and detection processes 

Training methods: 

Innovation projects, hackathons, mentoring junior analysts. 

Bloom’s isn’t just for analysts. The same framework can guide development roles within the industry for other roles.  

Technicians need to understand how equipment works – Apply, troubleshoot issues in the field – Analyse, and then recommend best-fit solutions – Evaluate. 

Operational Managers need to assess performance across teams- Evaluate, improve escalation processes – Create. 

Strategic Leaders need to understand regulatory frameworks – Understand, prioritise investments – Evaluate, and design long-term strategies – Create. 

This ensures every role gets learning that reflects its responsibilities—not a one-size-fits-all training deck. 

While the cognitive domain of Bloom’s Taxonomy is focused on thinking and knowledge, the affective domain is about attitudes, values, motivation, and emotional response—all of which are critical for determining whether training is truly effective. 

The affective domain has five hierarchical levels (from lowest to highest):

1. Receiving – Willingness to listen or be aware 
2. Responding – Active participation or engagement 
3. Valuing – Attaching value to knowledge or behaviour. 
4. Organising – Integrating new values with existing ones 
5. Characterising – Acting consistently with new values as part of an identity or culture. 

Figure 2 – Bloom’s Taxonomy in the affective domain. 

Each level represents a deeper acceptance and internalisation of learning. For example, an analyst might “receive” a session on cyber security, but only when they “value” its importance do they begin to apply it consistently. 

We can evaluate how learners are progressing through this domain. The table below gives some examples of how we can observe and measure this progress: 

Figure 3 – Examples of ways to measure progress through the affective domain.

We can incorporate this into our learning aims and instead of just cognitive goals like: 

“Learner will be able to interpret minimum night flow trends…” 

We can also include affective aims, like: 

“Learner demonstrates appreciation for data integrity by consistently validating sources before making decisions.” 

We can also incorporate activities that reveal learners’ values and attitudes, such as:

• Including reflection prompts: “What surprised you in this training? What do you now see differently?” 
• Proposing discussion questions: “How would you manage this scenario if you were a team lead?” 
• Conduct value-based decision exercises such as a cost benefit analysis.

To truly gauge training effectiveness in the affective domain, we must look at what people do differently over time, such as: 

• Are analysts taking more initiative? 
• Are managers reinforcing a data-driven culture? 
• Are teams more collaborative and working with a clear idea and philosophy? 

These are signs that the training is moving beyond just skills and knowledge but rather into mindset and behavioural change. 

Water companies across the UK face growing pressure to reduce leakage, improve resilience, and meet tough regulatory targets. The challenges are technical, but they are also human. 

By investing in structured, progressive training—grounded in research like Bloom’s Taxonomy—we can help build the people who will lead the next generation of leakage innovation. 

Whether it’s an analyst interpreting a trend or a technician deciding where to dig, or a strategic manager deciding where to spend their ‘millions’ the thinking behind the action matters. 

At SME Water, we’re proud of the systems we build—but we know that the best outcomes come when systems are paired with smart, capable, and confident people. That’s why we will continue exploring using frameworks such as Bloom’s Taxonomy to help guide the development of our people and our clients—helping our customers develop the skills they need, at the depth they need, for the challenges ahead.