How to Explain Technical Data Concepts to Non‑Technical Stakeholders
// Master proven techniques for turning complex data insights into clear, business‑focused stories that win over non‑technical stakeholders in the UK.
Introduction
Data analysts spend most of their time wrangling, modelling and visualising data.
Yet the true value of that work is realised only when the insights are understood by decision‑makers who may have little technical background. Bridging this gap is a core skill for every data professional in the UK – from NHS analysts presenting patient‑outcome trends to city‑councillors reviewing housing data.
This article outlines a step‑by‑step framework, backed by recent UK data‑literacy research, that helps you translate technical concepts into compelling, business‑focused narratives. Whether you are preparing a slide deck for the board, writing a briefing for a charity, or running a workshop for senior managers, the techniques below will make your message clear, credible and actionable.
1. Know Your Audience – the Foundation of Every Explanation
| Audience type | Typical concerns | What they need to hear |
|---|---|---|
| Executive board | ROI, risk, strategic alignment | How the insight drives profit, cost‑saving or regulatory compliance |
| Mid‑level managers | Operational impact, team performance | Practical recommendations and next steps |
| External partners / NGOs | Transparency, public benefit | Evidence of social impact and ethical data use |
| Cross‑functional peers | Integration with other systems | Technical compatibility, data quality, timelines |
Action steps
- Create a stakeholder matrix – list each stakeholder, their role, technical familiarity (low/medium/high) and key business goals.
- Conduct a quick pre‑meeting survey – a three‑question Google Form can reveal what terminology they already know and what they find confusing.
- Tailor the depth of detail – for a low‑tech audience, keep the focus on what the insight means, not how it was derived.
UK Insight: The Office for National Statistics’ 2023 stakeholder toolkit found that 68 % of the public feel more comfortable sharing data when they understand why it is needed, not how it is processed. Align your message with the “why”.
2. Use Storytelling and Real‑World Analogies
Numbers alone rarely persuade. Embedding them in a narrative creates emotional resonance and memory retention.
The Story Arc for Data Presentations
- Context (the “once upon a time”) – Briefly describe the business problem.
- Conflict (the “challenge”) – Show the data gap or pain point.
- Resolution (the “solution”) – Present your insight and the recommended action.
- Future (the “happily ever after”) – Quantify the expected outcome.
Analogy Examples
| Technical Concept | Analogy (UK‑friendly) |
|---|---|
| Data pipeline | A London underground line: stations are raw data sources, trains are processing jobs, and the final stop is the dashboard. |
| Model over‑fitting | A student who memorises past exam papers but can’t answer new questions – the model knows the training data too well but fails on fresh data. |
| Confidence interval | A weather forecast that says “there’s a 70 % chance of rain” – it’s a range of likely outcomes, not a guarantee. |
Tip: Keep analogies short, avoid mixed metaphors, and test them on a colleague who isn’t technical to ensure they land.
3. Visualise – Let the Picture Do the Talking
Research on the picture superiority effect shows that people retain visual information up to 42 % better than text alone. For data concepts, well‑designed visuals are indispensable.
Visual Types and When to Use Them
| Visual | Best for | Design tip |
|---|---|---|
| Flow diagram | Process or data pipeline | Use colour to highlight where data enters and exits. |
| Bar chart with annotations | Comparisons over time or categories | Add a call‑out box for the key takeaway. |
| Heat map | Correlation or density | Keep the colour palette colour‑blind friendly (e.g., blue‑orange). |
| Infographic | High‑level story for executives | Limit to three core messages; avoid small text. |
Tools Popular with UK Data Professionals
- Lucidchart – collaborative diagramming, easy to embed in PowerPoint.
- Tableau Public – interactive dashboards that can be shared via a secure link.
- Power BI – integrates with Microsoft Teams for live stakeholder Q&A.
When creating a visual, ask yourself:
- What is the single insight I want the viewer to take away?
- Is the visual cluttered with unnecessary axes, gridlines or data points?
- Does the caption use plain language, not jargon?
4. Focus on Business Impact, Not Technical Detail
Stakeholders care about outcomes. Translate technical metrics into business language.
| Technical metric | Business‑focused translation |
|---|---|
| Model accuracy 92 % | “If we adopt this model, we can expect a £1.2 m reduction in fraud losses each year.” |
| p‑value = 0.03 | “There is a 97 % chance the observed uplift is not due to random variation.” |
| Data latency 3 seconds | “Your dashboard will refresh fast enough to support real‑time decision making during peak trading hours.” |
Quantify wherever possible – use UK‑specific figures (e.g., “the average cost of a data breach in the UK is £4.5 m according to the 2024 NCSC report”) to give weight to your recommendation.
5. Cut the Jargon – Speak in Plain English
| Jargon | Plain English alternative |
|---|---|
| ETL | “Extracting data from source systems, cleaning it, and loading it into our analysis database.” |
| KPIs | “Key performance measures that show how well we are achieving our goals.” |
| API | “A way for different software programmes to talk to each other automatically.” |
| Scalability | “The ability to handle more data or users without slowing down.” |
Practical tip: Keep a glossary slide at the end of your deck. If you must use an acronym, define it the first time (e.g., “Customer Lifetime Value (CLV)”).
6. Encourage Interaction and Questions
A one‑way presentation often leaves hidden misunderstandings. Turn the session into a dialogue.
- Polls: Use Mentimeter or Slido to ask “How confident are you with the current forecasting method?” – results guide where you need to elaborate.
- Live demo: Show a short, interactive Power BI slice and let a stakeholder explore a filter.
- “What‑if” exercises: Pose a scenario (“What if sales drop 10 % next quarter?”) and walk through the model’s prediction.
Create a safe space: explicitly state “There are no dumb questions – every clarification helps us make better decisions.”
7. Leverage UK Data‑Literacy Statistics to Build Credibility
Citing authoritative sources demonstrates that your communication approach is evidence‑based.
- Data Skills Gap: The UK Government’s 2023 report highlighted that 45 % of organisations struggle to translate analytical findings for non‑technical audiences.
- Public Trust: The ONS toolkit (2024) reports that trust rises by 23 % when stakeholders are shown clear, real‑world examples of data impact.
- Economic Impact: According to the British Business Bank, firms that embed data‑driven decision making see a 12 % increase in productivity on average.
Use these figures to justify the time spent on clear communication: “Investing in a concise briefing now can save us weeks of mis‑aligned implementation later.”
8. Practical Toolkit for the Data Analyst
| Resource | How to use it |
|---|---|
| Stakeholder Matrix Template (Excel) | Map audience, knowledge level, and key messages. |
| Storyboarding Canvas (Miro) | Plot the narrative arc before building slides. |
| Visual Design Checklist (PDF) | Verify colour contrast, font size, and label clarity. |
| Glossary Sheet (Google Docs) | Share with the team and update after each project. |
| Feedback Form (Typeform) | Capture post‑presentation understanding scores (1‑5). |
Keeping a reusable toolkit reduces preparation time and ensures consistency across projects.
9. Practice, Iterate, and Measure Success
- Rehearse with a peer who represents a non‑technical perspective.
- Record the session and review the pacing, jargon usage, and visual clarity.
- Collect feedback – ask stakeholders to rate “clarity of insight” and “actionability”.
- Adjust future presentations based on the scores; aim for a minimum 4/5 average on both metrics.
Continuous improvement turns a one‑off explanation into a core organisational capability.
Conclusion
Explaining technical data concepts to non‑technical stakeholders is less about simplifying the data and more about translating it into the language of business impact, storytelling, and visual clarity. By:
- understanding your audience,
- weaving a concise narrative,
- using analogies and visuals,
- focusing on outcomes,
- stripping away jargon,
- fostering interaction, and
- grounding your message in UK‑specific data‑literacy research,
you will not only convey insights more effectively but also build trust, accelerate decision‑making, and demonstrate the strategic value of data analytics across the organisation.
Remember: the ultimate goal is not to teach data science, but to empower decision‑makers with the confidence to act on your findings. With the framework and tools above, you’re ready to make every data story count.