Over the past decade the UK has gone through major changes that have touched almost every aspect of daily life. From the long shadow of the financial crisis to Brexit, the Covid-19 pandemic, and the more recent cost of living pressures, each event has shaped how people feel about their lives.

Wellbeing is more than just the absence of illness. It reflects how satisfied people are with their lives, how happy they feel, whether they believe what they do is worthwhile, and how often they experience anxiety. The Office for National Statistics collects this information every year from people all over the UK, providing a unique view into the emotional and mental health of the nation.

For this project I worked with ONS wellbeing data from 2011 to 2023. After cleaning and transforming the data, I built an interactive Power BI dashboard to explore how these measures have changed over time, where the highest and lowest scores are found, and how wellbeing is spread across different regions. The aim is to turn over a decade of numbers into a story about how life in the UK has been experienced and felt.

Dashboard Walkthrough

The wellbeing dashboard is designed to be simple enough to navigate at a glance but detailed enough to give meaningful insights. It’s divided into three main pages, each focusing on a different angle of the data.

The first page provides an overall picture of wellbeing across the UK. Here you’ll find key performance indicators (KPIs) like the highest and lowest scoring regions, the most improved areas over time, and the overall score change since the earliest year in the dataset. This page acts as a “big picture” summary so you can quickly see who is doing well and who might be struggling.

The second page focuses on trends over time. This is where you can compare different regions and wellbeing measures year by year, spotting patterns like gradual improvements or sudden drops. It’s especially useful for identifying whether certain regions are consistently improving or stuck at the same level.

The third page is all about score distribution. Instead of looking only at averages, this view spreads out the data to show how scores are distributed across regions and measures. It helps uncover whether differences between places are small and consistent or wide and uneven.

Each page has filters for year, measure of wellbeing, and region, allowing you to tailor the view to your specific interest.

Data Overview

The data for this project comes from the Office for National Statistics’ annual survey on personal wellbeing. Every year thousands of people across the UK are asked four key questions:

• How satisfied are you with your life?

• To what extent do you feel the things you do in your life are worthwhile?

• How happy did you feel yesterday?

• How anxious did you feel yesterday?

The answers are given on a scale from 0 to 10, where higher scores indicate greater satisfaction, happiness, or sense of worth, and lower anxiety. For reporting purposes, the ONS groups these responses into categories such as “very good”, “good”, “fair”, and “poor”.

The dataset covers the period from 2011–12 to 2022–23 and includes results for every local authority and region in the UK. It also includes additional details such as the statistical confidence ranges for each measure, which help in understanding the reliability of the scores.

While the raw dataset is large and complex, it provides a rich foundation for analysis, allowing us to examine changes over time, regional differences, and how each wellbeing measure compares to the others.

Data Source

The data for this analysis comes from the UK’s official wellbeing statistics, collected and published by the Office for National Statistics (ONS). These figures are based on large-scale surveys where people across the UK rate their wellbeing in four key areas: life satisfaction, feeling that what they do is worthwhile, happiness, and anxiety.

The dataset covers more than a decade, from 2011–12 to 2022–23, and includes results for different administrative areas across England, Wales, Scotland, and Northern Ireland. Each record contains a score, as well as the lower and upper confidence limits, which show the possible range of the estimate. The data also includes codes for each geography, making it easier to group areas by region or country.

You can access the original dataset on the Office for National Statistics website.

To support transparency, I’ve made both versions of the dataset publicly available:

• 📁 Raw Dataset (CSV)

• 📁 Cleaned Dataset (CSV)

Data Cleaning and Preparation

The original dataset was not ready for direct analysis in Power BI. It included multiple columns for confidence intervals, long text labels, and some formatting that made it tricky to work with right away. The first step was to simplify and structure it in a way that would allow clear, meaningful comparisons.

One challenge was that the “Year” column was formatted like “2011–12” instead of a standard numeric year, so it had to be cleaned into a consistent format for filtering and visualizing trends. Another key step was to standardize the geographic codes (such as those starting with E, W, S, and N) so we could add a “Region” column. This allowed grouping results into England, Wales, Scotland, and Northern Ireland, as well as breaking them down by local authority.

For the wellbeing measures themselves, the dataset provided both the raw average scores and the categorical breakdowns (e.g., “good”, “poor”). We kept both, as the averages allow for precise calculations while the categories are helpful for storytelling.

Finally, we removed duplicate or irrelevant entries and reshaped the data so that each row represented one wellbeing measure for a specific place and year. This tidy structure made it much easier to create the visuals in Power BI without having to do repetitive manual fixes.

Findings at a Glance

When you explore the wellbeing data over the last twelve years, certain stories start to take shape. On the whole, people’s sense of life satisfaction and feeling that what they do is worthwhile have held steady. There have been gentle rises and falls, but nothing dramatic in those measures. Anxiety is the one that wobbles more. It seems to respond quickly to bigger social and economic shifts.

Regional differences are clear, too. Some parts of England consistently report high levels of wellbeing, while places in the North East and parts of Wales often lag behind. These differences have persisted over the years, suggesting that local circumstances play a big role in how content or anxious people feel.

Not every area moves in the same direction. A handful of local authorities have seen their scores fall since the early years of this data. The drops are most noticeable in how happy and how satisfied people say they are with their lives. Those pockets of decline could point to new or growing challenges that deserve more attention.

If you look at how the scores are spread, you see there is more to the picture than the averages suggest. Even within high-scoring regions, there are places where people feel a lot less satisfied or happy than their neighbours. And in some areas that rank lower overall, there are communities with surprisingly strong scores. This spread shows that averages can mask real differences on the ground.

Finally, the way anxiety behaves stands apart. Lower anxiety is better, and it doesn’t always follow the same pattern as the positive measures. Regions that score well in life satisfaction aren’t always the ones with the least anxiety, which suggests that different factors are at work.

In short, the data paints a picture of a country where overall wellbeing has stayed fairly steady, but with real and persistent gaps between different regions and communities. It also reminds us that what helps people feel happy and fulfilled isn’t always the same as what helps them feel calm and free from anxiety.

How to Use the Dashboard

This dashboard has been built so you can explore the wellbeing data from different angles without getting lost in the numbers.

On the Summary page, you can see the big picture — the latest national averages, how they compare over time, and which regions stand out at the top or bottom. This page is great for a quick overview.

The Wellbeing Trends page lets you dive deeper into regional differences and track how scores have shifted since 2011–12. You can see which areas are improving, which are holding steady, and which are showing declines.

The Score Distribution page is where you explore how scores are spread out. It shows you whether the wellbeing in a region is mostly similar or if there’s a wide gap between its highest and lowest scoring areas.

You can use the slicers at the top of each page to filter the data by year, measure of wellbeing, and region. This means you can focus on just one aspect, like Happiness in 2020–21, or compare several measures side by side.

Tip: If you’re not sure where to start, set the filters to the most recent year and explore each measure one at a time — this often reveals the clearest patterns.

Caveats and Limitations

While this dashboard provides a clear view of wellbeing trends across the UK, it’s important to remember what the data can and cannot tell us.

First, the wellbeing scores are based on survey responses, which means they reflect how people feel rather than any objective measurement. This makes them valuable for understanding public sentiment, but it also means they can be influenced by short-term events, seasonal effects, or even the way questions are asked.

Second, the data is aggregated to local authority and regional levels. This is great for spotting patterns and comparing areas, but it also hides variation within those areas. A region with a “good” average score could still have communities facing serious challenges.

Third, not all changes in scores over time are necessarily linked to policy or social shifts. Sometimes differences can be due to sampling changes, differences in survey participation, or broader events like the pandemic that affect everyone in some way.

Lastly, although the dataset is quite comprehensive, it does not include every possible factor that influences wellbeing. Things like employment rates, housing quality, healthcare access, and social connections all play a role but are not directly measured here.

This dashboard should be seen as a starting point as a way to guide deeper conversations and more targeted analysis, not as the final word on wellbeing in the UK.

Conclusion

Working with this dataset taught me that even clean numbers hide messy stories. From a purely analytical view, it is satisfying to see clear lines of stability in life satisfaction and happiness over the past decade, modest upticks and declines, and obvious regional patterns. Yet it is also a bit jarring when those charts show certain places consistently lagging behind or sudden dips around 2020.

What struck me most as I dug into the data is how much variance there is beneath the surface. An average score can look fine, but when you break it down by local authority or measure, the picture changes completely. In some communities, people are becoming more anxious while happiness stays unchanged. In others, feelings of worthwhileness drop even as life satisfaction rises. This complexity is where the data comes alive, because it reflects how differently people experience their lives depending on where they live and what they value.

As an analyst, it is tempting to focus only on the metrics and on how a line trends up or down, or which region tops the ranking. But the real takeaway is that these numbers represent people. The consistent stability hides personal struggles and successes. The regional gaps hint at the influence of local economies, support networks and services. The patterns in anxiety remind us that feeling calm is not just about being happy or satisfied, it’s about deeper factors we cannot see in the data.

In short, this project reminded me that data analysis is not just about finding trends; it is about telling the stories behind them. And the biggest story here is that wellbeing in the UK is a mosaic of experiences. Some areas are thriving, others are stuck or declining, and many places show a mix of both. By paying attention to these nuances, we can better understand where help is needed and where positive practices are working well.

But before making any decisions, I needed answers to a few critical questions:

• Does the UK primarily import or export laptops?

• What are the trends over time — are imports growing or declining?

• Which countries are the UK’s key trade partners for laptops?

• And most importantly, is there an opportunity in the UK for someone like me?

To answer these, I turned to publicly available trade datasets, focusing on laptops..

The raw data, however, was far from analysis-ready. It spanned several years, included mixed trade flows (imports, exports, re-exports, re-imports, domestic exports), and had inconsistent values, especially in CIF and FOB fields. Cleaning and organizing it was a significant step before any real insight could be drawn.

Using Microsoft Power BI, I developed a comprehensive dashboard that visualizes UK laptop trade flows from 2019 to early 2025, comparing imports and exports, country-level partnerships, trade values, and seasonal patterns.

This blog post presents both the journey and the findings — beginning with data preparation and culminating in a visual dashboard that aims to answer the central question:

“UK Laptop Imports vs Exports (2019–2025): Is It a One-Way Trade?”

📊 Dashboard Walkthrough

The dashboard was designed in Power BI to highlight key patterns in the UK’s laptop trade between 2019 and 2025. It is structured from top to bottom for intuitive exploration — starting with filters, followed by trend visuals, KPIs, and finally detailed country-level insights.

Here’s a breakdown of the main components:

• 🔘 Slicers (Filters)

  • Year Selector — Filter visuals by specific year or view all at once.

  • Trade Type — Toggle between Import, Export, or both.

  • Country — Drill down into a specific partner nation.

• 📈 Line Chart: Import vs Export Trend (Yearly)

  • Shows annual movement of laptop quantities from 2019–2025.

  • Clear visual evidence of imports far outpacing exports.

  • 2021 marked a peak in overall trade, likely due to post-pandemic tech demand.

• 📊 Column Chart: Trade by Month (Across All Years)

  • Aggregated monthly trend reveals November as the busiest import month — likely driven by holiday and Black Friday sales.

  • Exports showed no strong seasonality, remaining relatively stable.

• 🌍 Top Trade Partners (Bar Chart)

  • Lists the top countries by quantity and value.

  • China leads UK imports by a large margin.

  • Export volumes are comparatively low and dispersed among multiple countries.

• 📌 KPI Tiles

  • Total Imports (Qty)

  • Total Exports (Qty)

  • Total Trade Value (USD)

  • KPIs are fully dynamic — they update based on slicer selections.

• 📋 Matrix Table: Country-wise Import/Export Comparison

  • Comprehensive table comparing:

    • Quantity

    • Trade Value

    • Unit Price

  • Grouped by Trade Type and Country.

  • Ideal for deeper analysis, spotting anomalies, or export opportunities.

💾 Dataset Overview

To explore UK laptop trade patterns, I sourced data from the United Nations Comtrade Database, using the Harmonized System (HS) code 847130. This code specifically represents:

"Portable digital automatic data processing machines, weighing not more than 10 kg, consisting of at least a central processing unit, a keyboard, and a display."

This includes most types of laptops and notebooks traded internationally.

The dataset covers a monthly breakdown from January 2019 to early 2025, giving a complete 6-year view of how trade activity has shifted over time — pre-pandemic, during the pandemic, and post-recovery periods.

📊 Key Columns Used

Here are the main fields extracted from the raw dataset:

To calculate the unit price per laptop, I derived a new field:

UnitPriceUSD = CIF or FOB value / Quantity

This allowed for a clearer comparison between countries and trade types over time.

📚 Data Source

The data used in this project comes from the official UN Comtrade Database, an authoritative global repository for trade statistics maintained by the United Nations.

We specifically queried records related to:

• Reporter: United Kingdom (UK)

• Commodity Code: 847130 – Portable digital automatic data processing machines (laptops)

• Time Period: January 2019 to March 2025

• Trade Flow: Import and Export (excluding re-imports and domestic exports)

🔗 Direct access to the UN Comtrade Query Portal

The dataset was downloaded in monthly batches and compiled into a unified format for analysis.

📂 Dataset Versions

To support transparency, I’ve made both versions of the dataset publicly available:

• 📁 Raw Dataset (CSV)

• 📁 Cleaned Dataset (CSV)

🧹 Data Cleaning Process

To prepare the dataset for meaningful analysis, the following steps were taken using Power BI's Power Query Editor:

• Removed trade types not relevant for international comparison:

  • ❌ Domestic Export

  • ❌ Re-Import

• Combined monthly trade data (2019 to 2025) into a single dataset using Append Queries.

• Dropped columns with no usable data:

• Added a new column to calculate unit price per laptop:

  • UnitPriceUSD = Trade Value (USD) / Quantity

  • Used cifvalue for imports and fobvalue for exports.

• Filtered out invalid or unhelpful rows:

  • Rows where qty = 0

  • Rows where cifvalue or fobvalue = 0

  • Aggregated entries like partnerDesc = "World"

• Renamed columns for clarity in Power BI visuals:

  • flowDesc → TradeType

  • partnerDesc → Country

The cleaned dataset is now structured, filtered, and optimized for KPI tracking, trend visualization, and partner-country comparisons.

🌟 Key Highlights from the Dashboard

Here are some of the most interesting insights that emerged from the interactive analysis:

• Consistent Trade Imbalance: The UK has consistently imported more laptops than it exported from 2019 to 2025, reinforcing the idea of a one-way trade.

• Peak Import Periods: Import volumes spiked in specific months, particularly during the pandemic and end-of-year seasons, possibly driven by increased remote work and holiday demand.

• Main Import Partners: The UK imported the majority of its laptops from countries like China, the Netherlands, and Vietnam.

• Export Surges: While smaller in volume, exports showed occasional surges to countries such as Neitherland, Ireland, and UAE.

• Unit Price Trends: Import unit prices were generally lower than export unit prices, hinting at potential re-export strategies or value-added exports.

🧠 Conclusion: Is It a One-Way Trade?

Based on the data from 2019 to 2025, the answer appears to be yes — at least for now.

The UK’s laptop trade is heavily skewed toward imports, with more than 125 million units imported over the period and only a fraction exported in comparison. China remains the dominant supplier, while UK exports are scattered, relatively low, and inconsistent.

There are a few possible reasons for this imbalance:

• The UK lacks large-scale laptop manufacturing facilities.

• It's a high-consumption, tech-forward market with strong import demand.

• Some exports may involve re-exports of refurbished or excess stock.

This trade pattern reflects the UK’s role as a technology consumer more than a producer — a dynamic worth noting for anyone considering entering this space, whether as an importer, supplier, or logistics player.

The interactive dashboard allows you to filter by year, country, trade type, and region — so feel free to explore the patterns that matter most to you.

⚠️ Limitations

While the dashboard provides rich insight, a few limitations should be kept in mind:

• No Brand or Model-Level Detail: The HS code represents all laptops collectively. We can’t distinguish Apple from HP or high-end from budget laptops.

• Missing or Zero Values: Some entries included 0 quantity or value. These were removed, but small inconsistencies may remain.

• CIF vs FOB Differences: Import values use CIF (cost + insurance + freight) and export values use FOB (free on board), which aren’t directly comparable without adjustment.

• Limited Forecasting or Predictive Modeling: This dashboard is descriptive, not predictive. Trends are historical — not projections.

Despite these, the dataset still offers valuable, real-world insight into the UK’s international laptop trade landscape.

Over the past ten years, the UK automobile market has seen significant change, particularly in response to growing concerns about sustainability, emissions, and government-sponsored EV subsidies. — how dominant are petrol cars today?

This dashboard examines the data reported by DVLA from 2014 - 2024 thoroughly with an emphasis on fuel type trends, vehicles declared SORN, Most popular models of vehicles over the years.

Dashboard Overview

The Power BI dashboard is structured to answer key questions like:

• Which fuel types dominate the UK roads?

• How has the trend changed over time?

• What is the most popular car model?

• What proportion of vehicles are SORN vs Licensed?

• How do fuel types contribute to this licensing split?

Key Highlights

1. Petrol Still Leads, But EV adoption is rising

In the default view , the data shows Petrol is leading but in trends you can see that EVs are increasing like crazy every year.

• Petrol vehicles remain the largest group, but their lead is shrinking.

• Electric and hybrid vehicles have grown significantly post-2020.

2. Most Popular Model in 2024: Ford FIESTA

The Ford Fiesta continues to be the most licensed model, closely followed by the Volkswagen Golf and Ford Focus.

This trend reflects strong demand for commercial and reliable mid-size vehicles in the UK market.

Moreover, if you check the most licensed model over the years from 2014 - 2024 in different categories you’ll find different models and companies. Like if we talk about petrol and diesel, in the UK Ford is the most dominant vehicle.But for Hybrid electric (Petrol) people rely on Toyota and Hybrid electric (Diesel) Mercedes and BMW have been seen being used by people.

3. SORN Rate: 11.25% of Vehicles

Across the full dataset, around 54 million vehicles are marked as SORN (Statutory Off Road Notification), representing:

• 11.25% of the total vehicle count

4. Fuel Type Trends Over Time

Between 2014 and 2024:

• Diesel vehicles grew until about 2018–2019, then started declining

• Electric vehicles began rising significantly post-2020

The overall fleet expanded until 2019 before plateauing

How to Use the Dashboard

You can filter by:

• Year (2014–2024)

• Fuel Type (Petrol, Diesel, EVs, Hybrids, etc.)

• Licence Status (Licensed or SORN)

Each visual updates dynamically to reflect the selected filters — offering a highly interactive view into the UK vehicle population.

Insights & Takeaways

• Petrol is still king, but the crown is slipping.

• Diesel’s popularity has peaked, and its decline may continue.

• Electric vehicles are on the rise, but still lag behind in raw numbers.

• SORN rates are useful for understanding vehicle usage trends — such as seasonal vehicles or fleet retirements.

Data Source & Tools

• Source: UK government car licensing statistics (processed in Excel)

• Tool Used: Power BI

• Visuals Included: Donut charts, bar graphs, time-series line graphs, and KPI cards

Data Credibility & Processing

**Data Source
**The dataset used in this analysis is sourced from the UK Government’s open data platform — specifically from the DVLA vehicle licensing statistics. It includes anonymized records of vehicles registered in the UK, their fuel type, licence status (Licensed or SORN), and model details, recorded yearly from 2014 to 2024.

**Cleaning & Preparation
**The raw dataset contained multiple tables across years. Key preparation steps:

• Merged yearly sheets into one master dataset

• Filtered only active (Licensed) and SORN vehicles

• Grouped fuel types (e.g., Plug-in hybrids, range-extended electric)

• Removed blank/null entries in model and fuel type columns

• Created custom fields such as:

  • Total Vehicle Count

  • Fuel Type Grouping

  • Most Popular Model by Year

  • % Share of Each Fuel Type

Limitations

• This dashboard reflects licensing counts, not vehicle usage (i.e., miles driven)

• SORN data does not indicate why a vehicle was taken off-road

• Model popularity is based on total active records, not new registrations

Future Improvements

• Add more granular date data (e.g. monthly trends)

• Include emission data or regional breakdowns

• Integrate EV charging infrastructure trends for added context

Conclusion

Petrol cars still hold the lead in the UK — but they are no longer the unchallenged champions. As the push for greener transport continues, electric and hybrid models are rising, with policies and infrastructure expected to accelerate the shift. This dashboard captures a pivotal moment in that transition.

Hello and welcome to DataTales — your go-to destination for transforming raw data into meaningful stories that inspire smarter decisions and real impact.

In today’s data-driven world, having numbers is just the beginning. The true power lies in telling stories with data — turning complex information into clear, compelling narratives that everyone can understand and act on.

At DataTales, I’ll share practical data storytelling tips, hands-on Power BI tutorials, and real-world data visualization techniques to help you make your data speak louder. Whether you’re a business owner, analyst, or data enthusiast, you’ll find insights and tools to bring your data to life.

Why Data Storytelling Matters

Stories help us connect with information on a deeper, human level. Instead of getting lost in spreadsheets or dashboards, imagine your data guiding you through a narrative — highlighting what matters most and revealing hidden insights.

🔍Turning Public Data into Powerful Stories

One of the most exciting things we’ll do at DataTales is explore publicly available datasets — from government reports and open data portals to trends in health, business, education, and beyond.

I’ll take this data and:

• Break it down visually

• Build interactive dashboards

• Highlight the hidden patterns and insights

• Turn it all into engaging, understandable stories

So whether you’re curious about your city’s crime trends, how public spending works, or which foods are healthiest — I’ll help you see the story behind the stats.

What You’ll Find Here

• Step-by-step guides to create stunning, actionable dashboards

• Best practices for clear and engaging data visualization

• Case studies and success stories from real data projects

• Tips on using Power BI, Tableau, and other data tools effectively

Let’s Build a Data-Literate World

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