Mini-project examples

Improving Engagement with Evidence-Based Actions

Situation: Engagement levels in a support programme were lower than expected.

Task: Analyse operational and CRM data to understand where drop-offs occurred.

Action: Cleaned engagement data, identified participation trends, and created clear summaries for the team.

Result: Informed targeted actions that increased engagement by 20% within one quarter.

Using CRM Data to Improve Service Quality

Situation: Service performance needed better monitoring.

Task: Use CRM and feedback data to identify patterns.

Action: Tracked service metrics, flagged quality issues, and communicated insights to stakeholders.

Result: Supported service improvements and strengthened data-driven decision-making.

Using an appropriate chart to show sales by date

Situation: I analysed time-series sales data across multiple products to report performance trends to stakeholders.

Task: My task was to clean, analyse, and visualise the data in a way that clearly showed sales changes over time and allowed easy product comparison.

Action: Using advanced Excel skills, I transformed and structured the data with Power Query, then created a line chart with dates on the x-axis and sales metrics on the y-axis, using separate series for each product to highlight trends and variability.

Result: This enabled clear trend analysis, helped identify top-performing products and demand patterns, and supported data-driven decisions around inventory planning and promotions.

Trends & patterns I can see in the data

Situation: I was reviewing time-series sales data for multiple products to understand performance patterns over a given period.

Task: My task was to identify key trends, variations, and comparisons in the data and clearly explain what they indicated.

Action: Using advanced Excel and Power Query, I cleaned and structured the dataset, then analysed the line chart to compare sales trends across products, daily fluctuations, and overall movement over time.

Result: I identified that one product consistently outperformed the others, showing clear peaks and troughs with an overall upward trend, while the remaining products had lower but more stable sales. These insights highlighted demand cycles and supported data-driven decisions around forecasting, inventory, and promotions.

Using Insights to support business decisions

Situation: The business needed to use sales data to improve planning and decision-making across products.

Task: My task was to analyse sales trends and translate the insights into actions that could support commercial decisions.

Action: I prepared and analysed time-series sales data, identifying top-performing products, demand fluctuations, and recurring peak periods through trend analysis and comparison.

Result: These insights supported data-driven decisions such as optimising inventory levels, scheduling promotions during peak demand, allocating resources more effectively, and improving sales forecasting to maximise revenue.

SQL to Analyse Sales & Customer Trends

Situation: I was asked to analyse daily sales and customer trends across multiple products using data stored in a SQL database, alongside CSV and Excel files, to provide clearer insight into performance and customer behaviour.

Task: My goal was to extract, structure, and analyse the data to identify meaningful sales patterns and support decision-making.

Action: I wrote SQL queries using JOINs to combine sales, product, and customer tables, and applied aggregate functions such as SUM, COUNT, and AVG to calculate total sales, order frequency, and average spend. I used GROUP BY to segment results by product, date, and customer group, which helped identify repeat buyers and compare product performance. I then imported the refined dataset into Excel, merged additional files using common keys, and created a line chart with a trendline to visualise sales performance over time.

Result: I developed a dynamic dashboard that highlighted an upward trend in the top-performing product and identified high-value repeat customers. These insights supported better inventory planning, targeted promotions, and more informed, data-driven business decisions.

Designing Clear & Insightful Power BI Dashboards

Transforming 2025 Payroll Data into Strategic Insight Using Power BI

Situation: I was asked to analyse an organisation’s 2025 payroll data. It lacked clear visibility of how salary expenditure and pay rises were distributed across regions, departments, and role levels. There were concerns about rising workforce costs, but no consolidated, interactive view to support strategic decision-making.

Task: My responsibility was to design and deliver a Power BI dashboard that would clearly present payroll trends, highlight high-cost areas, ensure data accuracy, and provide actionable insights to support workforce planning, budget control, and talent retention strategy.

Action: I approached the project using strong data visualisation and governance principles:

Data Integrity & Validation

Cleaned and transformed payroll data within Power BI.
Built DAX measures to calculate total pay expenditure, average pay rise percentage, and departmental comparisons.
Reconciled totals against source payroll systems to ensure 100% accuracy before publishing.

Clarity & Simplicity in Visual Design

Used bar charts to compare regional and departmental salary costs.
Applied stacked visuals to show distribution by role level.
Added KPI cards to highlight total payroll spend and average pay rise.
Removed unnecessary clutter and used clear, descriptive titles so non-technical stakeholders could immediately understand the message.

Highlighting Key Insights (Relevance)

Used conditional formatting and colour contrast to draw attention to high-cost regions and senior role categories.
Designed visuals to clearly show that senior and management roles contributed disproportionately to total expenditure.
Included comparisons to show that some departments received significantly higher total pay rises, suggesting targeted investment or retention pressures.

Accessibility & Engagement

Applied accessible colour palettes and legible fonts suitable for both digital and printed reports.
Implemented interactive slicers for region, department, and role level, allowing leadership to explore the data independently without additional explanation.

Result: The dashboard revealed several important strategic insights:

Payroll expenditure was heavily concentrated in specific regions, creating the greatest financial burden.
Senior roles accounted for a disproportionate share of total salary costs.
Pay rises were unevenly distributed, with certain departments and roles receiving higher increases, indicating potential skills shortages or competitive talent markets.

As a result, the dashboard I created can be used to:

Closely monitor high-cost regions and senior role categories.
Review pay rise allocation for fairness, consistency, and return on investment.
Strengthen workforce planning to balance cost control with talent retention and long-term growth.

The dashboard can be a key reporting tool for financial and HR planning, improving transparency, strategic alignment, and evidence-based decision-making across the organisation.

Aspiring to become a Data Apprentice

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