Will Boyle

Running Data Analysis Dashboard

Project Overview

This project started from a very practical problem: I’ve been logging my running training in excel every day since 2021, and over time the data grew to 2,000+ rows spread across a messy spreadsheet. While I was consistent about recording workouts, I’ll admit that I didn’t have a good way to visualize the data. And as a Strava-graph-enthusiast, this was a problem!

I wanted a way to visualize trends in mileage, consistency, and effort. This is something that would let me step back and understand how my training evolved over the years.

Working with Real, Messy Data

Because this dataset grew over several years, and its primary purpose was simply inputting training data, it wasn’t clean by any means. It contained:

  • Grouped sections and summary rows
  • Merged cells
  • Inconsistent formats and labels
  • Mixed levels of detail

A large part of this project was spent cleaning and transforming the data using Power Query. I normalized the structure, removed inconsistencies, and reshaped the data into a format that could support accurate time-based analysis. This was a great lesson on how much effort is actually put into data preparation compared to making the final visualizations.

Building the Dashboard

Once the data was structured properly, I designed a dashboard that includes the following:

  • Yearly and weekly mileage comparisons
  • Rolling 4-week mileage trends
  • Breakdown of effort levels (easy, moderate, hard)
  • Simple filtering by year

To support this, I created many custom measures using DAX, including weekly aggregations, averages, rolling calculations, and percentage comparisons. These measures allow the dashboard to stay flexible and responsive as filters change.

Why This Project Matters to Me

I enjoyed that this project solved a real problem I personally deal with. I wasn’t simply following a tutorial to learn Power BI anymore, I was actually building something I would use!

The end result is a tool I’m excited to use on a frequently to analyze my training. It also reinforced how powerful Power BI can be when paired with solid data modeling and thoughtful transformations.

Technical Stack

Excel → Power Query → Power BI (DAX & Visuals)

  • Source data maintained in Excel
  • Data cleaning and transformation handled in Power Query
  • Custom measures and calculations written in DAX
  • Interactive visuals and filtering built in Power BI

Skills Demonstrated

  • Data cleaning and transformation of real-world datasets
  • Power Query (shaping, normalization, and restructuring)
  • DAX measure creation (aggregations, rolling calculations, percentages)
  • Time-based analysis (weekly, yearly, rolling metrics)
  • Dashboard design focused on clarity and usability
  • Translating personal data into actionable insights