A Data-Driven LEGO Explorer
An interactive Power BI explorer for LEGO themes, prices and piece counts.
RoleData Visualization Designer
Prepared data and designed catalog filters and comparisons.
Problem
A static catalog makes relationships between themes, prices and piece counts difficult to explore. I wanted visitors to compare sets and inspect the records behind each view.
Key decisions
I used price per piece as a starting point and filters to narrow the catalog. Set details remain alongside the charts so visitors can check a comparison against the records.
Solution
I prepared the catalog with Python and Power Query and built the Power BI explorer. Filters and charts connect theme, price and piece-count comparisons with the underlying set details.
Explore screens & design details
A closer look at the features, interface and evidence behind the work.
I paired price-per-piece charts with filters and set details so visitors could check what the metric represents.
Trade-off: Price per piece supports comparison, but leaves out rarity, condition and resale value.
Why this approach
A shared ratio makes comparisons consistent, while filters define the group being compared. Price per piece leaves out rarity, condition and resale value, so the set details remain part of the view.
What I learned
Connecting overview charts to set details made the data story easier to inspect and question.
Next validation task
Ask visitors to compare two themes, explain a price-per-piece difference and reset the filters. Check their interpretation against the records.
Evidence includes designs, live demos, and documented decisions. Business outcomes require sourced measurements.