Miniviz May - Week 4 Challenge - Where are the good vibes?
This week is all about maps - but not the kind that just sit there looking pretty. Use your map to show a pattern or difference across locations, not just where things are. For my example, I created a fictional dataset that assigns each country a dominant vibe. The goal isn’t accuracy - it’s to show how a single category can reveal something interesting across the world. This is the Shape Map visual, with an imported .json world map. Use the slicer to explore different vibes and see how the pattern shifts. A few tips: Keep it simple - one measure or category Limit your categories so it’s easy to read Make your map do the storytelling Have fun with it - this is a great week to get creative ✨ If you missed any of the other weeks, you can read about them at aka.ms/MinivizMayMiniviz May - Week 3 Challenge - What Matters Most?
This week we’re exploring one of the most revealing chart types: the treemap. We’re not just showing categories - we’re uncovering what actually takes over. 📊 Your challenge: Build a treemap that reveals where your time is really going - and what’s driving it. Check out the starter file and build your own tree map! You are welcome to use the data in the starter file or create your own. The key: keep it simple! Ensure your visual is accessible. Set tab order, alt text, and ensure proper color contrast. Have FUN! Need more info about the contest? Visit aka.ms/MinivizMayMiniviz May - Week 1 Challenge - What Changed?
1️⃣ Week 1 Challenge - now live! Let’s kick things off with one of the most important (and misunderstood) business metrics… headcount. This week, you’re not just showing a number - you’re telling the story behind how it changed. Your challenge: Build a waterfall chart that explains how headcount moved from Jan 1 → Mar 31 Check out the starter file and build your own waterfall chart! You are welcome to use the data in the starter file or create your own. The key: keep it simple! Need more info about the contest? Visit aka.ms/MinivizMayMiniviz May - Week 2 Challenge - What Relates?
If you’re joining us again, let’s keep it going! And if you’re jumping into week 2 fresh, welcome! This week we’re diving into one of the most insightful chart types: the scatterplot. This week, we’re not just placing dots - we’re uncovering how things move together. 📊 Your challenge: Build a scatterplot that explores the relationship between temperature and ice cream sales. Check out the starter file and build your own scatterplot! You are welcome to use the data in the starter file or create your own. The key: keep it simple! Ensure your visual is accessible. Set tab order, alt text, and ensure proper color contrast. Have FUN! Need more info about the contest? Visit aka.ms/MinivizMaySpotlight - Sales Performance Overview
Spotlight The KPI Card below shows the Sales for the Current Year, comparison vs Sales Target and Prior Year Sales, lowest performing Product in the current year and the month with lowest achievement vs Target. Main features: Complete performance snapshot - See Total sales, Target variance, YoY growth, and Monthly Trend in one unified view without switching between reports. Automatic problem detection - Instantly identifies your worst-performing product, weakest month, and total months below target without manual analysis. Smart contextual insights - Shows both Performance vs Target and YoY growth to help you understand if you're truly underperforming or if targets need adjustment. Visual pattern recognition - Color-coded sparkline with gradient areas lets you spot performance trends, seasonal patterns, and momentum shifts at a glance.Spotlight-Improving Health Habits Over a Year
This visual highlights the improvement of key healthy,lifestyle habits over the course of a year. The data shows positive changes in four areas: an increase in daily walking, a reduction in sugar intake, higher participation in learning activities, and an overall boost in the happiness score. I used Cards to display the main KPIs and a Before VS After comparison chart to show the scale of improvement. Together, the visuals tell a clear story of progress toward better wellness and healthier daily habits.QuickViz Challenge: Spotlight | Home Assistant weather sensors
(not participating in the contest - I already won a challenge) As a smart-home enthusiast, I monitor many sensors through a Home Assistant dashboard. I wanted to reproduce a similar view in Power BI, using data directly from my Home Assistant instance via its REST API. My Home Assistant installation is fully local for security reasons, and the sensors communicate through Zigbee2MQTT, which publishes their readings as MQTT messages. Home Assistant ingests these values, and Power BI connects to Home Assistant over the local network. For this first test, I focused on two data sources exposed by Home Assistant: • a temperature and humidity sensor on the balcony • the DWD (German Weather Service) weather integration for Home Assistant, which provides outdoor measurements via API for the nearest station To authenticate Power BI against Home Assistant, I created a long-lived access token in the Home Assistant user profile and stored it as a Power BI parameter. Power BI then calls the Home Assistant REST endpoint for each entity ID, passing the token in the header. Each request returns the recorded state history for the chosen period (in my case the last 24 hours). Power BI retrieves the JSON response, expands the state records, and loads the timestamps and sensor values into a table. A single Power Query script loops over the relevant entity IDs and combines the returned data into one table. That combined query is then referenced to create two focused queries, one filtered for temperature and one for humidity, which feed the respective visuals. The file can be modified with own token (and the local Home Assistant IP address) and own devices.QuickViz - Spotlight - Body Composition
This report is built from an imported Excel dataset and loaded into the Body Comp table, containing date, body_part, measurement_type, value, and unitdata. Visuals support time-based analysis of body composition measurements (cards, trend lines, and slicers date and measurement type). A measure, 14d Trend, compares the selected measurement date to the last 14-day average and returns neutral, upwards, or downwards using a ±3% neutral threshold. The dataset and measures enable quick detection of short-term changes in muscle mass or body fat and support overview of all body parts.
