An Interactive Dashboard for Data-Driven Listening Insights

Power bi chart showcase

I created this interactive Power BI dashboard to analyze music streaming trends and provide data-driven insights into song popularity, artist rankings, and audio characteristics. The goal was to explore how music consumption has evolved over time and how data analytics can uncover meaningful trends.

Unlike dashboards that prioritize aesthetics, this project is focused on analytical depth, allowing users to interact with data, filter insights, and explore key streaming metrics. The emphasis here is on the power of data storytelling rather than visual embellishments.

Why This Project?

This dashboard serves as an example of advanced data visualization, demonstrating how interactive analytics can be applied across various industries. The same methodologies used here—data cleaning, modeling, and visualization—can be adapted to: Business Intelligence, Healthcare, Finance, Supply Chain, and Manufacturing

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Data collection & cleaning process

Data Wrangling

  • I Gathered data from Kaggle and access Spotify develop API (Free) to get all Track URLs.
  • The dataset includes such as song titles, artists, release years, streaming counts, and audio attributes like danceability, valence, and energy.
  • By creating a Date Table (DateAutoTemplate), I make my Power BI model more efficient on handling time-based analysis. It ensures that my reports can group, filter, and compare data accurately over time.

Key metrics and why they matter

KPI Board

Track Info section dynamically displays data only for the most streamed song, achieved using Top N Filtering.

It Provides a Quick Overview Using the 5Ws (Modified for Music Data):

Who? → The Artist (e.g., The Weeknd).

What? → The Song Title (Blinding Lights).

When? → The earliest release year (2019).

Why? → The song’s widespread popularity and sustained streaming growth over time.

How? → Through its musical characteristics (e.g., danceability, speechiness, energy).

User engagement

Heat Map

This heatmap visualization was created using Deneb in Power BI, leveraging a custom template from PowerBI-tips GitHub. It provides a time-based analysis of music streaming trends.

How the Heatmap Works

The heatmap visualizes streaming frequency across different days of the week (Y-axis) and months of the year (X-axis).

Darker red areas indicate higher streaming activity, meaning users streamed more music during those periods.

Lighter or faded areas represent lower streaming counts, suggesting less engagement.

The top bar chart aggregates the monthly streaming totals, giving a quick comparison of how streaming varies across different months.

The right-side bar chart aggregates the weekly streaming totals, helping identify which days of the week are most popular.

Processing Chart

Dynamic Energy Gauge Using Deneb

This processing chart was built using Deneb in Power BI, leveraging a Vega-Lite gauge visualization from a Stack Overflow solution. It serves as a dynamic representation of a song's energy level, adjusting based on user selection.

  • Deneb & Vega-Lite were used to create a gauge chart.
  • Energy Score (0.0 - 1.0) represents a perceptual measure of a track’s intensity and activity.

When a user clicks on a specific track, the gauge dynamically updates to reflect that song's energy level.

Bringing Music to Life

Dynamic Album Cover

This implementation uses Spotify API to retrieve album cover URLs and dynamically display them in Power BI using the HTML Content Visual.

  • I Access Spotify API to fetch album cover images for each track.
  • Create a new DAX calculated column that retrieves the album cover URL for the most-streamed song.
  • Drag it into Power BI’s HTML Content Visual to display album images dynamically.
  • Use HTML & CSS to Format the Album Cover Display to ensure that the album cover fits perfectly within my Power BI dashboard.

What the data reveals

Data-Driven Insights & Findings

This Power BI dashboard reveals key trends in music streaming by analyzing track performance, listener engagement, and song characteristics. Here’s what I found:

Peak Streaming Trends

  1. Streaming peaks on weekends, especially Fridays and Saturdays, indicating users engage with music more during leisure time.
  2. Seasonal trends show spikes in streams during holiday months (December) and summer, suggesting that vacations and festivities impact listening behavior.

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Popular Song Characteristics

  1. Songs with higher danceability and energy levels tend to dominate the streaming charts.
  2. Valence (happiness score) is balanced, indicating that mood variety is important.
  3. Speechiness is generally low, meaning purely lyrical content (e.g., podcasts, spoken word) is less dominant in top-streamed tracks.

Artist & Track Performance

  1. The most-streamed track consistently accumulates billions of streams, reaffirming that repeat listening behavior plays a significant role in rankings.
  2. Older songs continue to perform well, showing that classic hits still attract large audiences.
  3. Established artists maintain dominance, while viral trends can occasionally push emerging artists into the charts.

Here are some ideas for future enhancements

Real-Time Streaming Data Integration

  • Connect to Spotify API for live streaming updates, instead of relying on static datasets

Predictive Analytics

  • Use machine learning models to predict which songs will trend next based on historical data

Demographic Analysis

  • Add location-based insights to see where specific songs are most popular.
  • Include age and gender analytics (if data is available) to understand audience diversity.