VanArsdel Ltd – Sales & Marketing Dashboard

This repository showcases an interactive Power BI dashboard built for the fictive company VanArsdel Ltd, focusing on sales and marketing performance across multiple countries and product segments. The project demonstrates data visualization, storytelling, and KPI monitoring skills.

📊 Project Overview

• Objective:
  Analyze VanArsdel’s international sales and marketing data to identify key trends, track revenue growth, and monitor urban vs. rural performance.

• Key Features:

  • Date range and slicers for country, category, and segment filtering.
  • KPIs for Urban Sales Share, Total Revenue, and goal comparison.
  • Visuals by Country, Year/Quarter, Category, and Manufacturer.
  • Trend analysis with line charts and revenue composition by manufacturer.

🗂 Repository Contents

• VanArsdel.pbix – Power BI dashboard file (interactive report)
• bi_date.xlsx – Sample data files used for the analysis
• bi_geo.xlsx – Sample data files used for the analysis
• bi_product.xlsx – Sample data files used for the analysis
• Flag_icons.xlsx – Sample data files used for the analysis
• InternationalSales – Data files used for the analysis
• README.md – Project documentation (this file)

📈 Dashboard Insights

The dashboard provides:

• Geographical revenue view using maps for regional analysis.
• Urban vs Rural performance with a gauge and KPI comparison against goals.
• Time-series revenue trends by year and quarter.
• Category & segment analysis showing top-performing products.
• Manufacturer-level contribution over time with stacked area charts.

These insights allow decision-makers to:

• Monitor sales targets and identify underperforming areas.
• Align marketing strategies with revenue drivers.
• Spot seasonal or regional trends for better planning.

🛠 Tools & Skills

• Power BI Desktop: Data modeling, DAX measures, interactive dashboards.
• Data Preparation: Fact and dimension modeling (Date, Geo, Product, Flags).
• Visualization Techniques: KPIs, maps, trend lines, drill-throughs, slicers.

🚀 How to Use

1. Clone the repository or download the .pbix file.
2. Open the report in Power BI Desktop.
3. Use the slicers to explore data by date range, country, segment, and category.

📌 Future Improvements

• Add marketing campaign cost vs ROI analysis.
• Integrate demographic segmentation data.
• Automate refresh with cloud-based data sources (Power BI Service).

Author

Project by Mark Hatala – focusing on data analysis, visualization, and storytelling for business insights.

Images

Olympic History Dashboard – Tableau Project

This project explores 120 years of Olympic Games history (1896–2012) using the Kaggle dataset 120 Years of Olympic History: Athletes and Results.
It was built as an interactive Tableau dashboard to practice data visualization and storytelling with real-world data.

📊 Project Overview

• Objective:
  Analyze global Olympic medal trends, top-performing athletes and countries, and focus on Hungary’s historical results.

• Key Questions:

  • Which countries dominated Olympic history?
  • Who are the top medal-winning athletes?
  • How did medal counts evolve over time?
  • What is Hungary’s performance compared to global trends?

🗂 Dataset

• Source: Kaggle – 120 Years of Olympic History
• Data covered: 1896 to 2012 Summer and Winter Olympics
• Includes: Athletes, country (NOC), gender, year, sport, event, medal type

📈 Dashboard Features & Insights

The Tableau dashboard includes:

• World Map: Medals by country (filterable by year, region, and income group)
• Top 10 Countries Medal Trends: Running total of medals since 1896
• Top Medal-Winning Athletes: Highlighting legends like Michael Phelps & Larisa Latynina
• Hungarian Performance: Line chart comparing Hungarian medals vs global totals
• Sports Heatmap: Medal count by year and sport (e.g., fencing, aquatics, gymnastics)
• Gender Trends: Breakdown of medals by men vs women over time

Key Insights:

• USA leads historically with the highest medal count.
• Strong historical performances by USSR, GBR, FRA, GER, and CHN.
• Hungary has an outstanding legacy in fencing and aquatics.
• Women’s participation and medal counts grew significantly after mid-20th century.

🛠 Tools & Skills

• Tableau Desktop: Dashboard design, filters, calculated fields
• Data Preparation: Basic cleaning in Tableau and Excel
• Visualization Techniques: Heatmaps, KPIs, line charts, maps, drill-downs

🚀 How to View

• Download and open Olimpic_medals.pdf for a static preview.
• For interactive exploration, open the Tableau workbook (.twb/.twbx) in Tableau Desktop (if included).

📌 Future Improvements

• Extend dataset beyond 2012 (Tokyo 2020, Paris 2024 when available)
• Add athlete demographics and event-level analysis
• Integrate with Power BI for comparison of visualization platforms

Author

Project by Mark Hatala – exploring data visualization and analytics through real-world datasets.

Images

This project was my first hands-on experience with SQL and Power BI, where I explored real-world bike-sharing data to practice analysis, data cleaning, and visualization.

🎯 Project Goal

Learn and apply SQL for querying, cleaning, and exploring data.
Build an interactive Power BI dashboard to visualize trends and insights.
Practice data storytelling using a public dataset.

📊 Dataset

The dataset comes from Kaggle: NYC Citi Bike Sampled Data (2013–2017).

For this project, I focused on the January–March 2017 (Q1) subset.

The data includes:

• Trip start/end times and stations
• Trip duration
• Rider demographics (age, gender, user type)

🛠 Tools & Skills Practiced

• SQL (MySQL Workbench) – Joins, aggregations, filtering, anomaly detection.
• Power BI – Data modeling, calculated measures (DAX), and dashboard creation.
• Data storytelling – Translating findings into meaningful business insights.

🔍 Key Steps

1. Data Cleaning & Preparation
   • Removed anomalies (e.g., invalid birth years).
   • Parsed and transformed date/time columns.
   • Identified peak/trough outliers and validated with domain context.
2. SQL Analysis
   • Queries for daily and hourly ridership trends.
   • Calculations of average trip duration, repeat riders, and user type breakdown.
3. Power BI Dashboard
   • Built interactive visuals:
     • Line chart: trips over time
     • Heatmap: weekday vs. hour trip patterns
     • Demographics breakdown
     • Map: most popular stations
   • KPI cards summarizing key metrics.

📈 Insights Gained

• Weekday commuter trends: Strong peaks at 8–9 AM and 5–7 PM.
• Subscriber dominance: Majority of trips are by subscribers; casual users spike on weekends.
• Station hotspots: Central Manhattan consistently drives the highest trip volume.

💡 Learnings

• First experience combining SQL for backend prep with Power BI for front-end visualization.
• Gained practice in data cleaning and understanding why anomalies matter.
• Improved skills in presenting insights visually for non-technical audiences.

🔗 Resources

• Dataset on Kaggle
• Power BI Dashboard
• SQL Queries

This project marks the start of my journey in data analytics and serves as a foundation for future, more advanced projects.

Brazilian E-Commerce Analytics Dashboard

This project was built as part of a Data Visualization & Analytics interview task. It demonstrates data modeling, insight generation, and storytelling skills using Power BI and a real-world dataset.

Project Overview

We used the Olist Brazilian E-Commerce dataset which contains over 100k orders from 2016 to 2018 (changed to 2019 - 2021). The dataset includes information about:

• Orders and order status
• Payments and installments
• Products and categories
• Sellers and customers (with geolocation)
• Delivery performance (actual vs. estimated)
• Customer reviews and satisfaction scores
• Website analytics (visits, sources, ad clicks)

Goal:
To build an executive-level dashboard (company health), a tactical dashboard (website performance), and generate three key business insights supported by data.

Key Tasks

1. Data Modeling

• Combined multiple CSV files (Orders, Items, Payments, Reviews, Customers, Sellers, Products, Geolocation, Website analytics).
• Created a star schema with olist_orders as the central fact table.
• Added a Date dimension for time intelligence.
• Cleaned and transformed data (handled missing/invalid values, converted time formats).

2. Executive Dashboard

KPIs and visuals built for C-level stakeholders:

• Total Revenue, Orders, Average Order Value
• On-time Delivery %, Avg. Review Score
• Orders & Revenue trend over time
• Map: Orders by State
• Top product categories & payment methods

3. Tactical Dashboard

Focused on website performance and user acquisition:

• New vs Returning visitors
• Conversion funnel: Visitors → Clicks → Orders → Revenue
• Average time on site, ad click performance
• Revenue per traffic source and platform

4. Insight Generation

Generated 3 actionable insights for executives:

1. Delivery delays hurt customer satisfaction: late deliveries have much lower review scores and higher cancellations.
2. Low customer retention: most customers buy once; repeat customers spend more and leave higher reviews.
3. Paid channel inefficiency: paid ads drive traffic but with low conversion; organic/direct channels perform better.

Each insight includes business recommendations, e.g., improve SLA compliance, launch loyalty programs, and reallocate ad spend.

Business Impact

• Highlighted that improving on-time delivery and retention could directly boost revenue and satisfaction.
• Identified website optimization and ad spend reallocation opportunities.
• Provided executives with a data-driven health check of the company.

Deliverables

• Power BI PBIX file: Interactive dashboards with slicers and drill-through pages.
• Executive summary: Three main insights with recommended business actions.
• Data model: Cleaned and documented schema with proper relationships.

How to Use

1. Clone or download the project.
2. Open the .pbix file in Power BI Desktop.
3. Use the filters/slicers to explore data across:
   • Time (month/year)
   • Campaign periods
   • Regions (states)
   • Website sources/platforms

Technologies Used

• Power BI Desktop for ETL, modeling, visualization
• Power Query for data cleaning
• DAX for calculated measures (e.g., on-time %, repeat purchase rate, conversion)
• Kaggle Dataset: Olist Brazilian E-Commerce (public)

Images

Author

Project by Mark Hatala LinkedIn | GitHub