Data Visualization

 Project Overview- AdventureWorks Power BI Dashboard

The primary objective was to create a comprehensive sales performance dashboard to monitor key metrics and trends for AdventureWorks Bike Shop in Power BI. The dashboard aimed to provide a clear and concise overview of the company's sales performance, helping stakeholders make informed decisions.

Purpose: This dashboard was designed to track and analyze the total revenue, profit, orders, and return rates across different product categories and time periods. The insights derived from this dashboard would assist in identifying sales patterns, product performance, and areas needing improvement.

Consumers: The dashboard is intended for use by the senior management team, including the Sales Director, Product Managers, and Marketing Analysts. These stakeholders require real-time data visualization to understand sales dynamics and drive strategic decisions.

Dataset: There were csv files for AdventureWorks raw data including files for Calendar , Customer, Product, Product category, Product subcategory, Sales , Return and Territory data. Sales data was available for 3 years 2020, 2021 and 2022

End Users' Needs: The end users need is to:

• Monitor overall sales performance and profitability.

• Identify top-performing and underperforming products.

• Understand sales trends and seasonality.

• Track return rates to address quality or customer satisfaction issues.

• Make data-driven decisions to optimize sales strategies and marketing campaigns.

Communication Goals:

• Provide a clear snapshot of key sales metrics (Total Revenue, Profit, Orders, Return Rate).

• Visualize revenue trends and highlight significant changes over time.

• Break down orders and revenue by product categories for deeper insights.

• Present product-level performance to identify high and low performers.

• Highlight most ordered and most returned product types for quick strategic insights.

• Have a forecast on revenue for next 6 months

Steps I followed to make this dashboard-

1. Connecting and Shaping Data

• Data Sources: Connected to various data sources including CSV files.

• Data Cleaning: Used Power Query Editor to clean and transform raw data. This included removing duplicates, handling missing values, update table names

• Data Integration: Merged data from different sources to create a unified dataset.

2. Creating Data Model

• Relationships: Established relationships between tables to create a robust data model. Key tables include Customers, Products, Sales, Categories and Subcategories.

• Calculated Columns: Added calculated columns to enrich the data model and provide additional insights.

3. Calculated Fields with DAX

• Total Revenue: Created a DAX measure to calculate the total revenue: Total Revenue = SUM(Orders[Amount])

• Total Profit: Used DAX to calculate total profit: Total Profit = SUM(Orders[Profit])

• Price Point: Created new field using DAX conditional statement: 

• Price Point =

switch (

    TRUE(),

    'Product Lookup'[ProductPrice] > 500, "High",

    'Product Lookup'[ProductPrice] > 100, "Mid- Range",

    "Low"

)

• Monthly Metrics: Implemented time intelligence functions in DAX to calculate monthly metrics like revenue, orders, and returns.

4. Data Visualization

• KPIs: Displayed key performance indicators (KPIs) such as Monthly Revenue, Monthly Orders, and Monthly Returns below revenue trend chart and used flashcards at the top to showcase overall performance of Revenue, Orders, Profit and Return Rate.

• Trend Analysis: Used a line chart to visualize revenue trends over time.

• Category Breakdown: Employed bar charts to show orders by category (Accessories, Bikes, Clothing).

• Product Performance: Included a table to detail product performance, showing total orders, total revenue, and return rate for each product.

• Highlights: Highlighted most ordered product type and most returned product type for quick insights.

• Interactive Elements: Added interactive features such as tooltips and slicers to allow users to drill down into specific data points.

Project 2 : Tableau Dashboard

Project Overview

Interactive Sales Dashboard with Dynamic Breadcrumb trail and Proportional Brushing

Problem statement: WoW 42 (Workout Wednesday Quiz) 

Dataset: Superstore Dataset

Key Attributes: Region, Segment, Ship Mode, Subcategory, Sales

The objective of this project was to create an interactive dashboard for visualizing sales data from the Superstore dataset. The aim was to enable users to explore sales metrics across different dimensions such as Region, Segment, Ship Mode, and Subcategory in a seamless and intuitive manner. The dashboard needed to allow for dynamic filtering and provide a clear summary of the selected data.

Steps Taken:

1. Data Preparation:

2. Data Cleaning: Ensured data quality by checking for missing values and correcting any inconsistencies.

3. Data Transformation: Aggregated sales data to make it suitable for visualizations.

4. Creating Bar Charts:

• Region: A bar chart showing sales distribution across different regions.

• Segment: A bar chart displaying sales by customer segments (e.g., Consumer, Corporate, Home Office).

• Ship Mode: A bar chart illustrating sales based on different shipping modes.

• Subcategory: A bar chart detailing sales for various product subcategories.

Interactive Features (Most Important):

1. Selection Highlighting: Implemented functionality where selecting a bar in any chart highlights the corresponding sales in all other charts.

2. Additive Filtering: Enabled users to select multiple bars, which cumulatively filter and highlight the relevant sections across all charts.

Creating a BANNER (Most Important):

1. Total Sales Display: Designed a banner to display total sales for the selected filters.

2. Dynamic Breadcrumb: Developed a breadcrumb trail to show active filters. When no filters are applied, it displays ‘All Segments Selected’.

Dashboard Construction:

1. Parameters and Sets: Utilized multiple parameters and sets to manage the dynamic interactions within the dashboard.

2. Calculated Fields: Created calculated fields to support complex filtering logic and aggregate calculations.

Proportional Brushing:

Smooth Interaction: Implemented proportional brushing to allow users to click into segment combinations of interest without navigating back to the top to change filters.

Results:

1. Enhanced User Experience: The interactive dashboard provides a user-friendly interface for exploring sales data across various dimensions.

2. Dynamic Insights: Users can quickly identify trends and patterns by dynamically filtering data and viewing the results in real-time.

3. Clear Summary: The total sales banner and dynamic breadcrumb trail ensure users are always aware of the current filters applied, enhancing the interpretability of the data.

Learning and Impact

1. Data Visualization Skills: This project enhanced my ability to create interactive and visually appealing dashboards using multiple parameters, sets, and calculated fields.

2. User-Centric Design: Focusing on user experience, I learned the importance of making data exploration intuitive and efficient.

3. Technical Proficiency: Improved my technical skills in tools such as Tableau (or any other visualization tool used) and honed my ability to manage complex interactions within a dashboard.

hank you for checking out the dashboard, I hope you enjoy playing around with it!

Tool used: Tableau Public