This project focuses on data mapping, integration, and analysis to support the development and enhancement of six UNCDF operational applications: OrgTraveler, Comms Central, Internal Support Hub, Partnership 360, SmartHR, and TimeTrack. These apps streamline workflows for travel claims, internal support, partnership management, and time tracking within UNCDF.Key Features and Tools:Data Mapping for Salesforce CRM Migration: Structured and mapped data flows to ensure compatibility and seamless migration to Salesforce CRM.Python for Data Cleaning and Transformation: Utilized pandas, numpy, and APIs to clean, preprocess, and transform raw datasets into standardized formats.Power BI Dashboards: Designed interactive dashboards to visualize workflows and monitor performance metrics for decision-making.Collaboration Across Platforms: Integrated Google Collab for code collaboration and Microsoft Excel for data validation and analysis.

🚀 Excited to Share My Latest Project! 🚀

I recently completed a comprehensive Power BI dashboard focused on analyzing the revenue and growth performance of top companies across various industries. This project was a fantastic opportunity to dive deep into data visualization and business intelligence, turning complex datasets into actionable insights.

Key Highlights: 📊 Revenue Analysis: Visualized the total revenue distribution across different industries and headquarters. 📈 Growth Insights: Analyzed revenue growth trends to highlight top performers and growth opportunities. 🏆 Company Ranking: Ranked companies based on revenue to identify market leaders. 🎯 Interactive Filters: Enabled dynamic data exploration with industry-specific filters. 💼 Key Metrics: Displayed essential KPIs like total revenue, average revenue growth, and employee count. 🎨 User-Friendly Design: Focused on creating a visually appealing and functionally effective dashboard layout.

This project has further honed my skills in Power BI, data visualization, and business intelligence, and I’m thrilled to add it to my portfolio.

This data set is perfect for practicing your analytical skills for Power BI, Tableau, Excel, or transform it into a CSV to practice SQL.

This use case mimics transactions for a fictional eCommerce website named EverMart Online. The 3 tables in this data set are all logically connected together with IDs.

My Power BI Use Case Explanation - Using Microsoft Power BI, I made dynamic data visualizations for revenue reporting and customer behavior reporting.

Revenue Reporting Visuals - Data Card Visual that dynamically shows Total Products Listed, Total Unique Customers, Total Transactions, and Total Revenue by Total Sales, Product Sales, or Categorical Sales. - Line Graph Visual that shows Total Revenue by Month of the entire year. This graph also changes to calculate Total Revenue by Month for the Total Sales by Product and Total Sales by Category if selected. - Bar Graph Visual showcasing Total Sales by Product. - Donut Chart Visual showcasing Total Sales by Category of Product.

Customer Behavior Reporting Visuals - Data Card Visual that dynamically shows Total Products Listed, Total Unique Customers, Total Transactions, and Total Revenue by Total or by continent selected on the map. - Interactive Map Visual showing key statistics for the continent selected. - The key statistics are presented on the tool tip when you select a continent, and the following statistics show for that continent: - Continent Name - Customer Total - Percentage of Products Sold - Percentage of Total Customers - Percentage of Total Transactions - Percentage of Total Revenue

Discover the booming Business Data Visualization Software market! Explore key trends, growth drivers, and leading companies shaping this dynamic sector. Learn about projected market size, CAGR, and regional insights in our comprehensive analysis. Find out how AI, cloud solutions, and self-service BI are transforming data analysis.

Analyzing sales data is essential for any business looking to make informed decisions and optimize its operations. In this project, we will utilize Microsoft Excel and Power Query to conduct a comprehensive analysis of Superstore sales data. Our primary objectives will be to establish meaningful connections between various data sheets, ensure data quality, and calculate critical metrics such as the Cost of Goods Sold (COGS) and discount values. Below are the key steps and elements of this analysis:

1- Data Import and Transformation:

• Gather and import relevant sales data from various sources into Excel.
• Utilize Power Query to clean, transform, and structure the data for analysis.
• Merge and link different data sheets to create a cohesive dataset, ensuring that all data fields are connected logically.

2- Data Quality Assessment:

• Perform data quality checks to identify and address issues like missing values, duplicates, outliers, and data inconsistencies.
• Standardize data formats and ensure that all data is in a consistent, usable state.

3- Calculating COGS:

• Determine the Cost of Goods Sold (COGS) for each product sold by considering factors like purchase price, shipping costs, and any additional expenses.
• Apply appropriate formulas and calculations to determine COGS accurately.

4- Discount Analysis:

• Analyze the discount values offered on products to understand their impact on sales and profitability.
• Calculate the average discount percentage, identify trends, and visualize the data using charts or graphs.

5- Sales Metrics:

• Calculate and analyze various sales metrics, such as total revenue, profit margins, and sales growth.
• Utilize Excel functions to compute these metrics and create visuals for better insights.

6- Visualization:

• Create visualizations, such as charts, graphs, and pivot tables, to present the data in an understandable and actionable format.
• Visual representations can help identify trends, outliers, and patterns in the data.

7- Report Generation:

• Compile the findings and insights into a well-structured report or dashboard, making it easy for stakeholders to understand and make informed decisions.

Throughout this analysis, the goal is to provide a clear and comprehensive understanding of the Superstore's sales performance. By using Excel and Power Query, we can efficiently manage and analyze the data, ensuring that the insights gained contribute to the store's growth and success.

Discover the explosive growth of the Business Intelligence (BI) Analysis Tools market, projected to reach $62.5 billion by 2033 with a 12% CAGR. This comprehensive market analysis explores key trends, drivers, restraints, and regional insights, featuring top players like Tableau, Power BI, and QlikView. Learn how self-service BI and cloud solutions are transforming data analysis.

🎯 Turning E-commerce Data into Actionable Insights! Today, I’m excited to showcase my Amazon Sales Analysis Dashboard built using Power BI. This dashboard offers a comprehensive view of sales performance, customer behavior, and product trends.

🔎 Key Insights Delivered: ✅ Total Sales & Revenue Trends – Visualize monthly and yearly revenue growth. ✅ Top-Selling Products – Identify products driving the highest revenue. ✅ Customer Insights – Analyze purchasing patterns and segment customers. ✅ Geographic Sales Analysis – Track regional performance to target growth areas. ✅ Category-Wise Performance – Evaluate which product categories are thriving.

🚀 Why This Matters: With clear and actionable insights, this dashboard can help businesses: ✔ Optimize inventory management ✔ Improve marketing strategies ✔ Enhance customer satisfaction ✔ Maximize sales revenue

💡 Tech Stack Used: 🔸 Power BI – Dynamic dashboard creation 🔸 DAX – Advanced data calculations 🔸 Power Query – Data cleaning and transformation 🔸 Data Storytelling – Transforming data into clear visual insights

Healthcare Cloud Based Analytics Market Outlook

The global healthcare cloud based analytics market size was valued at approximately USD 14.8 billion in 2023, and it is anticipated to reach around USD 54.3 billion by 2032, growing at a compound annual growth rate (CAGR) of 15.7% from 2024 to 2032. One of the primary growth factors influencing this market is the increasing demand for data-driven decision-making processes in healthcare settings to enhance patient outcomes and operational efficiency.

One significant growth factor for the healthcare cloud based analytics market is the rapid digital transformation within the healthcare sector. The transition from paper-based systems to electronic health records (EHRs) and the adoption of telehealth services are driving the need for sophisticated analytics solutions that can process vast amounts of healthcare data. The accessibility and scalability offered by cloud-based solutions make them particularly attractive for healthcare providers looking to leverage patient data for better diagnostic and treatment outcomes.

Moreover, the rising focus on personalized medicine and the need for population health management are propelling the demand for healthcare cloud based analytics. Personalized medicine requires the analysis of large datasets to understand individual patient profiles and predict responses to treatments. Similarly, population health management aims to improve health outcomes by analyzing data to identify trends and intervene proactively. Cloud-based analytics platforms provide the necessary computational power and flexibility to handle these complex data requirements efficiently.

The cost-efficiency of cloud based solutions compared to traditional on-premises systems is another crucial growth driver. Healthcare organizations are under constant pressure to reduce operational costs while improving patient care quality. Cloud-based analytics solutions eliminate the need for significant upfront investments in hardware and software while offering the benefits of scalable resources and reduced IT maintenance costs. This financial advantage is particularly appealing to small and medium-sized healthcare providers who may have limited budgets for technology investments.

The integration of Business Intelligence in Healthcare is transforming the way data is utilized to improve patient care and streamline operations. By employing BI tools, healthcare organizations can analyze vast datasets to uncover insights that drive better decision-making. These tools enable healthcare providers to track patient outcomes, optimize resource allocation, and enhance overall operational efficiency. The ability to visualize data through dashboards and reports allows for a deeper understanding of patient trends and organizational performance, ultimately leading to improved healthcare delivery and patient satisfaction.

From a regional perspective, North America currently holds the largest market share in the healthcare cloud based analytics market, driven by advanced healthcare infrastructure and high adoption rates of digital healthcare technologies. However, regions like Asia Pacific are expected to witness the highest growth rates during the forecast period. Factors such as increasing healthcare expenditures, growing awareness about the benefits of healthcare analytics, and supportive government initiatives are contributing to the market expansion in these regions.

Component Analysis

The healthcare cloud based analytics market can be segmented by component into software and services. The software segment includes various analytics platforms and tools designed to process and analyze healthcare data. These software solutions are essential for enabling healthcare providers to harness the power of big data and derive actionable insights. As the volume of healthcare data continues to grow exponentially, the demand for robust and scalable analytics software solutions is expected to increase significantly. Innovations in artificial intelligence and machine learning are also enhancing the capabilities of these software solutions, making them more effective in predictive analytics and decision support.

Cloud Computing in Healthcare is revolutionizing the way healthcare data is stored, accessed, and analyzed. By leveraging cloud technology, healthcar

According to Cognitive Market Research, the global Data Preparation Tools market size will be USD XX million in 2025. It will expand at a compound annual growth rate (CAGR) of XX% from 2025 to 2031.

North America held the major market share for more than XX% of the global revenue with a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031. Europe accounted for a market share of over XX% of the global revenue with a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031. Asia Pacific held a market share of around XX% of the global revenue with a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031. Latin America had a market share of more than XX% of the global revenue with a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031. Middle East and Africa had a market share of around XX% of the global revenue and was estimated at a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031. KEY DRIVERS

Increasing Volume of Data and Growing Adoption of Business Intelligence (BI) and Analytics Driving the Data Preparation Tools Market

As organizations grow more data-driven, the integration of data preparation tools with Business Intelligence (BI) and advanced analytics platforms is becoming a critical driver of market growth. Clean, well-structured data is the foundation for accurate analysis, predictive modeling, and data visualization. Without proper preparation, even the most advanced BI tools may deliver misleading or incomplete insights. Businesses are now realizing that to fully capitalize on the capabilities of BI solutions such as Power BI, Qlik, or Looker, their data must first be meticulously prepared. Data preparation tools bridge this gap by transforming disparate raw data sources into harmonized, analysis-ready datasets. In the financial services sector, for example, firms use data preparation tools to consolidate customer financial records, transaction logs, and third-party market feeds to generate real-time risk assessments and portfolio analyses. The seamless integration of these tools with analytics platforms enhances organizational decision-making and contributes to the widespread adoption of such solutions. The integration of advanced technologies such as artificial intelligence (AI) and machine learning (ML) into data preparation tools has significantly improved their efficiency and functionality. These technologies automate complex tasks like anomaly detection, data profiling, semantic enrichment, and even the suggestion of optimal transformation paths based on patterns in historical data. AI-driven data preparation not only speeds up workflows but also reduces errors and human bias. In May 2022, Alteryx introduced AiDIN, a generative AI engine embedded into its analytics cloud platform. This innovation allows users to automate insights generation and produce dynamic documentation of business processes, revolutionizing how businesses interpret and share data. Similarly, platforms like DataRobot integrate ML models into the data preparation stage to improve the quality of predictions and outcomes. These innovations are positioning data preparation tools as not just utilities but as integral components of the broader AI ecosystem, thereby driving further market expansion. Data preparation tools address these needs by offering robust solutions for data cleaning, transformation, and integration, enabling telecom and IT firms to derive real-time insights. For example, Bharti Airtel, one of India’s largest telecom providers, implemented AI-based data preparation tools to streamline customer data and automate insights generation, thereby improving customer support and reducing operational costs. As major market players continue to expand and evolve their services, the demand for advanced data analytics powered by efficient data preparation tools will only intensify, propelling market growth. The exponential growth in global data generation is another major catalyst for the rise in demand for data preparation tools. As organizations adopt digital technologies and connected devices proliferate, the volume of data produced has surged beyond what traditional tools can handle. This deluge of information necessitates modern solutions capable of preparing vast and complex datasets efficiently. According to a report by the Lin...

📌 Overview

This dataset contains a cleaned and transformed version of the public Divvy Bicycle Sharing Trip Data covering the period November 2024 to October 2025.

The original raw data is publicly released by the Chicago Open Data Portal, and has been cleaned using Pandas (Python) and DuckDB SQL for faster analysis.
This dataset is now ready for direct use in: - Exploratory Data Analysis (EDA) - SQL analytics - Machine learning - Time-series/trend analysis - Dashboard creation (Power BI / Tableau)

📂 Source

Original Data Provider:
Chicago Open Data Portal – Divvy Trips
License: Open Data Commons Public Domain Dedication (PDDL)
This cleaned dataset only contains transformations; no proprietary or restricted data is included.

🔧 Cleaning & Transformations Performed

• Combined monthly CSVs (Nov 2024 → Oct 2025)
• Removed duplicates
• Standardized datetime formats
• Created new fields:
  • ride_length
  • day_of_week
  • hour_of_day
• Handled missing or null values
• Cleaned inconsistent station names
• Filtered invalid ride durations (negative or zero-length rides)
• Exported as a compressed .csv for optimized performance

📊 Columns in the Dataset

• ride_id
• rideable_type
• started_at
• ended_at
• start_station_name
• end_station_name
• start_lat
• start_lng
• end_lat
• end_lng
• member_casual
• ride_length (minutes)
• day_of_week
• hour_of_day

💡 Use Cases

This dataset is suitable for: - DuckDB + SQL analytics - Pandas EDA - Visualization in Power BI, Tableau, Looker - Statistical analysis - Member vs. Casual rider behavioral analysis - Peak usage prediction

📝 Notes

This dataset is not the official Divvy dataset, but a cleaned, transformed, and analysis-ready version created for educational and analytical use.

The Designing Data Visualization Services market is booming, projected to reach $46 billion by 2033 with a 15% CAGR. Discover key trends, leading companies, and regional insights in this comprehensive market analysis. Learn how data visualization is transforming business decisions.

Unlock the power of data visualization! Explore the booming market for data lenses, driving business intelligence and decision-making. Discover key trends, leading companies, and growth projections for this dynamic sector. Learn how data visualization is transforming industries and boosting efficiency.

The Data Visualization Tools Market size was valued at USD 10.39 billion in 2023 and is projected to reach USD 22.12 billion by 2032, exhibiting a CAGR of 11.4 % during the forecasts period. This unprecedented growth is attributed to the increasing demand for real-time data analysis, the need for effective decision-making, and the rising adoption of cloud-based data visualization tools. Additionally, government initiatives aimed at improving data literacy and the implementation of data visualization solutions in various industry verticals are further fueling market growth. Data visualization tools enable businesses and individuals to transform complex data into insightful visual representations. Tools like Tableau, Power BI, and Google Data Studio offer user-friendly interfaces to create interactive charts, graphs, and dashboards. They help analyze trends, patterns, and correlations, aiding decision-making processes. Advanced features include real-time data updates, collaboration capabilities, and integration with various data sources like databases and cloud services.

Data Visualization Training Market Outlook

According to our latest research, the global Data Visualization Training market size in 2024 stands at USD 1.12 billion, driven by the accelerating adoption of analytics and business intelligence across all sectors. The market is projected to expand at a robust CAGR of 12.6% from 2025 to 2033, reaching a forecasted value of USD 3.27 billion by 2033. This remarkable growth is primarily fueled by the increasing need for data-driven decision-making, the proliferation of big data, and the rapid evolution of visualization tools and platforms, which collectively are transforming how organizations and individuals interpret and communicate complex data.

The primary growth factor for the Data Visualization Training market is the surging demand for actionable insights from vast data sets. As organizations across industries embrace digital transformation, the ability to quickly interpret and communicate data findings has become a crucial skill. Data visualization bridges the gap between complex analytics and actionable business intelligence, making it indispensable for decision-makers. Furthermore, the widespread adoption of self-service BI tools and the integration of artificial intelligence into analytics platforms have heightened the need for robust training programs. Enterprises are increasingly investing in upskilling their workforce to maximize the value of their data assets, driving demand for both foundational and advanced data visualization training solutions.

Another significant growth driver is the rapid expansion of remote work and online learning modalities. As enterprises and educational institutions transition to hybrid and remote environments, the need for accessible, flexible, and scalable training solutions has soared. Online data visualization training programs, including on-demand courses, virtual workshops, and certification tracks, are experiencing unprecedented uptake. This shift is particularly pronounced in technology-driven sectors and among global organizations seeking to standardize data literacy across distributed teams. The trend toward continuous learning and professional development further amplifies the market's growth trajectory, as individuals seek to enhance their employability and organizations strive to maintain a competitive edge in an increasingly data-centric landscape.

The evolution of data visualization tools and software platforms is also a pivotal factor shaping market growth. Leading solutions such as Tableau, Power BI, Qlik, and Google Data Studio are constantly introducing new features and integrations that require specialized training for optimal utilization. As these tools become more sophisticated, the demand for tailored training programs that address specific functionalities, industry use cases, and advanced analytics techniques is rising. Additionally, the democratization of data access within organizations is empowering non-technical users to engage with data visualization tools, further broadening the target audience for training providers. This convergence of technological innovation and workforce empowerment is expected to sustain robust market growth throughout the forecast period.

From a regional perspective, North America currently leads the global Data Visualization Training market, underpinned by a mature analytics ecosystem, high digital literacy, and strong enterprise investment in upskilling initiatives. However, Asia Pacific is emerging as a high-growth region, propelled by rapid digitalization, the proliferation of tech startups, and increasing government emphasis on data-driven governance and education. Europe follows closely, benefiting from stringent data regulations and a robust corporate training infrastructure. Meanwhile, Latin America and the Middle East & Africa are witnessing steady growth as organizations in these regions gradually adopt advanced analytics and visualization solutions. The interplay of regional dynamics, technological adoption, and market maturity will continue to influence the global competitive landscape and growth opportunities in the coming years.

Training Type Analysis

The Data Visualization Training market is segmented by training type into Classroom Training, Online Training, Corporate Training, and Workshops & Seminars. Each modality addresses unique learning preferences and organizational requirements, contributing to the market's overall diversity and res

According to Cognitive Market Research, the global Business Intelligence market size is USD 16.9 million in 2023 and will expand at a compound annual growth rate (CAGR) of 9.50% from 2023 to 2030.

The demand for Business Intelligence s is rising due to the increasing data complexity and rising focus on data-driven decision-making.
Demand for adults remains higher in the Business Intelligence market.
The Business intelligence platform category held the highest Business intelligence market revenue share in 2023.
North American Business Intelligence will continue to lead, whereas the Asia-Pacific Business Intelligence market will experience the most substantial growth until 2030.

Growing Emphasis on Data-Driven Decision-Making to Provide Viable Market Output

In the Business Intelligence Tools market, the increasing recognition of the strategic importance of data-driven decision-making serves as a primary driver. Organizations across various industries are realizing the transformative power of insights derived from BI tools. As the volume of data generated continues to soar, businesses seek sophisticated tools that can efficiently analyze and interpret this information. The ability of BI tools to convert raw data into actionable insights empowers decision-makers to formulate informed strategies, enhance operational efficiency, and gain a competitive edge in a data-centric business landscape.

In June 2020, SAS and Microsoft established a comprehensive technology and go-to-market strategic alliance. As part of the collaboration, SAS's industry solutions and analytical products will be moved to Microsoft Azure, SAS Cloud's preferred cloud provider.

Source-news.microsoft.com/2020/06/15/sas-and-microsoft-partner-to-further-shape-the-future-of-analytics-and-ai/#:~:text=and%20SAS%20today%20announced%20an,from%20their%20digital%20transformation%20initiatives.

Rise in Adoption of Advanced Analytics and Artificial Intelligence to Propel Market Growth

Another significant driver in the Business Intelligence Tools market is the escalating adoption of advanced analytics and artificial intelligence (AI) capabilities. Modern BI tools are incorporating AI-driven functionalities such as machine learning algorithms, natural language processing, and predictive analytics. These technologies enable users to uncover deeper insights, identify patterns, and predict future trends. The integration of AI not only enhances the analytical capabilities of BI tools but also automates processes, reducing manual efforts and improving the overall efficiency of data analysis. This trend aligns with the industry's pursuit of more intelligent and automated BI solutions to derive maximum value from data assets.

In March 2020, IBM created a new, dynamic global dashboard to display the global spread of COVID-19 with the assistance of IBM Cognos Analytics. The World Health Organization (WHO) and state and municipal governments provide the COVID-19 data displayed in this dashboard.

Source-www.ibm.com/blog/creating-trusted-covid-19-data-for-communities/

Market Dynamics of the Business Intelligence tool Market

Key Drivers for Business Intelligence tool Market

Increasing Demand for Data-Driven Decision Making Across Various Sectors: As companies produce vast amounts of data, there is an escalating requirement for tools that can analyze and convert raw data into actionable insights. Business Intelligence (BI) tools facilitate quicker and more precise strategic decisions in areas such as sales, finance, operations, and customer service.

Transition to Cloud-Based BI Solutions for Enhanced Scalability and Accessibility: Organizations are progressively shifting from on-premise BI systems to cloud-based solutions, which provide real-time access, foster collaboration, and reduce infrastructure expenses. This transition enhances scalability and accommodates hybrid or remote work settings.

Incorporation of AI and Machine Learning for Enhanced Predictive Analytics: Sophisticated BI tools are incorporating artificial intelligence and machine learning technologies to deliver predictive forecasting, anomaly detection, and natural language querying—thereby improving the accuracy of business forecasts and enhancing user accessibility.

Key Restraints for Business Intelligence tool Market

High Initial Setup and Customization Costs for SMEs: Small and medium-sized...

Discover the booming market for Designing Data Visualization Services! Explore a detailed analysis of market size, growth trends (CAGR 15%), key players (Tableau, Microsoft, Innominds), and regional insights. Learn how data visualization is transforming businesses and unlock growth opportunities in this dynamic sector.

The Data Lens (Visualizations of Data) market is experiencing robust growth, driven by the increasing need for businesses to derive actionable insights from complex datasets. The market, currently estimated at $50 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033. This growth is fueled by several key factors, including the rising adoption of cloud-based analytics platforms, the proliferation of big data, and the growing demand for data-driven decision-making across diverse industries. Businesses are increasingly recognizing the importance of visualizing data to identify trends, patterns, and anomalies, leading to improved operational efficiency, enhanced strategic planning, and better customer understanding. The market is segmented by various software solutions, including business intelligence platforms (like Tableau, Sisense, and Qlikview), data visualization tools (such as Plotly and Chartio), and specialized analytics platforms from vendors like Alteryx and IBM. The competitive landscape is dynamic, with established players and innovative startups vying for market share through continuous product development and strategic partnerships. The continued expansion of the Data Lens market is expected to be further propelled by advancements in artificial intelligence (AI) and machine learning (ML), which are enhancing the capabilities of data visualization tools. AI-powered features such as automated insights generation and predictive analytics are transforming how businesses interact with and interpret their data. Geographic expansion, particularly in emerging economies, is another significant growth driver. However, challenges remain, including the need for skilled data analysts to effectively utilize these tools and the complexity associated with integrating diverse data sources. Nevertheless, the overall outlook for the Data Lens market remains highly positive, indicating a sustained period of substantial growth and innovation throughout the forecast period.

The Business Intelligence (BI) analysis software market is booming, driven by big data and cloud computing. Discover key trends, growth projections (2025-2033), leading companies (Microsoft, Tableau, SAP, etc.), and regional market shares in our comprehensive analysis. Learn how BI is transforming decision-making across industries.

Discover the explosive growth of the data visualization market, projected at a 15% CAGR to reach $153 billion by 2033. This in-depth analysis reveals key trends, leading companies like Tableau and Sisense, and regional market breakdowns. Learn how data visualization is transforming business intelligence.

Sales_Analysis_Project

Graded Power BI Mini Project - Sales Analysis Using Power BI | Data Science Course

🎓 Data Science & Business Analytics Program – IIT Guwahati (via Emeritus)

Objective

Design and develop an interactive dashboard using Power BI to analyze customer order data. This includes deriving insights related to:

• ✅ Total order volume
• ✅ Sales trends by product category
• ✅ Regional customer segmentation
• ✅ Monthly growth patterns
• ✅ Key customer behavior metrics

Learning Outcomes

• Translate complex datasets into engaging, data-driven stories
• Build structured, audience-focused narratives to support strategic decisions
• Apply visualization and DAX techniques to highlight business KPIs

🗂️ Dataset Components

🧑 Customers Table

Captures demographic & regional attributes: - CustomerID, Name, Age, Gender, Region, CustomerSince, Email

🛒 Orders Table

Captures purchase activity: - OrderID, CustomerID, ProductID, OrderDate, Quantity, TotalSales

📦 Products Table

Provides product and pricing metadata: - ProductID, ProductName, Category, UnitPrice, Supplier

Power BI Implementation

🔗 Data Modeling

• Built relationships between Customers, Orders, and Products
• Created a star schema for efficient filtering and aggregation

Visualizations Created

DAX Measures

• Average Order Value
• Monthly Sales Growth (%) = (Current - Previous) / Previous

Design & Formatting Strategy

• Used theme-consistent color palettes and data labels
• Structured layout to support intuitive storytelling
• Added headers, custom tooltips, and cleaned slicers
• Saved as: Sales_Analysis_Dashboard.pbix

Skills & Tools Applied

Reflections

This project deepened my ability to merge technical BI skills with business storytelling. By transforming raw sales and customer data into visual narratives, I learned how to:

• Identify drivers of revenue by product and region
• Segment and profile customers by behavior and demographics
• Present actionable insights to stakeholders in a concise format

“Data becomes meaningful when it tells a story that leads to better decisions.”

Crafted with ♥ by Kanak Baghel | LinkedIn