Samuel Maina
Freelance Data Analyst | SQL, Excel, and Power BI Expert.
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Security Support Information System (SSIS)
The Security Support Information System (SSIS) is an end-to-end Business Intelligence solution developed to address operational visibility and decision-making challenges in the private security industry.
The project involved identifying a real business problem, cleaning and transforming operational datasets using Python (Pandas), designing a relational MySQL database, implementing SQL scripts, and developing interactive Power BI dashboards.
The final solution delivers Executive, Workforce, Operations, and Commercial Analytics dashboards featuring KPI reporting, DAX calculations, and interactive visualizations to help managers make informed, data-driven decisions.
Tools Used: Python (Pandas), MySQL, SQL, Microsoft Power BI, DAX, Excel.
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Bakery Sales Dashboard: Power BI
Walkthrough of a full sales analytics project for a bakery chain with 14 store locations. The raw data arrived across two separate tables (sales and price) with duplicates and no clean relationship between them I used SQL to handle the join and cleanup before anything touched Power BI.
A deeper issue turned up in the location data: cities and states were mixed into the same field, which would have broken any regional reporting. Fixed that at the source before building out the model.
What the dashboard delivers:
Revenue, transaction, and unit tracking across $44.8M in sales
Product performance broken down by time of day, surfacing which items to keep in stock and which to reconsider
Store-level benchmarking that flags underperforming locations against top performers like Florida
A dedicated Insights page with findings and recommendations, not just charts I also built for someone with zero Power BI experience to open and understand immediately
Tools: SQL, Power Query, Power BI, DAX
Video above walks through the full dashboard, page by page.
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Amazon Sales Performance Dashboard: SQL to Excel Pipeline
This project showcases an end-to-end data analytics pipeline designed to clean, structure, and visualize massive retail transactional data.
The Raw Data: Processed over 113,000 messy e-commerce records tracking sales, logistics, and customer details.
The SQL Cleanup: Cleaned the database by removing duplicate transactions to protect metric accuracy, standardizing product categories, and handling missing values.
The Staging Step: Created structured relational views to ensure fast, lag-free dashboard performance.
The Interactive Excel Dashboard: Developed a premium, dark-themed interactive Excel interface tracking $75.40M in Revenue, 113.7K orders, top states (like Maharashtra), and critical metrics like the 4.95% cancellation rate. Slicers allow users to instantly filter by product size, month, and order status.
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This interactive retail dashboard was developed to help a large e-commerce store track their sales performance, monitor profit margins, and look for shipping bottlenecks. The project covers everything from cleaning the raw data in SQL to designing a clean, user-friendly business layout.
Technical Workflow & Implementation
SQL Data Cleaning: Used SQL to do all the heavy lifting. I wrote queries to clean the raw dataset, handle date formats, organize the tables, and ensure there were zero duplicates or broken rows.
Excel Data Checks: Used Microsoft Excel to spot-check specific settings, audit row counts, and make sure the numbers matched perfectly before moving them into the final report.
UI/UX Dashboard Design: Designed a high-end, clean dashboard interface. Instead of using standard grid cells, I built the entire layout using custom floating shapes to create seamless containers and eliminate all white spaces, giving it a premium app-like feel.
Dashboard Page-by-Page Breakdown
1. Sales Overview Page
This is the main dashboard page that gives an instant snapshot of how the business is performing globally.
Main Sales KPIs: Displays high-level cards for total sales revenue, total profit margins, and overall order volumes.
Interactive Date Slicers: Features smooth calendar and order date controls so users can slice data by specific years, quarters, or months to see seasonal trends.
Cross-Filtering Map: Includes a dynamic regional map where clicking on any state or territory instantly updates the entire page's metrics.
2. Product Performance Page
This page lets management dive deep into the actual inventory and see what is making money versus what is losing it.
Category & Sub-Category Splits: Breaks down sales and profit performance by categories like Technology, Office Supplies, and Furniture.
Profit Margin Analysis: Highlights low-margin or negative-profit products so the store knows exactly where they are losing money.
Top Selling Products: Visualizes the best-performing items by revenue and order volume to keep track of high-demand stock.
3. Shipping & Logistics Page
This page focuses heavily on operations, tracking how efficiently orders are getting sent out to customers.
Shipping Mode Breakdown: Compares sales and volumes across different shipping methods, like Standard Class vs. Same Day delivery.
Order Delays & Timelines: Tracks the average time it takes for an order to move from the purchase date to the actual ship date.
Regional Logistics Tracking: Lets the operations team filter down by region or state to see where shipping bottlenecks or delivery delays are happening the most.
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