I’m a BI Reporting & Analytics professional with experience across business intelligence, data analytics, automation, revenue assurance, fraud analytics, and operational reporting.
My work focuses on turning messy, fragmented, or underused data into clear, practical outputs that help businesses make better decisions. I enjoy building solutions that sit close to real business problems — dashboards, automated reporting pipelines, anomaly detection workflows, data quality checks, and analytics-ready datasets.
I currently work in the telecommunications industry, where I support Revenue Assurance & Fraud Management through analytics, automation, and data-driven controls. My experience includes working with tools such as Power BI, SQL, Python, Excel, Power Automate, Power Apps, Databricks, and Tableau.
This GitHub is where I document portfolio projects that demonstrate practical, client-facing data solutions.
- Build Power BI dashboards and reporting solutions
- Clean, transform, and prepare messy datasets for analysis
- Automate manual reporting and operational processes
- Use Python and SQL to analyse business data
- Design data quality checks and assurance controls
- Build analytics workflows for revenue assurance, fraud detection, and operational monitoring
- Document projects clearly so they are easy to understand, maintain, and hand over
Business Intelligence & Reporting
Power BI, Tableau, Excel, Dashboard Design, KPI Reporting, Operational Reporting
Data Analysis & Engineering
SQL, Python, Pandas, Data Cleaning, Data Transformation, ETL, Data Quality Checks
Automation & Workflow Tools
Power Automate, Power Apps, Python Automation
Data Platforms
Databricks, Trino SQL, MySQL, Delta Tables
Business Domains
Telecommunications, Revenue Assurance, Fraud Management, CRM Data Quality, Sales & Marketing Analytics
Goal:
To simulate a real-world client problem where a business has messy CRM data that needs to be cleaned, standardised, deduplicated, and prepared for sales outreach, direct mail, and marketing segmentation.
Business Problem:
Small and medium-sized businesses often have customer records spread across inconsistent CRM fields. Names, emails, phone numbers, company names, and postal addresses may be incomplete, duplicated, incorrectly formatted, or stored in the wrong place. This creates problems for sales teams, marketing teams, and direct mail campaigns.
Solution:
This project builds a Python-based data cleaning pipeline that takes a messy synthetic CRM dataset and produces a clean, campaign-ready customer file.
Key Features:
- Standardises customer names and company names
- Cleans email addresses and phone numbers
- Identifies missing or invalid contact details
- Detects duplicate customer records
- Flags incomplete postal addresses
- Creates a final cleaned dataset ready for direct mail or sales outreach
- Produces a data quality summary report
Skills Demonstrated:
Python, Pandas, Data Cleaning, CRM Data Quality, Deduplication, Data Validation, Business Rules, Documentation
Repository:
CRM Data Cleaning Project
Goal:
To simulate a real-world executive reporting use case where a subscription-based business needs to understand sales performance, customer growth, churn, revenue trends, customer segmentation, and product/plan performance.
Business Problem:
Subscription-based businesses often have customer, subscription, revenue, product, and churn data spread across different systems. Leadership teams need a single, trusted view of business performance so they can identify revenue trends, monitor customer growth, understand churn risk, and make faster strategic decisions.
Solution:
This project builds an executive Power BI dashboard using synthetic business data to transform raw customer, subscription, transaction, product, and churn records into a clean, decision-ready reporting solution.
Key Features:
- Tracks total revenue, monthly recurring revenue, and revenue trends
- Monitors active customers, new customers, lost customers, and churn rate
- Analyses customer growth and retention performance
- Breaks down revenue by product, plan, region, and customer segment
- Highlights churn patterns and cancellation reasons
- Uses a structured star schema data model for Power BI reporting
- Includes DAX measures for executive KPIs
- Provides dashboard pages for executive overview, revenue trends, churn analysis, customer segmentation, and product performance
Skills Demonstrated:
Power BI, DAX, Power Query, Data Modelling, Star Schema Design, KPI Development, Executive Dashboarding, Revenue Analytics, Churn Analysis, Customer Segmentation, Business Intelligence Documentation
Repository:
Coming Soon
I’m currently building portfolio projects focused on practical data problems that businesses commonly need help with, especially in areas such as:
- CRM data cleaning
- Reporting automation
- Power BI dashboards
- SQL-based business analysis
- Revenue assurance controls
- Fraud and anomaly detection
- Marketing and sales analytics
The goal of this GitHub is not just to show code, but to show how data can be used to solve real operational and business problems.
Date: 19 May 2026
Location: Microsoft Offices, Bryanston, Johannesburg
Repository: skills-getting-started-with-github-copilot
I attended Microsoft's GitHub Copilot Demo Day at their Bryanston offices, where I completed a hands-on GitHub Skills exercise focused on getting started with GitHub Copilot.
The exercise introduced practical ways to use GitHub Copilot as an AI-assisted development tool for code suggestions, debugging, testing, and documentation. Although this was a guided learning exercise, it forms part of my broader professional development in using AI tools to improve analytics, reporting, and automation workflows.
As a BI Reporting & Analytics professional, GitHub Copilot can support faster delivery of technical work by helping with:
- Python scripts for data cleaning and reporting automation
- SQL query drafting and refactoring
- Test case generation for analytical workflows
- GitHub documentation and README creation
- Code review and productivity improvements
- GitHub repository management
- GitHub Copilot prompting
- AI-assisted development workflow
- Python project exposure
- Testing workflow awareness
- Documentation and version control
Completed and published the GitHub Skills exercise repository as evidence of hands-on exposure to GitHub Copilot and AI-assisted development practices.
You can view my resume here:
MAISHA_LUVHANI_CV_2025_SENIOR_SPECIALIST.pdf
I’m open to collaboration, freelance projects, and data/analytics opportunities.
If you’re looking for someone who can bridge the gap between business problems and practical data solutions, feel free to connect with me.