About
I am a detail-oriented Data Analyst focused on transforming messy, fragmented raw data into clean, structured, and actionable business intelligence. With a strong foundation in data architecture, advanced spreadsheet optimization, and functional programming, I specialize in building data pipelines that drive operational efficiency.<br>My approach to data centers on integrity and usability—ensuring datasets are normalized, lookups are structurally sound, and final interfaces are intuitive for business stakeholders. Whether automating repetitive data workflows or engineering multi-dimensional reporting dashboards, I bridge the gap between complex raw metrics and strategic decision-making.<br>Core Technical Toolset:<br>Data Manipulation & Reporting: Advanced Excel (Pivot Tables, Power Query, Data Modeling/Power Pivot, Slicers).<br>Advanced Lookups & Logic: XLOOKUP, INDEX/MATCH, Array Frameworks, Custom Sorting.<br>Programming & Scripting: Python (File Handling, Automation Scripts, Core Structural Logic).<br>Data Quality: Categorical Normalization, Error Diagnostics, Alpha-Numeric Data Cleansing.<br>I am eager to bring my analytical mindset, passion for clean data architecture, and technical problem-solving skills to a forward-thinking data or business intelligence team.<br>
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Skills
Ingestion
Normalization
BI/Data Model Tools
Excel PowerPivot
Data Visualization/Analytics Tools
Power BI
Operating Systems
Windows 7, Windows 10, Windows Server 2010, Windows Server 2016, MAC, Android, IOS
Microsoft Office Suite
MS Excel, Word, PowerPoint, Outlook
Technology Management
Database Management, Communication Systems, IT Infrastructure
Leadership & Strategy
Visionary Leadership, Operational Strategy, Organizational Growth, Cross-functional Collaboration
Business Intelligence
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Projects
Multi-Category Family Financial Spending Analysis
The Objective: Built a comprehensive financial tracking and reporting model to analyze expenditure behaviors across multiple household categories, providing deep insights into cost distribution. Tools Used: Microsoft Excel, Advanced Lookup Arrays (VLOOKUP, INDEX/MATCH), Descriptive Statistics, Interactive Pivot Tables. Key Responsibilities & Implementation: Descriptive Statistical Modeling: Utilized statistical aggregations (COUNT, COUNTA, MAX, MIN, AVERAGE) to establish spending baselines, track transaction frequency, and isolate peak expenditure anomalies. Multi-Dimensional Data Retrieval: Engineered robust lookup frameworks using VLOOKUP to seamlessly extract specific item expenditures across family units. Matrix Intersection & Extraction: Deployed a dynamic 2D grid intersection model using INDEX and MATCH combined, bypassing traditional lookup limitations to pinpoint exact individual spend metrics across granular expense categories. Aggregated Business Intelligence: Summarized thousands of data points into a polished, high-level Pivot Table report to instantly visualize overall spending trends and departmental distributions. The Deliverable: Created an interactive, tabular financial intelligence dashboard that enables stakeholders to immediately identify top spending drivers, track averages, and audit individual cost centers in a single view.
Data Quality Optimization & Standardization Engine
Project Title: E-Commerce & Operations Data Standardization Project The Objective: Transformed a highly inconsistent, unformatted corporate dataset into a clean, reliable, and production-ready data source for business reporting. Tools Used: Microsoft Excel, Advanced Text Functions, Data Cleansing Filters. Key Responsibilities & Implementation: Data Type & Alpha-Numeric Correction: Identified and resolved critical data entry corruption errors where numbers were mixed with look-alike letters (e.g., converting corrupted text inputs like "5O" and "1S" back into true structural integers like 50 and 15), ensuring future lookup operations would not fail. Categorical Normalization: Standardized fragmented categorical fields by auditing and correcting inconsistent naming conventions (e.g., consolidating variations like "F", "Fe", and "Femeale" into a single normalized "Female" attribute, and "M" or "maele" into "Male"). Structural Integrity: Cleaned the data canvas by identifying and removing redundant blank rows and structural anomalies, significantly improving dataset presentation and processing efficiency. The Deliverable: Delivered a 100% standardized, error-free data asset ready for executive reporting, eliminating data discrepancies and preventing downstream analysis errors.
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Education
Business Administration
Babcock University
Accounting
Lagos State University
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Certificates
Association of Accounting Technicians West Africa (AATWA)
Association of Accounting Technicians West Africa (AATWA)
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Work
ISE-OLUWA TRANSPORT SYSTEM
Associate Accountant and Reconciliation Officer
Zion Expressions
Junior Accountant
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