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OLUWADAMILARE WILLIAMS 🥂😊

@its_justdami

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Data Analyst 📊 SQL | Excel | Power BI
Turning data into insights
Building in public 🚀

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My First Post on DATA📊 by @its_justdami

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15

a year ago

Another Day 🥂 by @its_justdami

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10 days ago

₦37,000,000 moved through my account.
But I almost saved nothing.

I analyzed 2,480+ transactions from my 2025 OPay dataset using Power BI.

Here’s what the data revealed:

• ₦37.29M Income
• ₦37.39M Expenses
• ₦32.6M spent on Transfers alone
• Spending peaked between May – August

On paper, it looks impressive.

In reality?

I was operating on a near break-even cycle.
87% of my expenses were transfers.

Money was moving.
But it wasn’t multiplying.

That’s the illusion of high cash flow.

This analysis forced me to ask better questions:
• Why are transfers dominating expenses?
• What percentage of my income is retained monthly?
• Is my spending seasonal or strategic?

Data doesn’t just show performance, It exposes behavior
And behavior determines growth.

If you moved ₦37M in a year…
Would your savings reflect it?

#PowerBI #DataAnalytics #FinancialAnalysis #PersonalFinance by @its_justdami

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8 months ago

Stop guessing, let SQL tell you where the money is really coming from 📊

I recently completed an exploratory data analysis using SQL to uncover what truly drives business performance.

Instead of surface level analysis, I focused on answering key business questions:

• Which region and country perform best?
• What are the top-selling products?
• Which customer segment generates the highest revenue?
• How do core metrics — revenue, cost, profit, and quantity — compare across the business?

To go a step further, I ranked products by profit, not just by sales volume, to identify which products actually contribute the most value.

Key insight:
High sales ≠ high profitability.
Some products generate strong revenue but deliver low profit once costs are considered.

This project reinforced how SQL can transform raw transactional data into clear, actionable business insights that support smarter decision-making.

Tools used: SQL
Skills applied: EDA, aggregation, ranking, business analysis.

#dataanalytics #datavisualization #viral by @its_justdami

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8 months ago

Ulcers aren’t random, the data exposed patterns most people miss

After analyzing ulcer patient data and building an interactive Power BI dashboard, clear patterns emerged:

What the data shows
• Most patients are over 50
• BMI levels suggest lifestyle factors play a major role
• NSAIDs appear repeatedly across patient histories
• Many cases are diagnosed late, when ulcers are already deep

Likely causes
• Prolonged NSAID and aspirin use
• Delayed medical check-ups
• Poor dietary habits and stress
• Untreated recurrent ulcers

What this data recommends
• Early screening for high-risk age groups
• Review and regulate long-term NSAID use
• Lifestyle and diet-focused interventions
• Data-driven monitoring for early detection

This is why healthcare needs data-driven decisions, not guesswork.
Early insights can change outcomes

#dataanalysis
#healthcaredata
#datavisualization by @its_justdami

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8 months ago

📊 Absenteeism Trend Analysis |

Turning Workforce Data into Actionable Insights.

I built an Absenteeism Analytics Dashboard to answer one critical business question:

What is driving employee absenteeism and how can organizations reduce productivity loss through data-driven decisions?

What this dashboard enables leaders to understand at a glance:
• How absenteeism varies across age groups, seasons, and months
• The primary reasons employees are absent
• Which employee segments contribute most to lost hours
• How health indicators (BMI) and education level relate to absenteeism patterns

Key insights uncovered:
• 4,000+ absent hours recorded across 36 employees, signaling a measurable productivity impact
• Employees aged 27–36 show the highest absenteeism rate, followed by the 37–46 group
• Medical reasons account for the majority of absenteeism cases, far outweighing other causes
• Spring and Summer record the highest absenteeism levels, indicating seasonal workforce risks
• Employees categorized as obese or overweight contribute a significant share of absence cases
• Absenteeism is most prevalent among employees with high school–level education, highlighting a potential area for targeted interventions

How I approached the analysis:
• Transformed raw HR data into decision-ready KPIs
• Analyzed absenteeism across time, demographics, and health indicators
• Used Power BI to design an intuitive, executive-friendly dashboard
• Focused on insight generation, not just visualization

Why this matters to the business:
This analysis helps HR teams and leadership:
• Reduce absenteeism-related productivity loss
• Design targeted wellness and preventive health programs
• Improve workforce planning and operational efficiency
• Make proactive, data-backed people decisions.

If you’re interested in HR Analytics, Workforce Optimization, or Power BI storytelling, let’s connect 🤝
Feedback is always welcome.

#DataAnalytics #HRAnalytics #WorkforceAnalytics #PowerBI #PeopleAnalytics by @its_justdami

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8 months ago

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8 months ago

Just completed my first SQL project!
I went from zero to querying like a pro, exploring databases, joining tables, and finding insights that actually matter.

If you’ve ever wanted to see how SQL can turn raw data into answers, this is for you.
Fun fact: I learned how to filter, sort, and analyze data just like the pros do!

Can’t wait to dive deeper and build even cooler projects!
#SQL #DataAnalytics #FirstProject #viral by @its_justdami

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8 months ago