Here is my Q3 2025 Dashboard 📊
3 pages:
Overview
By Commodity
By Region
Built with:
✅ Power Query for data cleaning
✅ DAX measures and calculations
✅ Data modeling and relationships
✅ Conditional formatting
✅ Interactive slicers
#TechInNigeria#NigeriaEconomy#Nigeria
I just built TWO Power BI dashboards
analyzing Nigeria's Commodity Price
Indices & Terms of Trade 🇳🇬📊
Q3 2025 ✅
Built from raw NBS Excel data with:
⚡ Frequent power outages
💻 Weak laptop
📚 Zero prior Power BI experience
Here is everything I learned 👇
Thread 1/10🧵
📌 PART 1 : How Data Analysts Collect Company Data (Beginner-Friendly Guide)
Lately I’ve been talking to a lot of new analysts who feel confused about what to actually do when a company says:
“Help us analyze our data.”
I’ve been there, unsure of where to start, what questions to ask, or how real analysts collect business data.
So today, I want to break it down in the simplest way possible.
If you’re a beginner, read this carefully and save it.
1️⃣ Start With a Requirements Discovery Call
Before touching Excel, SQL, or Power BI, your first job is to understand the business, not the dataset.
Ask the company questions around:
Business Goals
• What problem are we solving?
• What decisions will this analysis improve?
• What does success look like?
Data Needs
• What data sources exist?
• Where is the data stored (Excel, SQL, CRM, POS)?
• What time period should be analyzed?
Output Expectations
• Dashboard, report, or cleaned dataset?
• Which KPIs matter the most?
• Should the report update weekly or monthly?
Access & Security
• Will you need login access?
• Any sensitive columns to anonymize?
This is how professionals avoid confusion and build trust early.
2️⃣ Ask for the Right Data Files
Depending on the industry, request the correct tables:
Retail / E-commerce
Orders, Customers, Products, Inventory, Returns.
Finance
Transactions, Ledger, Forecasts, Budgets.
Healthcare
Appointments, Billing, Encounters, Lab results.
HR
Employees, Payroll, Hiring funnel, Performance.
Always request the files in Excel or CSV, with proper column names.
And ask for a data dictionary, it explains what each column means.
👉 If this helped you, watch out for Part 2 where I’ll break down cleaning, analyzing, and delivering insights like a pro.