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I built this Excel cash flow analysis to understand how a business's liquidity could change under different scenarios. I compared a Base, Adverse, and Critical scenario to see the impact on cash balances throughout the year and identify periods where liquidity could become more vulnerable.
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Your Excel dashboard can look impressive and still be completely useless.
A lot of people think a good dashboard means:
More charts.
More colors.
More KPIs.
More visuals.
But that's not what makes a dashboard useful.
A good dashboard should answer 3 questions quickly:
What is happening?
Why is it happening?
What should I do next?
For example, instead of showing 10 different sales charts, a useful dashboard could immediately highlight:
→ Sales are down 12%
→ The decline is mainly from Product A
→ Region X is responsible for most of the drop
That's the difference between displaying data and communicating insights.
Don't build dashboards to show how much you know about Excel.
Build them to make decisions easier.
P.S - Want to turn your raw data into an interactive Excel dashboard?
Send me a DM.
Clean Data. Better Decisions.
Good data work starts with accuracy.
I help organize, clean, and validate data so that spreadsheets and datasets are easier to use and trust.
From data entry and formatting to identifying duplicates and inconsistencies, I focus on keeping every detail clear and consistent.
Accuracy → Consistency → Reliable data.
Have data but don't know where to start?
Before analyze a dataset, I first understand what the data can actually tell me.
I usually start by looking at:
→ What each row represents
→ Which columns contain useful information
→ Missing or duplicate data
→ Inconsistent or unusual values
→ Patterns and trends worth investigating
From there, I can clean the data, explore it, and turn the numbers into findings that are easier to understand.
For example, customer or sales data can help uncover:
• Which products perform best
• Which customer groups contribute the most
• Where sales are declining
• Patterns in customer behavior
• Areas that may need attention
My approach is simple: understand the data → analyze it → find meaningful insights.
If you have a dataset that needs cleaning, analysis, or meaningful insights, feel free to reach out.
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