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Zeeshan Akram
• 2d
Why I treat my Data Analysis like a Software Engineering project.
Most dashboards fail because they are built on a "black box." When I tackle an analysis, like my recent work on a £17.3M retail dataset, I apply a software engineer’s rigor to the data lifecycle:
Version Control: Every SQL query and Python cleaning script is versioned in GitHub. If a number changes, I can tell you exactly which line of code changed it. (View the Project here)
Defensive Logic: In my RFM models, I don’t just use defaults. I engineer custom thresholds to isolate high-value "Champions" (worth 40x more than at-risk users) to ensure marketing capital isn't wasted.
Data Provenance: I document every schema constraint and "blindspot"—like the 23% missing guest IDs I identified in my latest audit, to ensure leadership makes decisions on facts, not assumptions.
I'm a Data Analyst who believes that a dashboard is only as good as the engineering behind it.
If you’re a founder who needs more than just "pretty charts," let’s talk about building a reliable source of truth for your revenue.
Data Analysisgrowthstrategyrevenueoperations
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