Client Challenge
The client approached me with a critical reporting limitation. Their core system stored only the latest state of data, continuously overwriting historical values. While this worked for day-to-day reporting, it made trend analysis impossible.
They specifically needed a way to capture key business metrics on a monthly basis without changing the source system, building new database tables, or introducing IT-heavy solutions. The request was clear: an automated, reliable, and cost-effective way to preserve historical data.
Current State
In the existing setup, a database table was overwritten with every update. Historical values were permanently lost, which meant:
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No month-over-month or long-term trend analysis
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No historical baseline for performance comparison
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No visibility into how KPIs evolved over time
Capturing monthly metrics manually would require either ongoing analyst effort or custom database development, both introducing unnecessary cost and complexity for a relatively simple requirement.
Automated Solution
Instead of modifying the database, I implemented a lightweight automation using Power BI and Power Automate.
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Power BI acted as the calculation and validation layer for required metrics
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On the first day of each month, Power Automate:
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Extracts the required metrics from a Power BI table
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Creates a monthly snapshot file
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Uploads it automatically to SharePoint
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Notifies stakeholders that the data is available
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These monthly files are then imported back into Power BI, creating a growing historical dataset used for trend analysis and reporting.
No new databases were added. No manual exports were required. The process runs fully automatically.
Business Impact
After automation, the organization gained:
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Full historical visibility of key KPIs
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Reliable month-over-month trend analysis
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Zero risk of missed or inconsistent snapshots
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No dependency on IT teams or manual processes
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A scalable solution that grows automatically each month
Most importantly, historical data now exists where it never existed before.
Cost Savings and ROI
What Was Avoided
A traditional enterprise approach would typically require:
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New database tables or ETL jobs
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10 to 15 days of data engineering work
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Ongoing monitoring and maintenance
At an average cost of €500 per day, this equals:
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€5,000 to €7,500 in development costs
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€720 to €1,440 per year in maintenance (1 to 2 hours per month at €60 per hour)
Manual Work Eliminated
Manual monthly snapshotting would realistically take 2 to 3 hours per month, or:
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24 to 36 hours per year
At an analyst rate of €35 per hour:
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€840 to €1,260 per year
Total Yearly Savings
By eliminating:
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Database development
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Ongoing maintenance
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Manual analyst work
This automation delivers €6,500 to €10,200 in savings per year, excluding additional benefits such as better decisions, faster insights, and reduced operational risk.
Final Takeaway
This use case demonstrates how Power BI and Power Automate can act as a lightweight data engineering layer, even when source systems do not store historical data.
By automating monthly snapshots:
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Historical data is preserved without changing core systems
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Reporting becomes more powerful and future-proof
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Costs and complexity are kept to a minimum
This is the type of problem I solve: turning data limitations into scalable, automated solutions that deliver long-term business value without overengineering.