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Automating an MIS Pack: Reconciliation and Exception Handling

Automating an MIS Pack: Reconciliation and Exception Handling
Datta Sable
Datta Sable
BI & Analytics Expert

Write the manual process down

For a fictional weekly pack, list file collection, period selection, reconciliation, report generation and review. Record who supplies the files and who approves the result. Do not describe every minute of the current process as removable: some checks may remain necessary after automation. This walkthrough does not assert that a client saved 400 hours per month.

Use a control sheet

Take four fictional amounts of 40, 60, 25 and 75. The approved batch total is 200 and the row count is four. Store these controls with the batch identifier. When preparing a report, compare both values before release. A total of 200 alone would miss one duplicated 25 and one missing 25; a useful reconciliation also checks keys and exception counts.

batch: weekly-example-01
expected rows: 4
expected amount: 200
release conditions: unique row keys, no blocking exceptions, reviewer approval

Separate preparation and distribution

Generate a draft first. Keep source errors in an exception list with enough context to correct them, and do not convert invalid amounts to zero just to finish the job. A reviewer should approve a specific version rather than an unversioned folder location. Give distribution a durable key so rerunning preparation does not resend the same pack repeatedly.

Measure the complete workflow

Compare actual collection, investigation, review and maintenance effort for equivalent reporting periods. If a routine step gets faster but exceptions take longer to diagnose, include that extra work. Separate released capacity from verified payroll or budget savings. Keep the baseline and observation window with the result so a later case study can be supported by evidence instead of a promotional headline.

Primary references

Technical References & Standards

Datta Sable
Author

Datta Sable

Senior BI Developer & Data Architect with over 10 years of experience in engineering high-fidelity analytics systems. Specialized in Tableau, Power BI, SQL, and Python-driven automation for enterprise-grade decision clarity.