Data Strategy
The hidden cost of dirty financial data
Most reconciliation pain is really a data-quality problem in disguise. We quantify the drag and show how standardization at ingestion pays for itself.
Ask a controller why the close ran late and the honest answer is rarely "the matching was hard." It is that the data showed up in twelve formats, three date conventions, and descriptions no two systems agree on.
Dirty data is a tax on everything downstream. It inflates exception counts, erodes trust in automation, and forces manual cleanup that never scales. I have watched good teams lose whole days to it.
This is well documented. Deloitte and EY consistently cite data quality as the number one barrier to finance automation, and PwC's data governance research makes the same point: automation only pays off when the inputs are clean. KPMG puts it bluntly — garbage in, garbage out.
Standardizing at the point of ingestion — normalizing dates, amounts, and descriptions before matching begins — removes the tax at its source. In our own deployments, standardization alone cut exception volume by roughly a third before any AI matching even ran.
See it in practice
Recon Pilot puts these ideas to work with autonomous matching, explainable decisions, and audit-ready sign-off.
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