Glossary

Data trustworthiness refers to the degree to which financial data can be considered reliable, credible, and safe to use for decision-making. In finance operations, trust in data is not implicit; it must be earned through validation, consistency, and transparency across systems and processes.

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When data is not trustworthy, finance teams compensate with manual checks, parallel spreadsheets, and informal verification routines. These workarounds slow down operations and introduce additional risk, as decisions are made on data that is only partially verified or understood.

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Trustworthy data is the result of continuous validation. It is aligned across systems, reconciled against authoritative sources, and traceable back to original documents. Most importantly, it behaves consistently over time, allowing finance teams to rely on it without repeated confirmation.

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This level of trust is increasingly achieved through AI agents that monitor data quality in real time and enforce validation rules at scale. At Phacet, data trustworthiness is a core outcome of its control-first approach, notably across workflows such as 3-way matching, where unreliable data directly impacts payments and margins.