TOS006

Advanced Analytics vs Data Quality

Business ContextAdvanced analytics can identify patterns, relationships, anomalies, and emerging exposures that conventional testing may miss. Analytical sophistication cannot compensate for incomplete, inaccurate, inconsistent, poorly structured, or incorrectly interpreted source data.

Audit TRIZ ResolutionIntegrate data-quality validation into the analytical process rather than treating it as a separate preliminary exercise. Automated profiling, reconciliation, lineage checks, completeness testing, and exception analysis should determine whether data are sufficiently reliable for each intended analytical use.

Applicable TRIZ Principles

Principle 10 – Prior Action validates critical data characteristics before analytical conclusions are generated.

Principle 23 – Feedback identifies and corrects data-quality weaknesses revealed during analysis.

Principle 3 – Local Quality applies data-quality requirements according to the significance of the intended audit conclusion.

Expected Outcome

More reliable analytics

Earlier detection of data problems

Fewer misleading conclusions

Better use of advanced techniques

Decision Indicators

Sophisticated models operate on poorly understood datasets.

Analytical results change materially after data corrections.

Auditors cannot trace important data to authoritative sources.

Data preparation consumes more time than expected on every engagement.

Model sophistication receives greater attention than input reliability.