Without appropriate data validation, errors can remain hidden in the dataset and affect the research results.

Potential consequences include:

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• Additional costs - correcting data problems late in the project can require reprocessing, additional analysis, further checks, or even renewed data collection.

• Delays in reporting - unexpected problems discovered shortly before delivery can delay tabulation, analysis, reporting, and the final presentation of results.

• Loss of the research window - some research projects are tied to a specific period, such as an active advertising campaign, product launch, election, event, or other time-sensitive activity. If data problems are discovered too late, there may no longer be enough time to correct them while the research window is still relevant.

• Inaccurate findings - errors or inconsistencies in the data can affect calculations, tables, analysis, and conclusions.

• Reduced confidence in the results - clients and research teams may have less confidence in findings if data quality issues are discovered late in the process.

• Additional pressure on project teams - urgent corrections can create unnecessary pressure for researchers, analysts, programmers, and other teams working toward a reporting deadline.

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Early data validation helps identify potential issues while there is still time to investigate and address them.