A serious problem caused by one or more ineffective data analysis processes. In addition to the financial burden, problems with data quality and analysis can have a serious impact on security, compliance, project management and human resource management, among others. Error can creep into data analytics at any stage. The data quality may be inadequate in the first place. The data could be incomplete, inaccurate, not current, or may not be a reliable indicator of what they are intended to represent. Data analysis and interpretation are prone to a similar number of pitfalls. There can be confounding factors and the mathematical method can be flawed or inappropriate. Correlation can be erroneously considered to suggest causation. Statistical significance may be mistakenly attributed when the data do not support it. Even if the data and analytic processes are valid, data may be deliberately presented in a misleading manner to support an agenda. Problems arise when insufficient resources are applied to data processes and too much confidence placed in their validity. To prevent data-driven disasters, it’s crucial to continually examine data quality and analytic processes, and to pay attention to common sense and even intuition. When data seem to be indicating something that does not make logical sense or just seems wrong, it is time to re-examine the source data and the methods of analysis.