The greatest concern for the risk practitioner when a big data project has resulted in the creation of an application used to support important investment decisions is the data quality. Data quality is the degree to which the data is accurate, complete, consistent, reliable, relevant, and timely. Data quality is essential for the success of any big data project, as it affects the validity and reliability of the analysis and the outcomes. Poor data quality could lead to erroneous or misleading results, which could have negative consequences for the investment decisions and the organization’s performance and reputation. The other options are not as concerning as the data quality, although they may also pose some challenges or risks for the big data project. Maintenance costs, data redundancy, and system integration are all factors that could affect the efficiency and effectiveness of the big data project, but they do not directly affect the accuracy and reliability of the analysis and the outcomes. References = Risk and Information Systems Control Study Manual, Chapter 3, Section 3.3.1, page 3-20.