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Big data applications can enhance the market competitive advantages of enterprises and organizations and can improve people's quality of life. However, by the impact of many factors, failure rate of big data project is higher than the IT project. In order to reduce the risk of failure, big data projects must overcome a serial of challenges. Ambiguous requirements, poor data quality, and lacking changeability and extensity will directly affect the results of big data analytics. And even cause the wrong decision, inaccurate prediction and improper planning to make the big data projects with potential high risk. For this, this paper migrates iterative and incremental development (IID) features to the data preprocessing, and draws up the iterative and incremental data quality improvement (IIDQI) procedure. IIDQI procedure applies data preprocessing task frame to repeatedly detect and identify the defects of data quality, and incrementally strengthen big data quality and control the factors of failure risk. Iterative inspection activities can effectively enhance data quality, intercommunication efficiency, and precision requirement and objective to reduce the risk of big data project failure.

An Iterative and Incremental Data Quality Improvement Procedure for Reducing the Risk of Big Data Project