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Organizations struggle to fully leverage the data-driven economy due to several challenges. Data quality issues often lead to poor results in software development and machine learning (ML) and generative AI (GenAI) projects, requiring significant time to fix gaps and biases. This feeds into the data preparation bottleneck, consuming valuable resources as teams gather, de-identify, and augment data. Furthermore, ML and GenAI models demand large, varied datasets that are often unavailable, leading to further project and ultimately innovation delays. Contributing to each of the challenges listed above is data privacy; all sensitive information must be protected, and ideally removed from data before it is shared.
Figure 1: Musts for the Data-Driven Organization
Organizations need an efficient way to prepare test data for their increasing needs in the data-driven economy. Data must be stored and used in privacy-compliant ways, become more portable, and the data preparation phase of projects must become less effort- and time-intensive.
