The excitement surrounding Artificial Intelligence (AI) is increasing every day. But, along with it, there comes a growing awareness of the crucial role of data quality. Data quality issues can stem from various factors, including missing data, human errors, weak data governance, and incorrect transformations. When companies aim to utilize their data for AI model development, low-quality data can significantly restrict the potential value derived from such models.
Whether it’s AI or any other decision-making process, the evidence is the same: Data-driven decisions based on high-quality data are better decisions.
Implementing standards such as the FundsXML standard can enhance data quality. By following standardized data formats and validation rules, FundsXML users can minimize errors and inconsistencies in their data. This improves the training and performance of AI models, leading to more reliable predictions and ultimately to better decisions.