## 2 Materials and Methods
### Dimensions of Data Challenges for AI
#### 2.2.1 Dimension I: Data Quality
Implementing an effective data governance framework requires a clear understanding of the organization's goals, data quality requirements, and existing data management practices [62]. Organizations must establish data quality metrics, set data quality targets, and monitor data quality performance regularly [63]. In addition, organizations should invest in data governance technologies such as data catalogs, data lineage tools, and data quality management systems to support the data governance process [54]. By adopting a robust data governance framework, organizations can significantly improve data quality and unleash the full potential of AI. Furthermore, effective data governance is essential for fostering a data-driven culture within an organization. By promoting collaboration and communication between different departments and stakeholders, data governance helps to break down data silos and facilitates the sharing of data assets [54]. This enables organizations to leverage their data more effectively and gain valuable insights into strategic decision-making [58]. Training and education are critical components of data governance [60]. Ensuring that employees have a solid understanding of data quality concepts, tools, and best practices helps create a shared vision and commitment to maintaining high-quality data. This can lead to more accurate and reliable AI models that drive innovation and create a competitive advantage [59]. In addition to internal data governance efforts, organizations should consider the importance of external data quality. As AI systems often rely on data from various sources, including third-party providers and public datasets, ensuring the quality of external data is crucial for the success of AI initiatives [57]. Collaborating with data providers and establishing data quality agreements can help mitigate potential data quality issues stemming from external sources [58]. In summary, implementing a comprehensive data governance framework is vital for addressing data quality challenges in AI. Organizations that prioritize data governance can enhance their decision making, optimize operations, and unlock the full potential of AI technologies. By fostering a data-driven culture, investing in data governance technologies, and ensuring both internal and external data quality, organizations can build a solid foundation for AI success. Addressing Data Quality Challenges: Techniques and Strategies  
This section proposes solutions to address the challenges in data quality. The results are summarized in Figure 7.  
Data Cleaning: Data cleaning is an essential step in improving data quality for AI. This involves identifying and correcting errors, missing values, inconsistencies, and outliers in data. Techniques such as data profiling and validation can help identify areas of data that require cleaning. Data Profiling and Data Preparation: Data profiling involves analyzing datasets to identify data quality issues such as missing data, duplicate records, and inconsistent values. Data preparation involves cleaning and transforming the raw data into a usable format for AI algorithms. These processes are essential to ensure that the data used to train the AI models are accurate, complete, and consistent. Data Labeling: Data labeling involves tagging data with relevant metadata that describe its characteristics, which can help ensure that AI models are trained with high-quality  
Figure 7: Proposed solution to data quality challenges. data. For example, in image recognition, data labeling may involve identifying objects in the images and adding descriptive labels to the data. Imputation Techniques for Missing Data: Missing data are a significant challenge in ensuring the data quality of AI systems. Various imputation techniques have been proposed to handle missing data, including mean imputation, regression imputation, and multiple imputation [54]. Advanced techniques, such as matrix completion methods, have been explored in recent years [55].