## 2 Materials and Methods
### Dimensions of Data Challenges for AI
#### 2.2.1 Dimension I: Data Quality
Integrity: Data integrity refers to the maintenance of data accuracy and consistency throughout its lifecycle, including during storage, retrieval, and processing [46]. In other words, it is the degree to which data are reliable and trusted to be correct. Compromised data integrity can result in AI systems making decisions based on corrupt or inconsistent data, leading to unreliable or flawed outcomes. Relevance: Relevant data refer to the degree to which the data used for training and building machine learning models are appropriate and applicable to the task or problem being addressed [37]. This is directly related to a specific problem or task being addressed by an AI system. Irrelevant data can introduce noise or bias into a system, thereby reducing their performance and effectiveness [39]. Figure 6: Data quality measures. By considering these dimensions of data quality and their implications for AI systems, organizations can better understand the challenges they face in maintaining high-quality data for AI applications. This understanding can inform the development of strategies and best practices to address data quality issues, thereby ensuring that AI systems can deliver accurate, reliable, and valuable insights and outcomes. Challenges in Data Collection, Pre-Processing, and Management  
Data Collection: Data collection for AI applications is often driven by the need to solve the problem of ensuring that relevant data are collected, which is a challenge because of the sheer volume of data, the diversity of data sources, and the need for representative samples [49]. Data collection to ensure data quality is determined by data requirements and identifying the types of data needed for the application, data sources, and data quantity [50]. To ensure data quality in the data collection phase, the following considerations must be considered:  
Data Pre-Processing: Data pre-processing is a crucial step in ensuring data quality, as it involves cleaning, transforming, and integrating the data to facilitate analysis [51]. Pre-processing can be time-consuming and resource-intensive, given the need to handle missing values, outliers, inconsistencies, and other data quality issues [52]. Moreover, pre-processing decisions can have significant implications for AI model performance, as they influence the characteristics of the input data [53]. Data Quality Management: Effective data management is essential for maintaining data quality and ensuring that AI systems can access and process data efficiently [41]. Data management challenges include maintaining data storage and retrieval systems, implementing version control, and ensuring data security and privacy [46]. The Role of Data Governance in Ensuring Data Quality  
Data governance plays a critical role in maintaining, ensuring, and enhancing data quality in organizations [54]. It encompasses the processes, policies, standards, and technologies that manage the availability, usability, integrity, and security of data [55]. Effective data governance helps organizations make better decisions, optimize operations, comply with regulations, and create a competitive advantage [56]. Implementing a comprehensive data governance framework is essential for addressing data quality challenges in AI [57]. Data governance includes several aspects such as data stewardship, data quality management, data privacy and security, and data architecture [58]. Data stewardship involves assigning responsibility and accountability for data quality to designated data stewards to ensure that data meet organizational standards [59]. Data quality management refers to the processes and tools used to measure, monitor, and improve data quality, such as data profiling, cleansing, and enrichment [60]. Data privacy and security are concerned with protecting sensitive information and ensuring compliance with relevant regulations [61]. Data architecture includes the design, organization, and management of data structures, storage systems, and data integration technologies [57].