## 2 Materials and Methods
### Data Landscape
It is vital to maintain the comprehensive documentation of data assets, including their source, transformation processes, and usage history. Document data lineage to track the origin and transformations applied to data. This documentation ensures transparency and enables users to understand the data's context and reliability. Finally, ensure that data accessibility platforms and infrastructure can handle the performance and scalability requirements of AI-based digital transformation. Evaluate system performance, response times, and scalability under different data access scenarios. Scale resources as needed to accommodate increasing data accessibility demands. Data Governance:  
Data governance involves the management of the availability, usability, integrity, and security of the data. It encompasses the data policies, procedures, standards, roles, and responsibilities related to data management, data privacy, and compliance issues [68]. Tools such as Collibra [69] and Informatica Axon [70] can support data governance efforts. Data governance is crucial for AI-based digital transformation initiatives to ensure the availability, integrity, and privacy of data. Implementing data governance in AI-based digital transformation requires a multifaceted approach to managing data throughout its lifecycle. Begin by establishing a robust data governance framework that details policies, processes, roles, and responsibilities. This framework should articulate data governance objectives, delineate data stewardship roles, and establish cross-functional data governance committees tasked with overseeing data-related activities. Clarity in data ownership and accountability is key to assigning specific individuals or teams within the organization the responsibility for data management. Data stewards play a pivotal role in ensuring data quality, integrity, and compliance. To foster a culture of accountability for data management, it is important to encourage the organization-wide recognition of the value and importance of data. To maintain and improve the governance process, regular audits and reviews are essential. These evaluations should assess compliance with established data governance practices, determine their effectiveness, and identify areas that require improvement. Data governance assessments highlight adherence to policies and reveal gaps or non-compliance areas. The insights from these audits should inform the refinement of data governance processes and be applied to AI models and algorithms. Establish guidelines for AI model development, training data selection, model validation, and ongoing monitoring to ensure that ethical considerations and interpretability requirements are met while maintaining transparency and thorough documentation throughout the process. Metrics and reporting mechanisms play critical roles in data governance. It is important to define specific metrics that can measure the effectiveness of data governance initiatives. Establishing key performance indicators (KPIs) will help track data quality, compliance, accessibility, and the maturity of the data governance process. Develop a regular reporting framework to ensure the clear visibility of governance activities and their progress towards the set objectives. Recognize that implementing data governance is a change management initiative. It requires clear communication of its importance to all stakeholders, along with fostering a data-driven culture throughout the organization. Providing training and support to employees ensures that they understand and commit to data governance practices. It is also important to address any resistance to change by continuously communicating the benefits and positive impacts of a strong data governance strategy. Data Volume and Variety:  
The amount and types of data collected by the organization can influence the types of AI techniques that can be applied. Large volumes of data can support more complex models such as deep learning.