## 2 Materials and Methods
### Data Landscape
Technologies such as Apache Hadoop and Apache Spark, among other distributed computing platforms, enable the parallel processing and analysis of large datasets. Furthermore, leveraging machine learning and AI algorithms can extract valuable insights from varied data sources. Given the volume and diversity of data, automating data preparation processes is essential. Data preparation tools and technologies can streamline the ingestion, cleansing, and transformation of data. Automated data pipelines and workflows can significantly reduce manual efforts and ensure consistency in data preparation. Lastly, it is crucial to continuously monitor the volume and variety of data to ensure that the organization's infrastructure and processes remain capable of meeting evolving requirements. Implementing monitoring mechanisms to detect shifts in data volume, variety, or data source patterns is necessary. Organizations must regularly assess and adapt their data management strategies to accommodate changes in data characteristics, ensuring ongoing alignment with the organization's AI objectives. To effectively implement data governance in AI-based digital transformation, organizations must deeply understand the various elements of the data landscape. This understanding is crucial for assessing data readiness for AI, identifying gaps, and devising a strategy to address them. The use of data is a critical facet of this transformation, and several considerations are key to effectively using data in AI initiatives. Organizations should start by defining clear objectives for data usage in AI transformations and determining how data will contribute to specific business goals, process improvements, or innovations. Aligning data usage with strategic objectives ensures a focused and relevant application [72]. A data-driven decision-making culture within an organization is vital. Encouraging stakeholders to base their decisions on data and insights from AI models fosters trust in data and AI processes [73]. Identifying relevant data sources is also essential. An organization must assess both internal and external data sources, including structured and unstructured data, to ensure that they effectively contribute to AI outcomes [74]. Feature engineering is critical for transforming raw data into meaningful features that enhance the performance of the AI model. This involves applying domain knowledge and data analytics techniques to select and transform the most informative attributes [75]. Ethical considerations in data usage cannot be overstated. Organizations must adhere to privacy regulations and data protection policies, employing techniques such as anonymization and encryption to maintain data privacy throughout the AI lifecycle [76]. Measuring ROI and value from data usage in AI initiatives is necessary. This involves establishing KPIs that reflect the organization's goals and tracking the impact of data-driven initiatives [77]. Predictive and prescriptive analytics are powerful tools for leveraging data. Using historical and real-time data, organizations can forecast future trends and behaviors and generate recommendations for optimizing business processes [78]. Personalization enhances customer experience. By leveraging customer data, AI models can create personalized recommendations, targeted marketing, and customized offerings [79]. Data also plays a role in risk management and fraud detection. AI models can be used to identify potential risks or fraudulent patterns, with real-time monitoring systems in place to proactively address these issues [80]. Lastly, continuous improvement and learning from data are imperative. Organizations should establish feedback mechanisms to continuously refine AI models and strategies, allowing for learning and adaptation to new data and business requirements [81]. The data landscape assessment process can be summarized as shown diagram in Figure 7.