## 3 Methodology: A CRISP-DM Approach to a Real Case
### Data Understanding  
In alignment with the CRISP-DM methodology, the data understanding phase provided a structured exploration of the multiple datasets underpinning the equipment importation process in the public sector. This phase aimed to assess the structure, quality, and relevance of the available data to support the development of AI-driven mechanisms for error classification, correction recommendation, and process optimization. The combined dataset consisted of 12,843 complaint records derived from 7615 unique user interaction logs, spanning a temporal window from January 2022 to December 2024. These logs reflect a broad range of technical and administrative issues encountered across various public sector departments and infrastructures. The dataset included both structured fields (e.g., equipment type, supplier ID, timestamps) and unstructured fields (e.g., free-text error descriptions and user comments). The data corpus was composed of heterogeneous sources obtained from operational systems responsible for asset management and technical support. These sources included Excel and CSV files representing distinct administrative and technical domains, such as equipment registries, supplier associations, user logs, and historical importation outcomes. Key datasets were derived from platforms including PatOnline and PATS, which collectively captured a wide range of structured and semi-structured information. The data contained attributes such as equipment type, model, memory and storage configurations, supplier identifiers, timestamps of importation attempts, and diagnostic error messages. Importation logs were particularly rich in unstructured content, often containing free-text descriptions of failures, which presented both analytical opportunities and preprocessing challenges. A key aspect of the classification task was understanding the class balance across the four semantic clusters identified through BERT-based embeddings. The distribution of complaints was as follows: Cluster 0 (Startup Errors and Equipment Reassignment): 28.1%, Cluster 1 (Software Reinstallation and Slow Performance): 36.2%, Cluster 2 (Peripheral Failures and Error Indicators): 18.7%, and Cluster 3 (Equipment Replacement and Migration): 17.0%. While moderately imbalanced, the distribution allowed for stratified sampling during model training and informed the evaluation metrics used. An initial exploratory analysis was conducted to evaluate the completeness and consistency of the data. This included data type profiling, assessment of value distributions, and identification of columns with high levels of missingness. Visual inspection techniques, such as heatmaps and null-value matrices, were employed to detect patterns of sparsity and redundancy. Several tables exhibited fields with over 50% missing values, particularly in legacy or auxiliary datasets, suggesting the need for dimensionality reduction or selective exclusion during the preparation phase. In contrast, critical fields relevant to model training, such as equipment specifications and error descriptions, were generally well populated, though inconsistencies in formatting and nomenclature were common. Free-text fields, especially those containing equipment names and part numbers, displayed high lexical variability. Variants such as \"HP ProBook 430 G5\" and \"HP Probook 430G5\" were semantically equivalent but syntactically dissimilar, indicating the necessity of applying normalization techniques and entity resolution in subsequent phases. Similarly, the MensagemEroImportacao field revealed a rich set of diagnostic expressions, from SQL constraint violations to ambiguous timeout errors, necessitating natural language processing for semantic grouping and downstream interpretability. Relational structures across datasets were also identified, with foreign key relationships linking records across different sheets and file types. For example, associations between equipment entries and their respective importation logs were essential for constructing supervised learning datasets.