Business Technology Solutions BTS

Development of a Data Mining Data mining has dramatically affected the retail industry, offering insights and opportunities for growth. The multifaceted applications of data mining in retail explore how this technology has revolutionized decision-making processes and customer experiences. By examining real-world examples and industry trends, we uncover the profound impact that data mining has had on optimizing operations, enhancing customer relationships, and driving business success in the retail sector. 1. Data Mining Study Phases 1.1 Phase 1: Understanding the problem that needs to be solved We have to understand the problem that needs to be solved and the relevant prior knowledge. This involves working with business stakeholders to identify the critical issue, the desired outcomes, and any hypotheses or hunches experts may have based on their domain knowledge (Tufféry, 2011, p. 34). 1.2 Phase 2: Understanding the available data and checking its quality understanding the available data and assessing its quality. As Tufféry (2011) explains, “The success of a data mining project depends directly on the data quality.” This requires profiling the data to check for missing values, outliers, skewed distributions, etc., that may require data cleaning or preprocessing. Relevant domain knowledge can guide the understanding of data (Tufféry, 2011, p. 36). 1.3 Phase three: Modeling The modeling phase involves selecting and applying data mining algorithms to search for meaningful patterns or build predictive models according to the project goals (Tufféry, 2011, p. 40). Techniques like classification, clustering, regression, and association rule mining are commonly used. Modeling is an iterative process of testing different algorithms and parameters (Tufféry, 2011, p. 40). 1.4 Phase four: Evaluating and testing the model After generating the models, they must be evaluated and validated on test data to ensure they generalize accurately to new cases (Tufféry, 2011, p. 48). The critical criteria are predictive performance, model interpretability, and business relevance of the findings. Any issues lead back to refining or retraining models. 1.5 Phase 5: Organizing the knowledge gained from data mining Then, the organized data will be presented to stakeholders (p. 54). This includes summarizing key findings, generating visualizations, and outlining a plan to deploy models for business use. A structured process helps ensure that data mining projects yield actionable insights. AM Group leverages data mining for inventory management and supply chain optimization. Analyzing historical sales data enhances stock levels, reduces wastage, and improves overall operational efficiency. (Beshara,2016).   References: Tufféry, S. (2011). Data mining and statistics for decision making. Wiley.