Generic: A robust analytics model not only performs well on the data it was initially trained on but also produces reliable insights when applied to new data within the existing business model. It should capture the essential system characteristics while abstracting away unnecessary details.
Robust: The analytics model should possess adaptability, enabling it to reflect changes in the data, particularly in relation to evolving business models.
Scalable: Effective analytics models should demonstrate the capability to swiftly process and analyze large volumes of diverse data, catering to both existing and new business models.
Essentially, there exist numerous approaches to netherlands whatsapp number data formulating analytics models, and broadly speaking, analytics models can be conceptualized from four primary perspectives or manifestations, as illustrated in Figure 1 below.
Figure 1: Analytics Models Taxonomy
Analytics, fundamentally, involves questioning to extract insights from data for the purpose of measuring and enhancing business outcomes [3]. Depending on the nature of these inquiries, three types of analytics models emerge:
Descriptive analytics models: “What happened?” Descriptive analytics delves into historical data to discern past patterns, trends, and relationships. It employs exploratory, associative, and inferential data analysis techniques. Exploratory data analytics models scrutinize and summarize datasets, while associative descriptive analytics models elucidate the relationships between variables. Inferential descriptive data analysis is utilized to infer or extrapolate trends about a larger population based on a sample dataset.