Introduction to Data Analytics

By reviewing the literature, the author finds that data analytics for business is defined as a process for discovering patterns in data and turning data into insights to improve decision-making and make actionable recommendations by using statistical techniques, computer programming skills and business domain knowledge . Based on this definition, four characteristics are identified: (1) collecting business data, (2) analyzing data, (3) turning data into insights, and (4) making decisions based on data-driven insights to improve business outcomes. Making use of simple examples, this chapter illustrates how we can analyze the collected data using four different types of analytics, increasing in complexity but also in value, namely (1) descriptive analytics, (2) diagnostic analytics, (3) predictive analytics, and (4) prescriptive analytics. To make the best use of these analytics, this chapter also reviews the principles and guidelines that support best practices in analytics, for example, the LAMP model. Based on the fundamental definitions, theories and principles, the chapter discusses some practical applications and the steps to carry out an analytics process. There are five basic steps in the analytics process: (1) identifying the problem and formulating the research question, (2) building hypotheses and models, (3) collecting data, (4) analyzing the data with robust analysis techniques, and (5) giving actionable recommendations. After presenting the essential knowledge of the analytics process, the chapter ends with some real-world analytics applications, including how Lowe’s Company identifies their most significant human resource area, how the Royal Bank of Scotland applies big data to their “personology” strategy to improve customer service, and how Walmart predicts the best-selling products to ensure sufficient supplies.