Understanding the Predictive Analytics Lifecycle covers each phase of the development of a predictive analytics initiative. Through the use of illuminating case studies across a range of industries that include banking, megaresorts, mobile operators, healthcare, manufacturing, and retail, the book successfully illustrates each phase of the predictive analytics cycle to create a playbook for future projects.
Predictive business analytics involves a wide variety of inputs that include individuals' skills, technologies, tools, and processes. To create a successful analytics program or project to gain forward-looking insight into making business decisions and actions, all of these factors must properly align. The book focuses on developing new insights and understanding business performance based on extensive use of data, statistical and quantitative analysis, explanatory and predictive modeling, and fact-based management as input for human decisions. The book includes:
- An overview of all relevant phases: design, prepare, explore, model, communicate, and measure
- Coverage of the stages of the predictive analytics cycle across different industries and countries
- A chapter dedicated to each of the phases of the development of a predictive initiative
- A comprehensive overview of the entire analytic process lifecycle
If you're an executive looking to understand the predictive analytics lifecycle, this is a must-read resource and reference guide.
Keywords: Understanding the Predictive Analytics Lifecycle; Alberto Cordoba; predictive analytics; predictive analytics lifecycle; predictive business analytics; business analytics; predictive model; predictive modeling; predictive human decisions; predictive initiative; development of a predictive initiative; analytics process lifecycle; business intelligence; stages of the predictive analytics cycle; stages of the predictive analytics lifecycle; business process improvement; forecasting; shelfware; time and materials projects; change management; data consistency; data governance; NoSQL; OLAp; visual analytics; reporting; predictive models for cruise industry; predictive models for mobile operator; predictive models for retail banking; SMS; churn; webex; data security; scorecard; predictive models for fraud detection; master data management; alerts; rules; analytical CRM; COGS; models for inventory optimization; inventory replenishment models; inventory forecasting; historical data; IT independence; KPI