Chapter 3 - Data warehousing, data mining, data modeling, and data analytics

Abstract

Data warehouses are constantly evolving to support new technologies and business requirements—and remain relevant when it comes to big data and analytics. Regardless of how new or sophisticated your data warehouse is, it likely needs modernization. Data warehousing, along with data modeling, and side by side with data analytic capability gives us the upper hand with our knowledge by collecting the right information at the right time with the right data coming from all directions, whether or not these data are structured or unstructured. We should be able to have proper tools in hand to be able to take this information and knowledge to be in a position of resilience based on predictive analysis driven by data. This chapter will discuss data warehousing, data modeling, and consequently, data analytics where, in combination, they all are variables functioning within the process of predictive analytic modeling. This process allows us to have the knowledge we are looking for. Getting reliable information from data warehouses is resource-intensive; missing one step can result in wasted processing time and/or bad data.