Analytics
Analytics has emerged as a catch-all term for a variety of different business intelligence (BI) and application-related initiatives. For some, it is the process of analyzing information from a particular domain, such as website analytics. For others, it is applying the breadth of BI capabilities to a specific content area (for example, sales, service, supply chain). In particular, BI vendors use the “analytics” moniker to differentiate their products from the competition. Increasingly, “analytics” is used to describe statistical and mathematical data analysis that clusters, segments, scores and predicts what scenarios are most likely to happen. Whatever the use cases, “analytics” has moved deeper into the business vernacular. Analytics has garnered a burgeoning interest from business and IT professionals looking to exploit huge mounds of internally generated and externally available data.[1]
The DAMA-DMBOK2[2]recognizes 4 different types of analytics that are as follows:
1. Predictive Analytics: “The development of probability models based on variables, including historical data related to possible events (purchases, changes in price, etc.).”
2. Prescriptive Analytics: “Anticipates what will happen, when it will happen, and implies why it will happen.”
3. Unstructured Data Analytics: “Combines text mining, association, clustering, and other unsupervised learning techniques to codify large data sets.”
4. Operational Analytics: “The processes of tracking and integrating real-time streams of information, deriving conclusions based on predictive models of behaviour, and triggering automatic responses and alerts.”
Simply put, "analytics" refers to the systematic computational transformation of enterprise data into insights, for the purpose of making better decisions.[3]
The Oxford English Dictionary defines analytics as "The systematic computational analysis of data or statistics."
[4]
// Content analytics is relevant in many industries.