Business Data Lake Conceptual Framework
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Analytics
Data (and thus also Events and Insights) and Analytics are the two main “dynamic” concepts of the Business Data Lake. They represent the living “things” that have to be created, updated to populate the Business Data Lake as a Platform to actually create added business value. Analytics are designed and created by the Business Use Case Contributors. Three kinds of Analytics are commonly distinguished: Descriptive Analytics focuses on understanding what is happening or why it’s happening. Business Intelligence analytics falls in this category. Predictive Analytics go further by providing predictions about the probable outcome. Common prediction types are Classification (identifying “clusters” of data) and Regression. Logistic Regression provides pre-identified values as output (for instance predicting whether an email is spam or not is a binary output). Linear Regression provides continuous values as output (for instance predicting the price of an object Prescriptive Analytics provide insights about how to make something (e.g. a Business Goal) happen. More advanced techniques should be leveraged, for instance Simulation, Optimization and Heuristics. Analytics can be specific to a Line of Business (thus managed and executed in one or several Business Compartments). They also can be “public”, available to all Lines of Business. Directory: bigdata bigdata -> Use Cases from nbd(nist big Data) Requirements wg bigdata -> Use Cases from nbd(nist big Data) Requirements wg 0 bigdata -> Nist big Data Public Working Group (nbd-pwg) nbd-pwd-2015/6a,DW. abbreviated rr (M0444) Source: nbd-pwg status: Draft Title: Big Data Use Case #6 Implementation, using nbdra author: Afzal Godil bigdata -> Nist special Publication 1500-4 draft: nist big Data Interoperability Framework: Volume 4, Security and Privacy Download 493.56 Kb. Share with your friends:
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