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Business Intelligence (BI) is a wide category of applications and technologies for collecting, storing, analyzing, and providing access to data to help the enterprise users make better decision making.BI applications supports the activities decision support systems, querying and reporting, online analytical processing (OLAP), statistical data analysis, forecasting and data mining.While most companies possess enough market knowledge to predict or anticipate which customer segments are their most profitable, the leaders of those businesses also know that scaling a business is not best left to guesswork or instinct.That’s why, in a customer segmentation process like the one described in this guide, it is critical to develop customer segment hypotheses and variables, and validate them with a well-developed, scientific research process.However, valid assessment could be facilitated by using a more comprehensive framework of validity when validating the rubric; (3) rubrics seem to have the potential of promoting learning and/or improve instruction.The main reason for this potential lies in the fact that rubrics make expectations and criteria explicit, which also facilitates feedback and self-assessment.Background The introduction of non–vitamin K antagonist oral anticoagulants has been a major advance for stroke prevention in atrial fibrillation; however, outcomes achieved in clinical trials may not translate to routine practice.We aimed to evaluate the effectiveness and safety of dabigatran, rivaroxaban, and apixaban by comparing each agent with warfarin.

This paper investigates whether evidence for these claims can be found in the research literature.Forrester Research often defines Business Intelligence in one of two ways.Typically, Forrester uses the following broad definition (Forrester,2008) : Business Intelligence is a set of methodologies, processes, architectures, and technologies that transform raw data into meaningful and useful information used to enable more effective strategic, tactical, and operational insights and decision-making.While your hypotheses do not need to be complicated mathematical or statistical statements, they should be clear and logical enough to be testable and useful.For example, a typical hypothesis might look like this: Using that example, the segmentation variables can be defined as the objective measures, factors, or characteristics that help you differentiate segments, whether they are needs- or value-based.

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