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In areas such as business intelligence, for example, data integration is used to describe the combining of data, whereas data fusion is integration followed by reduction or replacement. Data integration might be viewed as set combination wherein the larger set is retained, whereas fusion is a set reduction technique with improved confidence.
Business analysis is a professional discipline [1] focused on identifying business needs and determining solutions to business problems. [2] Solutions may include a software-systems development component, process improvements, or organizational changes, and may involve extensive analysis, strategic planning and policy development.
In other words, business intelligence focusses on description, while business analytics focusses on prediction and prescription. [1] Business analytics makes extensive use of analytical modeling and numerical analysis, including explanatory and predictive modeling, [2] and fact-based management to drive decision making.
Data integration refers to the process of combining, sharing, or synchronizing data from multiple sources to provide users with a unified view. [1] There are a wide range of possible applications for data integration, from commercial (such as when a business merges multiple databases) to scientific (combining research data from different bioinformatics repositories).
The main characteristic that distinguishes operational analytics from other types of analytics is that it is analytics on the fly, [1] which means that signals emanating from various parts of a business are processed in real-time to feed back into instant decision-making for the business. This is sometimes referred to as "continuous analytics ...
Data level - data level (or early) fusion aims to fuse raw data from multiple sources and represent the fusion technique at the lowest level of abstraction. It is the most common sensor fusion technique in many fields of application.
When proportionate changes in the same figure over a given time period expressed as a percentage is known as horizontal analysis. [2] Vertical or common-size analysis reduces all items on a statement to a "common size" as a percentage of some base value which assists in comparability with other companies of different sizes. [3]
The meaning of the term is that the variability around the individual-level means is significantly different from the variability encompassing the aggregate means. [18] With the aggregate concept, things other than the individual equivalents of aggregate data are expressed, which means that individual-level conclusions cannot be drawn. [3]