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In many big data projects, there is no large data analysis happening, but the challenge is the extract, transform, load part of data pre-processing. [ 225 ] Big data is a buzzword and a "vague term", [ 226 ] [ 227 ] but at the same time an "obsession" [ 227 ] with entrepreneurs, consultants, scientists, and the media.
A cloud-based architecture for enabling big data analytics. Data flows from various sources, such as personal computers, laptops, and smart phones, through cloud services for processing and analysis, finally leading to various big data applications. Cloud computing can offer access to large amounts of computational power and storage. [30]
Data mining is a particular data analysis technique that focuses on statistical modeling and knowledge discovery for predictive rather than purely descriptive purposes, while business intelligence covers data analysis that relies heavily on aggregation, focusing mainly on business information. [4]
The storing, managing, accessing, and processing of this vast amount of data represents a fundamental need and an immense challenge in order to satisfy needs to search, analyze, mine, and visualize this data as information. [5] Data-intensive computing is intended to address this need.
Whereas once the problems posed by big data were only found in the scientific community, today big data is a problem for many businesses that operate transactional systems online and, as a result, amass large volumes of data quickly. [37] [36] The analysis of unstructured data types is another challenge getting attention in the industry ...
Business – Businesses collect large amounts of data, daily. Some examples of types of data collected in business operations include; sales data, consumer behavior data, distribution data. An augmented analytics platform provides access to analysis of this data, which could be used in making business decisions. [1]