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The process of data aggregation involves collecting data and expressing it in a summarized form. Data from multiple sources are gathered and presented in summary statistics or totals. Data aggregation is important in business analytics because it provides solid answers to analytical questions. It also dramatically reduces the time taken to query humongous data-sets. Data aggregation is usually done on a large scale using tools referred to as data aggregators. These tools boast features that support aggregate data collection, processing, and presentation.
Data mining is a statistical method used to convert raw data into practical and meaningful information. The process of data mining depends on the use of sophisticated software to locate patterns in large datasets. With the right information, businesses can understand their customers better and formulate viable marketing strategies. Also, data mining relies on effective gathering of data, storing (warehousing), and processing using various tools.
Text mining or text analytics is a method of artificial intelligence. It uses natural language processing (NLP) to change an unstructured text into structured data. The transformed and normalized data can then be used for machine learning algorithms or analysis. Text mining evaluates a huge collection of data to identify facts, associations, and assertions. This information after further analysis and conversion to a structured format can also be presented using charts, HTML tables, etc.
Predictive analytics is an advanced method that is used to make inferences about future events that are unknown. Predictive analytics employs various statistical techniques such as machine learning, AI, data mining, etc. Predictive analytics strives to find patterns in transactional and historical data to highlight future risks and opportunities.