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2. Data analysis - Wikipedia

   en.wikipedia.org/wiki/Data_analysis

   Data analysis is the process of ... when subgroup analyses will be performed during the main analysis phase. [126] The characteristics of the data sample can be ...

3. Business analytics - Wikipedia

   en.wikipedia.org/wiki/Business_analytics

   The difficulty in ensuring data quality is integrating and reconciling data across different systems, and then deciding what subsets of data to make available. [ 3 ] Previously, analytics was considered a type of after-the-fact method of forecasting consumer behavior by examining the number of units sold in the last quarter or the last year.

4. Analytics - Wikipedia

   en.wikipedia.org/wiki/Analytics

   Data analysis focuses on the process of examining past data through business understanding, data understanding, data preparation, modeling and evaluation, and deployment. [8] It is a subset of data analytics, which takes multiple data analysis processes to focus on why an event happened and what may happen in the future based on the previous data.

5. Business intelligence - Wikipedia

   en.wikipedia.org/wiki/Business_intelligence

   Business intelligence (BI) consists of strategies, methodologies, and technologies used by enterprises for data analysis and management of business information. [1] Common functions of BI technologies include reporting, online analytical processing, analytics, dashboard development, data mining, process mining, complex event processing, business performance management, benchmarking, text ...

6. Exploratory data analysis - Wikipedia

   en.wikipedia.org/wiki/Exploratory_data_analysis

   Exploratory data analysis is an analysis technique to analyze and investigate the data set and summarize the main characteristics of the dataset. Main advantage of EDA is providing the data visualization of data after conducting the analysis.

7. Predictive analytics - Wikipedia

   en.wikipedia.org/wiki/Predictive_analytics

   It is important to note, however, that the accuracy and usability of results will depend greatly on the level of data analysis and the quality of assumptions. [1] Predictive analytics is often defined as predicting at a more detailed level of granularity, i.e., generating predictive scores (probabilities) for each individual organizational element.