Business analytics, a data management solution and business intelligence subset, refers to the use of methodologies such as data mining, predictive analytics, and statistical analysis in order to analyze and transform data into useful information, identify and anticipate trends and outcomes, and ultimately make smarter, data-driven business decisions.
The main components of a typical business analytics dashboard include:
Data Aggregation: prior to analysis, data must first be gathered, organized, and filtered, either through volunteered data or transactional records
Data Mining: data mining for business analytics sorts through large datasets using databases, statistics, and machine learning to identify trends and establish relationships
Association and Sequence Identification: the identification of predictable actions that are performed in association with other actions or sequentially
Text Mining: explores and organizes large, unstructured text datasets for the purpose of qualitative and quantitative analysis
Forecasting: analyzes historical data from a specific period in order to make informed estimates that are predictive in determining future events or behaviors
Predictive Analytics: predictive business analytics uses a variety of statistical techniques to create predictive models, which extract information from datasets, identify patterns, and provide a predictive score for an array of organizational outcomes
Optimization: once trends have been identified and predictions have been made, businesses can engage simulation techniques to test out best-case scenarios
Data Visualization: provides visual representations such as recipe charts and graphs for easy and quick data analysis