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1. Using statistical techniques and machine learning algorithms to analyze current and historical data to make predictions about future events and outcomes in business intelligence.
2. A software tool or platform that facilitates the automation and management of the Extract, Transform, Load process.
3. Processing data in large blocks at scheduled intervals.
4. The analysis of data as soon as it is acquired, often used to make immediate decisions or respond to events as they happen.
5. The process of identifying and capturing changes made to data in real time, enabling real time updates to data warehouses.
6. A central table in a data warehouse that contains the primary measures or metrics of a business process.
7. The first step in the Extract, Transform, Load (ETL) process, which involves retrieving data from various sources, such as databases, files, or APIs.
8. The process of retrieving or pulling data from the source systems or applications.
9. The capability of a system to provide multidimensional analysis of data in a data warehouse.
10. The graphical representation of information and data, using visual elements such as charts, graphs, and maps, to facilitate understanding and decision-making in business intelligence.
11. The process of ensuring the accuracy, completeness, and quality of the extracted and transformed data.
12. Where data is processed in small, fixed-size batches rather than processing the data all at once.
13. The delay between data being generated and being available for reporting and analysis in a data warehouse.
14. The process of identifying and correcting or removing errors, inconsistencies, and inaccuracies in data stored in the data warehouse.
15. The second step in the ETL process, which involves converting and restructuring the extracted data into a format suitable for the destination system or application.
16. The process of collecting, organizing, and storing data to be retrieved and analyzed later.
17. The third and final step in the ETL process, which involves transferring the transformed data into the target database, data warehouse, or application.
18. A technology-driven process for analyzing data and presenting actionable information to help corporate executives, business managers, and other end-users make more informed business decisions.
19. The process of discovering patterns, insights, and valuable information from large datasets stored in the data warehouse.