Aggregate data

Aggregate data is high-level data which is acquired by combining individual-level data. For instance, the output of an industry is an aggregate of the firms’ individual outputs within that industry. Aggregate data are applied in statistics, data warehouses, and in economics. There is a distinction between aggregate data and individual data. Aggregate data refers to individual data that are averaged by geographic area, by year, by service agency, or by other means. Individual data are disaggregated individual results and are used to conduct analyses for estimation of subgroup differences. Aggregate data are mainly used by researchers and analysts, policymakers, banks and administrators for multiple reasons. They are used to evaluate policies, recognise trends and patterns of processes, gain relevant insights, and assess current measures for strategic planning. Aggregate data collected from various sources are used in different areas of studies such as comparative political analysis and APD scientific analysis for further analyses. Aggregate data are also used for medical and educational purposes. Aggregate data is widely used, but it also has some limitations, including drawing inaccurate inferences and false conclusions which is also termed ‘ecological fallacy’. ‘Ecological fallacy’ means that it is invalid for users to draw conclusions on the ecological relationships between two quantitative variables at the individual level. In statistics, aggregate data are data combined from several measurements. When data is aggregated, groups of observations are replaced with summary statistics based on those observations. In a data warehouse, the use of aggregate data dramatically reduces the time to query large sets of data. Developers pre-summarise queries that are regularly used, such as Weekly Sales across several dimensions for example by item hierarchy or geographical hierarchy.