What Is Data Aggregation?

Financial data aggregation generates real-time insights into market trends, investment risks, and news sources to build predictive models that forecast market movements and identify potential opportunities.

Aggregate data in healthcare aims to improve patient outcomes, consolidate clinical research, and address public health challenges by analyzing HIPAA-compliant health records at scale. 

Data aggregation helps improve performance, identify target markets, and optimize selling strategies. Aggregated data can be manipulated to segment customers (e.g., demographics, purchasing behavior) and improve marketing campaigns.

Data aggregation is used to optimize marketing spend, identify top-performing channels, and compare KPIs. Data aggregation is also used on websites to track visitor behavior and optimize web design.

Aggregated data is used to understand customer behavior, optimize inventory management, and determine optimal pricing strategies (especially when based on real-time competitor pricing tracking).

Data aggregation can offer insights into network performance, usage trends, and customer experience. This is especially helpful for telecommunications infrastructure planning and service delivery.

Government agencies leverage aggregated data from census bureaus, statistical reports, and public records to inform policy decisions and address societal challenges. 

Data aggregation typically involves three steps: collecting data, processing the extracted data, and presenting the aggregated data in a comprehensible format.

There are several ways to aggregate data and various types of data aggregation to consider for your needs.

• Manual data aggregation is feasible for small datasets, but is also time-consuming, labor-intensive, and prone to errors. It involves using spreadsheets to manually calculate averages or totals from raw data.
• Automated data aggregation leverages data aggregation tools to efficiently consolidate data at scale. Such tools can be customized to produce different outputs and can be scheduled to run on a recurring basis.
• Time data aggregation summarizes data from one source over a period of time (e.g. hourly, daily, monthly). For example: Your goal may be to track seasonal buying behavior.
• Spatial data aggregation combines all data from a grouping of resources. For example: This can be used to identify connections between environmental exposures and health outcomes within a specific population.
• Attribute data aggregation summarizes data based on specific characteristics (e.g., customer segment, job title, product category). This can draw insights about subsets within a dataset.