Data gravity occurs when the volume of data in a warehouse increases and the number of uses also increases. In some cases, copying or moving data can be troublesome and expensive. Therefore, data tends to pull services, applications and other data into its warehouse. Primary examples of data gravity are data warehouses and data lakes. Data in these systems is inert. Scalable volumes of data often break existing infrastructure and processes, requiring risky and expensive fixes. Therefore, the best practice is to move design processing to data, not the other way around.
Descriptive analysis is the analysis of historical data to determine what is, what has changed, and what patterns can be identified.
Active Data Warehouse (ADW) is a combination of products, features, services, and business partnerships that support Active Business Intelligence business strategy.
Risk management, sometimes referred to as risk mitigation, is the process of developing a strategy to reduce certain individual risks until the aggregate risk level for an activity is reduced to an acceptable level.
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