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.
Data democracy is the process of ensuring that all employees within an organization can easily access and use data without the need for technical knowledge. This approach eliminates data silos, speeds up business processes and improves decision-making.
Predictive analysis is the analysis of big data to make predictions and determine the likelihood of future outcomes, trends, or events occurring.
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