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.
TechTarget defines machine learning as: “... it is a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed.
Predictive analysis, a type or extension of predictive analysis, is used to recommend or predict certain actions when certain information states are reached or conditions are met.
It can be defined in the form of enterprise marketing technology that provides contextually relevant experiences, value and benefit at an appropriate moment in the customer's lifecycle through preferred customer touchpoints.
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