Big data is an evolving term that describes any voluminous amount of structured, semistructured and unstructured data that has the potential to be mined for information.
Dimensions of Big Data
Volume
Variety
Velocity
This dimension refers to the quantity of data, as big data is frequently defined in terms of massive data sets with measures such as petabytes and zettabytes.
Volume
This dimension refers to the increasingly diversified sources and types of data requiring management and analysis.
Variety
This dimension deals with the accelerating speed at which data flows in from sources like business processes, machines, networks and human interaction with things like social media sites, mobile devices, etc. The flow of data is massive and continuous.
Velocity
Big data analytics refers to the strategy of analyzing large volumes of data, or big data. This big data is gathered from a wide variety of sources, including social networks, videos, digital images, sensors, and sales transaction records.
Types of Big Data Analytics
Descriptive Analytics
Diagnostic Analytics
Prescriptive Analytics
Predictive Analytics
Descriptive analytics answers the question...
“What is happening?”
Diagnostic analytics answers the question...
“Why did it happen?”
Prescriptive analytics answers the question...
“What should I do about it?”
Predictive analytics answers the question...
“What is likely to happen?”
Descriptive Analytics or data mining are at the bottom of the big data value chain, but they can be valuable for uncovering patterns that offer insight.
Diagnostic analytics are used for discovery or to determine why something happened.
Prescriptive analytics is really valuable, but largely not used. Where big data analytics in general sheds light on a subject, it gives you a laser-like focus to answer specific questions.
Predictive analytics use big data to identify past patterns to predict the future.