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Big Data

Big data is a term that describes the large volume of data that are so big and complex that traditional data-processing application software are inadequate to deal with them. Big data challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating, information privacy and data source. Big data can be analyzed for insights that lead to better decisions and strategic business moves. It also help companies make sense out of random information,become proactive and start setting the pace instead of continuously putting out fires and following competition.

Big Data as Three Vs:


Volume

Such voluminous data can come from different sources, such as business transaction systems, customer databases, medical records, internet clickstream logs, mobile applications, social networks, the collected results of scientific experiments, machine-generated data and real-time data sensors used in internet of things (IoT) environments.


Velocity

Rate of how fast the data is being dealt with and must be processed and analyzed. It is important as big data analysis expands into fields like machine learning and artificial intelligence (AI), where analytical processes automatically find patterns in the collected data and use them to generate insights.


Variety

Data comes from different kinds of format, from structured to unstructured documents etc. It refers to the degree of certainty in data sets. Uncertain raw data collected from multiple sources, such as social media platforms and webpages. It may have multiple meanings or be formatted in different ways from one data source to another -- things that further complicate efforts to process and analyze the data.

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