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Big data analytics is a process that allows for advanced analytics to be performed on large amounts of raw data to detect patterns and probabilities. Big data storage often occurs in a data warehouse or data lake, and is most often unstructured, though large enough segments of structured data still qualify as big data. Analytical processing of large data sets is more complicated than regular data processing, and requires the use of specialized big data analytics tools and machine learning, ML.
In order to form the most logical conclusions, big data analysis typically looks at the 6 Vs: volume (quantity and size of the stored data), variety (the nature of the data), velocity (the speed of data ingestion), veracity (reliability of the data), value (the usability of the data after processing), and variability (characteristics of the various formats in the data). Because big data must consider the different ways the 6 Vs interact, specialized analytics tools are required. The 6 Vs also help ensure that the conclusions that are being drawn are reliable and take into consideration all relevant data.
A data scientist can use ML and big data technologies in order to perform predictive analytics on large data sets. By doing so, they can gain insights into the ways that different categories of people are most likely to act in any particular scenario. Big data analytics can provide support for a wide range of uses, and is not just retail oriented. These types of analyses can help organizations understand how to interact with voters before an election, or even plot out the best ways to raise funds for nonprofit organizations. Businesses and organizations can make more informed decisions regarding potential action paths when utilizing these types of analytics. Ensuring proper data management, analyzation, and usage is imperative to improve business intelligence.
When organizations analyze big data, they can: