What is big data analytics?

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:

Make informed decisions: Analyzing data enables organizations to better understand trends and, as larger amounts of data are processed over longer periods of times, achieve more accurate results. This allows organizations to better predict outcomes and responses.
Adapt quickly: If a sudden change in projections or outcomes occur, big data analysis can enable organizations to quickly identify the anomaly and pinpoint what needs to occur to enhance performance in response.
Support decisions: By having data analytics completed, organizations can have a quick reference of how different data relates to decisions that need to be made, and can assist with planning for future actions. This can be especially useful when important decisions need to be made, but also require strong evidence in their support.
Improve security: By being on the lookout for suspicious activity, big data analytics can help prevent security breaches before they happen. It can even be used to predict crime patterns based on everything from current events to weather patterns.
Support healthcare: A growing trend in healthcare relies on big data analytics to identify patterns within different illnesses. By being able to look at a large group of people, and identify differences between pathological and nonpathological individuals, healthcare can make more accurate predictions of illnesses worth discussing with a primary care provider.

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