Big data analytics is a form of data analysis that focuses on larger, more complex datasets ranging in size from terabytes to zettabytes, or one thousand to one trillion gigabytes, of information. The sizes and varieties of these datasets are beyond the scope of traditional datasets as they can store massive amounts of raw and unfiltered information.
Different forms of analysis, like machine learning, must be applied to these datasets because, according to IBM, standard algorithms, data programming languages and other types of research usually used to understand traditional datasets cannot accommodate the tremendous amounts of data present in these more complex sets. Analyzing big data is difficult because of the high volume, velocity and variety of the data present.
For some big datasets, there are constant additions and updates made to the data and great amounts of diversity that can alter the findings of researchers. In the past two decades, data and computer technologies have allowed new analytic techniques to be applied to large datasets to increase the efficiency of research methods and the usefulness of these datasets.
These new analytic techniques are often applied in business or corporate models to ascertain trends among consumers, employees, product sales and other important metrics. Big data analytics also proves helpful in many fields of science like healthcare research, virology, astronomy and others. Overall, many studies involving the storage of large and diverse sets of data and information have become accustomed to utilizing big data analytics in some capacity to detect patterns and increase scientific breakthroughs.
Sources
“Big Data Analytics.” IBM, https://www.ibm.com/analytics/big-data-analytics
“Big Data Analytics: What It Is and Why It Matters.” SAS,
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