An Insight into 26 Big Data Analytic Techniques
‘Big Data’ is the application of specialized techniques and technologies to process very large sets of data. These data sets are often so large and complex that it becomes difficult to process using on-hand database management tools.
The radical growth of Information Technology has led to several complimentary conditions in the industry. One of the most persistent and arguably most present outcomes, is the presence of Big Data. The term Big Data is a catch-phrase was coined to describe the presence of Huge amounts of data. The resultant effect of having such a huge amount of Data is Data Analytics.
Data Analytics is the process of structuring Big Data. Within Big Data, there are different patterns and correlations that make it possible for data analytics to make better calculated characterization of the data. This makes data analytics one of the most important parts of information technology.
Hence, here I am listing the 26 big data analytics techniques. This list is by no means exhaustive.
A/B Testing is an assessment tool for identifying which version of a webpage or an app helps an organization or individual meet a business goal more effectively. This decision is taken by comparing which version of something performs better. A/B testing is commonly used in web development to ensure that changes to a webpage or page component are driven by data and not personal opinion.
It is also called as spilt testing or bucket testing.
A set of techniques for discovering interesting relationships, i.e., “association rules,” among variables in large databases. These techniques consist of a variety of algorithms to generate and test possible rules.
One application is market basket analysis, in which a retailer can determine which products are frequently bought together and use this information for marketing. (A commonly cited example is the discovery that many supermarket shoppers who buy nachos buy beer also.)
Statistical Classification is a method of identifying categories that a new observation belongs to. It requires a training set of correctly identified observations – historical data in other words.
Statistical classification is being used to:
A statistical method for classifying objects that splits a diverse group into smaller groups of similar objects, whose characteristics of similarity are not known in advance. An example of cluster analysis is segmenting consumers into self-similar groups for targeted marketing. Used for Data Mining.
In crowdsourcing, the nuance is, a task or a job is outsourced but not to a designated professional or organization but to general public in the form of an open call. Crowdsourcing is a technique that can be deployed to gather data from various sources such as text messages, social media updates, blogs, etc. This is a type of mass collaboration and an instance of using Web.
A multi-level process dealing with the association, correlation, combination of data and information from single and multiple sources to achieve refined position, identify estimates and complete and timely assessments of situations, threats and their significance.
