An Insight into 26 Big Data Analytic Techniques: Part 1

‘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.

  1. A/B testing

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/B testing

  1. Association Rule learning

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.)


  1. Classification Tree Analysis

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:

  • Automatically assign documents to categories
  • Categorize organisms into groupings
  • Develop profiles of students who take online courses


  1. Cluster Analysis

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.

  1. Crowdsourcing

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.

  1. Data fusion and data integration

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.

Data fusion techniques combine data from multiple sensors and related information from associated databases to achieve improves accuracy and more specific inferences than could be achieved by the use of a single sensor alone.

  1. Data Mining

Data mining is sorting through data to identify patterns and establish relationships. Data mining is referred to the collective data extraction techniques that are performed on large volume of data. Data mining parameters include Association, Sequence analysis, classification, Clustering and Forecasting.

Applications include mining customer data to determine segments most likely to respond to an offer, mining human resources data to identify characteristics of most successful employees, or market basket analysis to model the purchase behavior of customers.

data analytic techniques

  1. Ensemble learning

It is an art of combining diverse set of learning algorithms together to improvise on the stability and predictive power of the model. This is a type of supervised learning.

  1. Genetic Algorithms

Optimization techniques that use processes such as genetic combination, mutation, and natural selection in a design based on the concepts of natural evolution. Genetic algorithms are techniques that is used to identify the most possibly viewed videos, TV shows and other forms of media. There is an evolutionary pattern that can be done by the use of genetic algorithms. Video and media analytics can be done by the use of genetic algorithms.

  1. Machine Learning

Machine Learning is another technique that can be used to categories and determine the probable outcome of a specific set of data. Machine Learning defines a software that can be able to determine the possible outcomes of a certain set of event. It is therefore used in predictive analytics. An example of predictive analytics is probability of winning legal cases or the success of certain productions.

  1. Natural Language Processing

A set of techniques from a subspecialty of computer science (within a field historically called “artificial intelligence”) and linguistics that uses computer algorithms to analyze human (natural) language. Many NLP techniques are types of machine learning. One application of NLP is using sentiment analysis on social media to determine how prospective customers are reacting to a branding campaign.

  1. Neural Networks

Non-linear predictive models that learn through training and resemble biological neural networks in structure. They can be used for pattern recognition and optimization. Some neural network applications involve supervised learning and others involve unsupervised learning. Examples of applications include identifying high-value customers that are at risk of leaving a particular company and identifying fraudulent insurance claims.

  1. Optimization

A portfolio of numerical techniques used to redesign complex systems and processes to improve their performance according to one or more objective measures (e.g., cost, speed, or reliability). Examples of applications include improving operational processes such as scheduling, routing, and floor layout, and making strategic decisions such as product range strategy, linked investment analysis, and R&D portfolio strategy. Genetic algorithms are an example of an optimization technique.

In my next blog, I would describe the remaining 13 Big Data Analytics Techniques.


  • comment_avtar
    Sébastien Parmentier
    A nice overview ! Thanks FYI, you have a broken link with a / missing at “Read: Next Part of An Insight into …”

    6 years ago
    • comment_avtar
      Hi Sebastien, Links are now working

      6 years ago

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