Analytics

26 popular techniques for analysing Big Data

There are many techniques being used to analyze datasets. In this article, we provide a list of some techniques applicable across a range of industries. This list is by no means exhaustive. Indeed, researchers continue to develop new techniques and improve on existing ones, particularly in response to the need to analyze new combinations of data. We note that not all of these techniques strictly require the use of big data—some of them can be applied effectively to smaller datasets (e.g., A/B testing, regression analysis). However, all of the techniques we list here can be applied to big data and, in general, larger and more diverse datasets can be used to generate more numerous and insightful results than smaller, less diverse ones.
1. A/B testing: A technique in which a control group is compared with a variety of test groups in order to determine what treatments (i.e., changes) will improve a given objective variable, e.g., marketing response rate. This technique is also known as split testing or bucket testing. An example application is determining what copy text, layouts, images, or colors will improve conversion rates on an e-commerce Web site. Big data enables huge numbers of tests to be executed and analyzed, ensuring that groups are of sufficient size to detect meaningful (i.e., statistically significant) differences between the control and treatment groups (see statistics). When more than one variable is simultaneously manipulated in the treatment, the multivariate generalization of this technique, which applies statistical modeling, is often called “A/B/N” testing
2. 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 diapers also tend to buy beer). Used for data mining.

11 Comments
  1. jablfaran 3 years ago
    Reply

    Read More button does not refer the intended page…

    • Editor / BDMS 3 years ago
      Reply

      Apologies. Even we are unable to find it, as the source link has been removed/changed. We will update the link as soon as we find the article.

      • Safaa Anwer 6 months ago
        Reply

        that was 3 years ago .. where is the article

  2. Jason Miller 3 years ago
    Reply

    Have you found it yet?

  3. kumar 3 years ago
    Reply

    EXCELLENT

  4. Derp O'Laoi 3 years ago
    Reply

    10 months ago and still no updated link? Shame. was shaping up to be an interesting read.

  5. yamini 2 years ago
    Reply

    this data is useless. you have mentioned only two techniques.

  6. tilahun 2 years ago
    Reply

    how can i improve big data analysis through clustering ?
    please if you have any clue help me?

  7. blacksh33p 1 year ago
    Reply

    GO FUCK YOURSELF!

  8. Poor guy 1 year ago
    Reply

    this article cheat click rate to their site. however, these clicks won’t drive conversion.
    Poor

  9. Lucas Moabe 8 months ago
    Reply

    https://blogs.systweak.com/2016/11/an-insight-into-26-big-data-analytic-techniques-part-1/
    I found this like here guys, with the rest of the techniques. Hope it’s helpful.

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