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Big Data Myths and Facts

  1. Myth: Big Data Is New
    • Fact: Huge cross-references of every single word used in the Bible, called “concordances,” were in use by scholar monks for centuries well before the first databases.
  2. Myth: Big data is Made for Big Business
    • Fact: Enterprises of all sizes are able to now leverage big data analytics thanks to recent improvement in cloud and data management technology.
  3. Myth: Bigger Data Is Better
    • Fact: Quality of data wins over quantity of data. What to use is often more relevant than how much to use.
  4. Myth: Our data is so messed up we can’t possibly master big data
    • Fact: Advanced data quality, master data management, and data governance tools have made it easier to clean up the enterprise data mess.
  5. Myth: Every problem is a big data problem
    • Fact: If you are matching a couple fields) against a couple of conditions across a couple of gigabytes, it isn’t really a big data problem. Don’t treat every analytics need as a big data effort.
  6. Myth: Big Data applications require little or no performance tuning
    • Fact: Big Data applications require regular tuning of the analytical and statistical models as more and more data and variables are added.
  7. Myth: Big data is a Magic 8-Ball
    • Fact: Big Data may not tell you everything. A lot depends on the right questions and the right data for it to work
  8. Myth: Big data is only unstructured data
    • Fact: Big Data does not have to be unstructured. Even voluminous structured data may classified as Big Data because of its sheer volume.
  9. Myth: You need unstructured data to make predictions
    • Fact: Predictive models use a combination of unstructured and structured data for training the models and making inferences.
  10. Myth: Machine learning is a concept related to Big Data
    • Fact: The idea underlying machine learning is “using data to model an underlying process”. Machine learning algorithms can, however, provide valuable insights when used in conjunction with Big Data.
  11. Myth: Big data analytics will not require supervision by humans
    • Fact: The adjective “unsupervised” does not mean that these algorithms run by themselves without human supervision. An analyst (or a data scientist) who is training an unsupervised learning model has to exercise a similar hind of modelling discipline as the one who is training a supervised model.

If there is anything more you need to know on the Big Data world, you can get in touch with us here.

About The Author

Shrey Patel works with Intransure Technologies as Search Engine Optimizer. His passion for helping people in all aspects of Digital Marketing. You can find him on Twitter and LinkedIn.

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