11 Big Data Myths and Facts
Big Data Myths and Facts
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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
- 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.
- 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.
- 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.
- 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.
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