Thanks to all the buzz around the term Big Data, there are numerous outlooks and subsequent myths on the topic. While Big Data is an amazing tool that has numerous applications, it’s far from the magical fix for every analytics need. Let’s explore five of the top Big Data myths and the truth behind them.

Myth #1: How Machines Are Always Better Than People

To be clear, computers have two core advantages compared to individuals. First, a computer has the advantage of raw speed, being able to sort through more numbers in a few minutes than a person could in their entire lifetime. Second, a computer can quickly detect patterns and execute formulas across seas of data.

People tend to adapt to situations with limited information much better than machines do. For example, all self-driving car systems on the market still require significant human intervention in a wide range of situations. This is partly due to the myriad situations where unusual or downright novel things occur during any vehicle trip. Put simply, Big Data has to be paired with human decision-making in order to ultimately be effective.

Myth #2: Big Data is the Solution to Everything

It’s common for trends to become trendy far outside their proven applications. After all, everybody wants to be the Uber of something. 

The same issue applies in the world of Big Data. Some problems, however, just don’t lend themselves to mass computation efforts. This can arise due to things that machines struggle to identify, such as:

  • Limited available data
  • Biases built into a dataset
  • Inapplicable information
  • Flawed data

When a Big Data system is asked to analyze a problem, it doesn’t stop to ask a lot of questions about it. If the job is machinable, the computer will accept it.

Myth #3: How Expensive Big Data Is

The word “big” creates an unfair perception that nothing less than building a cluster of supercomputers that each have 8 high-end cards isn’t worth the bother. Nothing could be further from the truth.

Analytics packages have become very accessible, and many can be run right on a typical multicore desktop, laptop or even phone. In fact, many companies have been working hard to deploy cost-effective data processing software which can be used for a variety of data projects. This means that Big Data systems can be deployed in small settings, providing access to IoT devices, machine learning, AI, and dashboards nearly anywhere and at a fair per-unit price.

Myth #4: Why Use Big Data if You’re Not in IT?

The notion of Big Data being about computers is a bit like thinking that carpentry is about nails and hammers. With Big Data, the goal is to create insights that can be applied in a variety of fields. Much as you would use a hammer and nail to build a house, you can use Big Data to build insights that will drive actions and informed business decisions.

The retail sector was an early adopter of Big Data, due to the recognition that retail had missed the train to the .COM boom. Companies in retail use big data to collect and process information like:

  • Social media sentiments
  • Inventory numbers & forecasting
  • Trends in customer tastes
  • Buying processes
  • Global supply chains

If Big Data can revolutionize the way people buy apparel and shoes, it can do a lot of good in many other sectors as well.

Myth #5: Big Data isn’t for the Little Guy

Small operations have some major advantages when it comes to Big Data. Major players often have to deal with the same challenges that come with turning an ocean liner. They struggle to turn because it’s challenging to ignite change within any massive corporation. Conversely, the agility afforded to many small companies allows them to gather insights and react to them in a matter of weeks. Paired with the cost-effectiveness of modern Big Data systems, this presents an advantage that can be rapidly leveraged by small businesses.

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