3 Myths of Big Data Busted

MythBusters logo

When it comes to big data, myths are plentiful. Below are three common myths — busted.

1. IT is responsible for big data.

While you may need IT’s help in selecting and implementing a big data solution, big data and business intelligence isn’t meant for the IT team exclusively. The best solutions belong in the hands of decision-makers and end users. With that in mind, it’s especially important to get business users involved early on. What do they need big data to deliver to them? How can you make accessing that data as easy and user-friendly as possible?

2. Big data is so huge and tainted by so much bad data, it’s impossible to manage and understand it.

It’s true that garbage in amounts to garbage out. However, modern business intelligence solutions include a number of data quality and governance tools that make filtering out bad data a matter of routine. Data visualization tools help users make sense of millions of records. With quantity, patterns and trends become more readily apparent. Today, capturing, filtering, managing, and analyzing data is easier than ever — even if you have massive amounts of it. In fact, with bigger data comes a greater degree of certainty.

3. You need a data scientist to help your company make sense of big data.

While a data scientist may have been involved in the selection of statistical methods and algorithms that power your data sources, data scientists are not necessarily needed to make sense of big data. Once a business intelligence solution has been designed, tested, and implemented, it should be readily accessible to most information workers. Much of the data science-intensive work has already been done. Leave the computer and data science to the business intelligence and big data solution developers and invest in a system that’s ready to run right out of the box. Modules, mashups, dashboards, and other built-in tools definitely required a data scientist to help develop them, but, if they’re designed properly, they will not require a data scientist to hold your hand while you use them. If they do, it’s time to look for new big data and business intelligence solutions.


1. Information Week , “5 Big Data Myths Busted,” – http://www.informationweek.com/big-data/big-data-analytics/5-big-data-myths-busted/d/d-id/898906