The "Big Data" Conversation: Time to put some misconceptions to rest

Big data is a fairly new topic and what seems to be an elusive term for many. Conversations are important to help bring clarity to Big Data, as well as generate ideas about how we can shape, not only what it is, but also the future of where it’s going. This blog post provides a little big data clarity and continues the conversation.


As a recent graduate, and now professor in the University of Connecticut’s Business Analytics and Project Management masters program, I have a lot of conversations surrounding the topic of “Big Data” and questions such as, “What does that term actually mean?”

Big Data is a fairly new topic and what seems to be an elusive term for many. Conversations are important to help bring clarity to Big Data, as well as generate ideas about how we can shape, not only what it is, but also the future of where it’s going.

Not that it’s surprising, nor is it anyone’s fault necessarily, but I have found quite a few misconceptions surrounding the topic. One of the biggest misconceptions I have run into, which would be scary if it were true, is that “Big Data” is easy to gather and use. This thought-process could easily lead people to believe in one of the most prevalent “concerns” with Big Data and that is that someone is always spying on them.


To set the record straight, and on behalf of everyone who has worked with large datasets, Big Data is far from being easy. As far as collecting data is concerned, it comes with a broad spectrum of problems.

Some data collection can actually be about as painful as trying to use a lawn mower to win the Daytona 500. For other data, it’s like driving a rocket ship through the Daytona 500 (although it sounds like you’d win because of the speed), which means it’s almost impossible to steer and if you don’t manage it right you’re probably going to crash. Finding the right balance is tough. Just like in real life, not everyone can own a racecar - a tool that has been engineered and perfected over time in order to hopefully win the race - or in this case have the adequate knowledge and tools to successfully navigate through all the data.

Here at Hurricane Labs, for example, our team (which includes technical folks, data scientists, interns, and more) works with Big Data in order to help organizations and enterprises who don’t have the tools or aren’t sure how to “win the race” on their own. We enable a better way to take control of the massive amounts of security noise zooming around and steer it in a direction that provides contextual structure and intelligent visibility.


Back to the explanation about the ways of data collection and usage… Although this isn’t the most exciting, one example most can relate to is the very popular form of identity theft: having your credit or debit card numbers stolen.

If you’re an active participant on the internet, then it’s likely you or someone you know has experienced this. According to, “As of 2010, it is estimated that 11.1 million adults are affected by identity theft each year.” This number has likely only risen with the growth of the web and there are also additions, such as the monumental data breaches at companies like Home DepotTarget, and Sony that have taken place since then.

Even if you personally haven’t had this happen, for someone who has, you should ask them the question, “How did you KNOW it was stolen?” Most of the time they will tell you they were notified by their bank. Most banks take this type of criminal activity seriously and are vigilant about it. The reason the bank is able to notify someone that their card number has been stolen, is because of Big Data.

Fraudulent usage is often one of the problems that can be detected through the use of Big Data. Banks don’t have people sitting there watching everything you buy, just like companies aren’t hiring one person to watch every single move you make. These would be inefficient methods. Instead, organizations that are keeping track of abnormal activity are utilizing Big Data to do so.

Sometimes it helps to understand Big Data if you draw a parallel. When you go to deposit $100 at the bank, the teller won’t put it in a physical box for you to withdraw from the next time you go there. Your bank will take your $100 and put it into a giant vault with the rest of the money. A similar thing takes place with the company recording you spending $4 at the drug store. That record will go into a giant database and sit there until it is called upon for duty. Sadly for that lonely little record, most of the time it will never be needed. But when engineered and perfected systems (like the race cars in the Daytona 500) run and compare it with other purchases, it becomes very powerful and can keep you safe.


There is a lot that has gone into a company that reaches a point where they are able to make great predictions about your usage and communicate to you to keep you safe. It requires teams of people, time to implement and run, and ongoing modifications and tuning to keep things accurate and effective - and, of course, to provide proper protection for your privacy.

Like I said, Big Data is not easy, but with the right people, goals, tactics and time, it can majorly impact and shape the way a lot of our world works.

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