Close panel

Close panel

Close panel

Close panel

Data> Big Data Updated: 23 Aug 2018

What makes Big Data big?

Technically, big data is no different than any other data, there’s just more of it. After all, gathering data is nothing new. We all have birth certificates, marriage licenses, insurance cards, and our grandparents had those same things decades ago… even without computers. The difference today is the sheer volume of data and the ways that data can be collected and analyzed.

Every two days, we collect as much data as we did from the beginning of time until the year 2000. And by 2020, the amount of digital information available will have grown from around 5 zettabytes today to 50 zettabytes (that’s one billion terabytes).

Where does all that data come from? For one thing, the time we spend online and on our phones leaves a digital trail. Every time we ask our smartphone for directions, check in somewhere using social media, connect with friends by text or with chat apps -- and of course, when we shop -- we leave evidence of our movements, our preferences and our choices.

At first, that can feel a bit like Big Brother is watching us. But the fact is, this vast amount of data -- big data -- and the ability to analyze it can be used to our advantage. The patterns and trends that emerge through big data are particularly helpful in improving healthcare, predicting and responding to natural and manmade disasters, and even preventing crime. They can also be used to inform and improve our interactions with businesses.

Take financial institutions, for example. As BBVA Compass Data Portfolio Director Michael Taylor points out, by definition, banks have always possessed a great deal of information that consumers provide to them as part of routine interactions. “We also know certain things about customer behavior, such as how often they use ATMs, how much they typically keep in their savings account, or how often they visit a branch. We can also supplement this information with additional data which help us understand how to provide products and services to meet a customer’s specific needs."

Taylor adds that simply collecting the data isn’t enough to make it useful.

“Until recently, informational systems were typically structured and developed to support financial, risk and regulatory reporting,” Taylor said. “That meant the data we used for our advanced analytics had already passed through that filter before it was made available to the advanced analytics teams.”

Now, new tools and technology gives data scientists direct access to a breadth and depth of data that would have never been available through the systems that were designed to support reporting requirements.

Taylor: By comparing more data points, relationships begin to emerge that ... enable us to learn more about our customers and help them make smarter financial decisions.

“Big data works on the principle that the more you know about anything or any situation, the more reliably you can gain new insights and consider what may happen in the future. By comparing more data points, relationships begin to emerge that were previously hidden, and these relationships enable us to learn more about our customers and help them make smarter financial decisions,” Taylor said.

That’s why it was important for the bank globally to move away from its legacy enterprise data warehouse to a new big data platform that provides access to all data in all systems, unfiltered by reporting requirements.

“Now we are developing tools and capabilities to leverage data - always with consumer consent - on a scale we couldn't with our legacy platform and workforce,” Taylor explained. “By leveraging the new tools and data, we are able to take the step beyond reporting on customer behavior and begin truly understanding their behavior to support them in key financial decisions and life events.”