According to IBM, we create about 2.5 quintillion bites of data every day, and IDC estimates that the volumes of data will more than double every two years.
"Big Data" has quickly become a buzzword across industries. It is defined as data sets that are too large and complex for conventional tools to capture, store, and analyze. Just as the term was first emerging, Gartner described Big Data in 2001 via the three "Vs"—volume (amount of data), velocity (speed of data in and out), and variety (range of data types and sources), and continues to lean on these characteristics today.
Marketers have collected and analyzed customer data and used the resulting insights to improve the customer experience and boost sales for decades. Big Data poses a new set of challenges to marketers as many conventional tools and practices fail to capitalize on these vast and varied data sets.
At the same time, Big Data affords a strong opportunity for brands to understand their customers more than ever before.
Why Do We Need Big Data?
Big Data doesn't include simply more data, but also new streams of it, collected by digital sensors on connected devices beyond phones that make up the "Internet of Things." It also incorporates data from an ever-expanding number of digital channels frequented by customers. That includes conversations on social channels, transactional data (e.g., credit card information), browsing and search history, and data collected by GPS technology.
Insights gleaned from all that data can offer marketers valuable clues around the tastes, intent, and journey of their customers and prospects.
Alongside the rise of Big Data, new tools are emerging to help analyze and understand that data. Tools such as artificial intelligence, in-memory computing, pattern recognition, and highly scalable NoSQL data storage systems can empower marketers to capture and analyze data on customer activity in real time, and respond as appropriate.