Big Data is a buzz phrase that is used in various situations and is constantly developing.
To classify Big Data decisively is not so easy. Firstly, it is not just a stand-alone term but rather a combination of many aspects to reveal a whole picture. And secondly, Big Data is a buzz phrase that is used in various situations and is constantly developing. It is time to set things straight.
Buzz phrase? Collective term? Synonym?
All of the above. Fundamentally, Big Data represents large digital data volumes as well as the capturing, analyzing and evaluating of it. Therefore, Big Data is also the collective term for all digital technologies, architectures, methods and processes that are required for these tasks. Or as Hasso Plattner says: “Big Data is a synonym for large data volumes in a wide range of application areas as well as for the associated challenge of being able to process them.”
Large data volumes?
Very large. “By the year 2003, humans had created a total of 5 trillion gigabytes of data. In 2011 the same amount was created within 48 hours. Now, creating the same data volume requires just 7 minutes,” illustrated RBB Radioeins in simple and effective terms. Driven by the internet, social networks, mobile devices and the Internet of Things, the worldwide digital data volumes will grow another tenfold by 2020. In Germany alone the current figure of 230 billion GB will rise to 1.1 trillion GB.
This is exactly were Big Data comes into play: The huge data volumes are checked for relationships using a such algorithm, and the whole process requires a combination of several disciplines. “It ranges from traditional informatics and data science to interface design. Machine learning, deep learning and artificial intelligence to mathematics, statistics and data interfaces,” explains Florian Dohmann, Senior Data Scientists at The unbelievable Machine Company. “A lot of this is nothing new, but combining them all creates the basis for new opportunities.”
So it is only about data volumes?
Fundamentally, yes. Big Data is firstly defined by data volumes that are “too large, too complex, change too quickly or are structured too weakly to be analyzed with manual and traditional data processing methods,” according to Wikipedia. But to define where Big Data begins – i.e. from which point the targeted use of data becomes a Big Data project – you need to take a close look at the details.