How Much Big Data? Translating terabytes into something concrete
For some time now, we’ve been hearing about ‘big data’ and the buzzword is showing no signs of decline. When quantified, we hear about billions of terabytes, even if this is an amount so large that it’s impossible to grasp. For many of us, dealing with large, and growing volumes of data and information, from a variety of sources (and devices), is the new normal (even if not terabytes).
In reality, there are only a few companies who deal with the true, terabyte-level big data. Still, that doesn’t diminish the very real, large volumes of data and information that medium- and large-sized companies have to manage on a daily basis. Because several of our customers fall into this category, we can share a little insight into what it means to handle everyday Big Data at the average company.
A large transportation company receives over 300,000 emails annually just as part of their Customer Service, for example. A bank manages over 5000 requests per month via its search engine. A leading telecommunications operator manages more than 30,000 text messages a day. Looking at the information sector, a major news agency analyzes an average of 4000 news items daily, while a media company monitors approximately 250,000 Tweets and 50,000 Facebook posts every day to support the activities of Marketing Intelligence.
While these are just some examples, let’s assume that they are on the low end and would be magnified for even larger companies or those with more complex operations.
On any level, such volumes are easy to perceive as overwhelming, especially without the right technology to properly manage it. In this ‘big data’ lies significant intelligence to fuel any number of areas, from improving business processes, to informing your product development and customer intelligence. To benefit from this information requires advanced technologies, especially for analyzing unstructured information—which makes up the majority of today’s Big Data—that can get to the ‘sense,’ the meaning of a text.
Through a comprehension of text, semantics take data from overwhelming, to opportunity. Semantic technology supports all of these activities through the automatic and accurate processing of any type of text, helping customers better manage their internal information, to more quickly respond to customer service requests, to obtain critical strategic information about their brand reputation. You don’t need millions of terabytes, but only a sufficient quantity that reflects your business.