The amount of data created on a daily basis is reaching mind numbing proportions, with social media posts and other trivial entries becoming a significant portion of the daily storm of data. While many treat social media as little more that gossip, inane blathering and information that only matters to a small group of individuals, the truth of the matter is all that minutia may have value.
Nevertheless, the real trick is how to uncover that value and filter the wheat from the chaff to make any sense out of the numerous tweets, Facebook posts, and other bits of information floating around the social ether. The path to “making sense” can be paved with technologies that are starting to come into vogue for businesses large and small – big data. However, it is not just the generic term “big data” that brings value, it is truly the processes that make up big data analytics that can deliver answers that can drive business processes.
It may not be so easy to see how big data analytics can garner value out of the minutia floating around in the ether of the internet. However, the elements that make up big data analytics has the muscle to plow through the twitterverse, the realm of Facebook, and numerous other worlds of social interaction sites. Afterall, big data analytics is all about mining information from huge piles of data.
Nevertheless, value is a subjective element – simply put, when mining for gold, you have to know what gold is to begin with. That said, the tools that make up big data analytics do have their limitations and a human touch is definitely needed to make sense out of all the noise. The trick is to know what to look for. Arguably, business marketing and product development has the most to gain from applying big data analysis to social data sources. Those business processes can benefit from customer sentiment analysis, product adoption trends and common complaints that float around in the ether.
That process can focus on mining social media data sets for key words, such as a product name or company name and then cross referencing all occurrences to that mention to create a sentiment index, which can lead to insights. While it may sound simple, the truth is it is anything but. Businesses will need to turn to data scientists and others to really make big data analytics work for them and, of course, educate themselves on how big data works.
Frank J. Ohlhorst is the author of Big Data Analytics: Turning Big Data into Big Money, John Wiley & Sons, 2012.