Your big data ain’t as big as you think
Your data ain’t as big as you think it is
Are you on the big data bandwagon? Since I’m on the marketing team to help develop messaging around Spoken’s new big data tool, I have been keeping an eye out for how others in the call center space are using big data and what exactly they are doing with it.
And I discovered something very interesting: every time I clicked on a blog post about how to use big data in the call center, invariably the topic covered wasn’t big data. It was… average handle time. Or first call resolution. ONE metric. Not thousands of structured and unstructured data points. One data point. Or perhaps two.
What exactly is big data?
Which begs the question: what exactly is big data, apart from the latest buzzword used to describe data?
At last year’s Gartner Symposium, I attended a session during which one of Splunk‘s customers provided a case study as to how they were using big data to provide personalized shopping experiences. And that session kicked off with the best definition of big data I’ve heard so far: big data is any pool of data that is too unwieldy for you to understand.
That definition actually isn’t too far off. Wikipedia defines big data as “the term for a collection of data sets
so large and complex that it becomes difficult to process using on-hand
database management tools or traditional data processing applications.” This accommodates not only traditionally structured data (such as average handle time) but also a plethora of unstructured data (such as social media updates) that are fare more unwieldy.
So I suppose one could make the argument that for some folks, average handle time is too hard to understand. (Don’t laugh; there are nuances to what comprises this single metric!) However, I would argue that a single metric, such as AHT or FCR, is fairly easy to process and analyze using traditional data processing applications.
What qualifies a data pool as “big data” is the variety, enormity and structure of the data sets involved. Armed with this information, I set out to determine if the tool in development was really a big data tool or just a data convenience tool.The question: how many data sources and types of data does it take to qualify as “big”?
General answer: if just considering the amount of data makes your head explode, it’s probably big data.
How do YOU handle big data?
So, question for our readers: is managing a tremendous amount of structured and unstructured data an issue for you in your contact center? If so, what would be helpful to manage, aggregate and render useful that data?