Big data is a handy catch-all term for the en masse collection of data ubiquitous in modern society. If you have trouble understanding the value of big data in your business, the problem might not be the idea of big data itself, but rather your interpretation of it. What data you have and how you use it is the keystone of actually making sense of big data.
One of the most important things to realize about big data is that you need a wide variety of information to be able to draw meaningful conclusions. Simple problems like “at what time of day does our website see the most traffic?” and “what is our best day of the week for sales?” aren’t the realm of big data – they’re basic ideas easily seen in any spreadsheet or sorted out in an SQL query. The so-called three V’s of big data – volume, variety, and velocity – are a better start. These characteristics reference the ideal big data system’s ability to rapidly collect a large amount of disparate data and analyze them in real time.
Big data systems, therefore, are the ideal scientists. They observe and learn from their surroundings, drawing conclusions from provable statistics rather than working with preconceived notions. A good big data system will run itself – it will notice trends in the data gathered and report them for inspection, while at the same time making all of its data available for human review. The issue here is not only the volume of the data, but rather the application and analysis of them as well.
It’s tempting, however, to get caught in the trap of thinking that just because you’ve collected a lot of data, your work is done. Simplistic definitions and buzzword status means that the idea of big data has become diluted in daily use. If you’re having difficulty understanding the value of big data, chances are you’re stuck in the superficial idea of just having a large database – focus your effort instead on developing your network configuration tools to not only track data, but also look for some meaningful conclusions from them.
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There are a wide variety of architectures available for developing big data analysis systems, many of which have been developed specifically for the challenge that such large amounts of data – and the intricacies of their interpretations – present. It’s important to realize, however, that this is still a relatively young field, and there is much work that needs to be done before big data analysis reaches real everyday use. The abilities of any proposed systems to access, sort, and interpret data are all key to the development of those systems, as the massive quantities of data often require special memory architecture (for example, SSDs rather than HDDs) as well as specialized algorithms to sort through them.
No matter what your approach to the world of big data, however, by far the most important component to remember is that the idea of big data only becomes useful when you think about its applications. Simply gathering terabytes of data just isn’t enough. You’ll need some way to set up methods to interpret that data, be it your own specialized routines or more general solutions; the volume of the data in question is an important piece of the puzzle, but the central idea is that of interpretation. This, rather than the surface-level understanding of the buzzword of big data, is where the value really lies, and where the concept can mean something to your company.
