Big data is a trend that’s gaining traction in the business environment. By taking a close look at the data that you collect, and identifying trends, you can potentially predict how your business can perform, and how your clients will respond to your products or services. Yet, there are two major questions that you need to ask: how are you going to use this data, and is the data that you’ve collected specifically to achieve that goal?
Big data, according to Gartner’s IT glossary, is “high-volume, high-velocity and/or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation.” In other words, businesses collect this data, and then use tools to analyze it to find trends and other useful information that can be used to improve the way that they do business. Yet, there’s one thing that can hold businesses back, and it’s in the actual data that they collect.
The problem here is that, even if you manage to collect all sorts of data and analytics, how much of it is actually useful and relevant to your organization’s operations? For example, as an IT company, we could take a critical analysis of support requests that technology companies receive, and see if there are common themes among them. This would help us by allowing us to build out workflows to combat the commonly-occurring issues. However, since we typically work with SMEs, any information related to how enterprises handle their technology would be, while somewhat helpful, not nearly as useful as examining the major pain points of small businesses.
For your organization, big data, depending on what your goods and services are, can help you better target your audience and find potential buyers. Therefore, you want big data that accounts for your target audience, and information outside this realm won’t necessarily help you.
Granted, big data doesn’t always seem to make sense; at least, not for Tom Goodwin at Forbes. He explains that the human condition itself is counterproductive to big data, and that we often act in unpredictable ways: “Big Data doesn’t get how weird we are. Big data can’t explain how I can be a Guardian reading, Whole Foods loving, Golf playing guy that owns an old BMW with spinning chrome wheels. Well, I know I can’t. People are irrational, they do things for strange reasons that even they don’t understand. They may explain it, but they will post rationalize to seem more logical.”
Basically, what big data comes down to isn’t just about the data that you collect, but what you manage to do with it. While you might be able to predict some things, it’s important to take what you collect with a grain of salt, as when dealing with people, chances are that when you try to predict their actions, you’ll continue to be astounded on a daily basis. That’s just how we are as individuals, and until an algorithm can understand that, big data will be an interesting way to almost guess an outcome.