Let’s say something once and get it out of the way. There is an incredible amount of data out there for the taking. More than we ever had access to before. Frankly, there is more actionable data out there than we could even understand. Today’s businesses have access to more information about their infrastructure, markets, and customers than ever before. They can access data related to all of these factors as well as how each works together with the others. Well … they can … as long as their data science and Big Data protocols can keep up.
The biggest problem? Figuring out which data is best for your business now, which should be stored for later and which can stay right there on the cutting room floor. But you can’t answer this question unless you have a properly trained, skilled team of data scientists working on it for you. If your business is unwilling to invest in data collection, categorizing and extrapolation, you might end up with a huge mess. The data equivalent of Lucy and Ethel trying to keep up with the desserts on the conveyor. The data keeps coming, but you don’t know how to capture it or what to do with it once you have it.
Data capture and analysis requires – yes, requires – the best possible informed oversight. This is not just about cross-training some guy in another department. You need someone who understands data, an expert not only in current methods but in extrapolating ways to make those methods better. The right sort of data professional will enable your business to properly “read” and “understand” the data being gathered. If your team doesn’t understand how to read the metadata and can’t determine which data is the most valuable to your company, you are simply letting data stack up without getting the most out of it.
Data analysis is not a one size fits all situation. Remember, not all data is created equal, and the importance of various data sets is dependent on many factors including your industry, business, and target customer. It also includes how you interact with that customer, what sort of information you gather, and when you get it … among other things. The point here is just because something worked somewhere else doesn’t mean it will work for you. It certainly might, but will probably need some tweaking. And those adjustments will need to be made more than once.
Speaking of which, data sets are not static. Just because one set of data has been analyzed or categorized does not mean you are done with it. The real beauty of big data is in the way data can be mined, connected and organized in new and different ways. Connections that were not possible a year or two ago can be made now. This trend will continue. That means yesterday’s disconnected and disparate data may very well be the missing link connecting today’s data with tomorrow’s data. So, you can’t just leave it sitting up there on the proverbial shelf indefinitely.
And, finally, you may have all of this working for you. But if your data science teams are not asking the right questions, you will never get the answers your business needs to get the most out of its data. What questions should you ask? That depends on a lot of different factors. That’s why you need a team that understands data science. Not just some people who can ‘do the work’ but also people who understand what they are actually doing.
Roman Temkin is a real estate developer who embraces social media and technology trends.
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