Not All Data is Created Equal

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.