Do Not Worry About Big Data
A recent article in the Guardian asked if “we” should be “worried” about Big Data. In a word, no. But let’s take a serious look at the question before we explore the answer. The Guardian article begins with a discussion of a quote, often attributed to Joseph Stalin: “the death of one man is a tragedy; the death of millions is a statistic.” No matter how you feel about that quote, the implications in the article are that numbers are scary, so massive numbers – such as are encountered with Big Data – are terrifying. Well, I guess they could be if you allow big things to frighten you just because they are big or because you don’t fully understand them.
Sure, there is no way for people to quantify and process the sheer amount of data being gathered and classified by Big Data protocols. In fact, there’s no way for people to quantify and process a fraction of that data. That’s why data science exists. We need the help of these programs to realize the advantages locked away in all that data. That valuation, based on something that doesn’t have a specific monetary value, is called contingent valuation. It’s a sort of scaled importance based on assumed or understood value or personal value.
For example, one individual may look at Big Data and see a massive, jumbled mess of “stuff” of which nothing can be extrapolated. However, a data scientist can look at the same set of data and develop protocols that mine that data for vital information that will make an organization more efficient and a business more profitable.
Another issue addressed in the Guardian article is something called “extension neglect.” The idea here is that, in many cases, when something has no set value, we don’t know how to evaluate that something in mass quantities. For instance, you might be willing to pay $5 for a gallon bag of packing peanuts, and yet not be surprised to find a garbage bag full of packing peanuts for the same $5. Outside of a shipping company, who knows how much packing peanuts cost, right? This is the same idea behind Big Data. If some data is valuable, lots of data is also valuable. But how valuable? Well, if you don’t understand both the potential and how to extract that potential value, you can’t quantify it. Therefore, the tendency is to toss out an – often drastically undervalued – figure and just assign it, regardless of data set size or scope.
The bottom line is this, Big Data might be tough to quantify and easy to misunderstand…but it’s only “scary” if you let it be…and it can be very profitable if you take the time to answer the questions that might seem too worrisome to ask.
Entrepreneur & Philanthropist Jonah Engler says focusing on big data can increase revenues and profits.