Why We Need To Kill “Big Data”

It’s the New Year and along with resolutions about eating healthier, being kinder and exercising more frequently, I’d like to add one more to the list. Let’s banish the term “big data” with pivot, cloud and all the other meaningless buzzwords we have grown to hate.
To be completely honest–I have been one of the bigger abusers of the term in posts, as you can see here, here and here. It seems like every enterprise startup nowadays is in “big data.” There are even venture funds devoted to investing in “big data” startups.
Why have I grown to hate the words “big data”? Because I think the term itself is outdated, and consists of an overly general set of words that don’t reflect what is actually happening now with data. It’s no longer about big data, it’s about what you can do with the data. It’s about the apps that layer on top of data stored, and insights these apps can provide. And I’m not the only one who has tired of the buzzword. I’ve talked to a number of investors, data experts and entrepreneurs who feel the same way.
According to Vincent McBurney, ”Big Data” originates from Francis Diebold of the University of Pennsylvania, who in July 2000 wrote about the term in relation to financial modeling. That was over 10 years ago. In the meantime, so much has happened since then with respect to how and what people can do with these enormous data sets.
And big data is not just about the enterprise. The fact is that every company, from consumer giants like Facebook and Twitter to the fast-growing enterprise companies like Cloudera, Box, Okta and Good Data are all big data companies by definition of the word. Every technology company with a set of engaged regular users is collecting large amounts of data, a.k.a. “big data.” In a world where data is the key to most product innovation, being a “big data” startup isn’t that unique, and honestly doesn’t say much about the company at all.
According to IBM, big data spans four dimensions: Volume, Velocity, Variety, and Veracity. Nowadays, in the worlds of social networking, e-commerce, and even enterprise data storage, these factors apply across so many sectors. Large data sets are the norm. Big data doesn’t really mean much when there are so many different ways that we are sifting through and using these massive amounts of data.
That’s not to under-estimate the importance of innovation in cleaning, analyzing and sorting through massive amounts of data. In fact, the future of many industries, including e-commerce and advertising, rests on being able to make sense of the data. Startups like GoodData, Infochimps, Cloudera, Moat, and many others are tackling compelling ways to actually make use of data.
Another fact worth pointing out is that enterprise companies like IBM, large retailers, financial services giants and many others have been parsing through massive amounts of data for some time now, before this word was even coined. It’s just that the types of data we are now parsing through is different, and we don’t need to be using these data analytics systems through on-site data centers.
So let’s figure out a different way to describe startups that are dealing with large quantities of data. Perhaps it’s about the actual functionality of apps vs. the data. It’s the New Year and a great time to brainstorm over ways we can avoid “the term that must not be named.”