I visited the Sydney Museum of Contemporary Art yesterday. Among the many works—some marvelous, some not so much—one in particular caught my attention. It was titled something like “My life is nuts.” Here it is:
This piece of art consists of a peanut for everyday that the artist has lived. It is a “work in progress” in that each day the museum staff adds another nut to the pile.
Because I’ve been thinking a lot about so-called Big Data these days, I couldn’t help but see a connection between Big Data and this pile of peanuts. Just as this pile of nuts is an incremental representation of the artist’s days on earth, Big Data is an incremental increase in data volume, velocity, and variety. Two differences exist, however. The Big Data pile of nuts increases at an exponential rate, unlike the linear increase of the artists lifespan, and the artist hasn’t chosen to give herself a new name for arbitrary points along the growth of this pile. Data has been increasing at an exponential rate since the advent of the computer; it didn’t suddenly become big and it hasn’t passed some threshold that makes is qualitatively different than it was in the past.
Last Thursday I spoke at the ActuateOne Live event in San Francisco. Immediately before my keynote, Shaku Atre, a longtime thought leader in the field of business intelligence with a resume that dates back at least as far as mine spoke about the “Top Ten Rules of Big Data Systems.” When I listened to her presentation, I expected to hear opinions about Big Data that differed from mine, but I was surprised to find none. I’m sure that our opinions differ in some respects, but this wasn’t obvious from the content of her presentation. I pointed out to her afterwards that the contents of her presentation could have been titled “Top Ten Rules of Business Intelligence Systems” and given ten years ago with little alteration. She didn’t disagree. She said, “You are right—I seem to see Big Data as a fluid continuation of the past rather than a significant departure from the past, as you do.” Anticipating the allure of all things big, bigger, and biggest, Atre has already acquired the rights to the URL “humungousdata.com” and she led a panel discussion at the event titled “Humungous Data? No problem with business analytics.” In cautionary contrast, one of the slides in my presentation included the words “Big Data, little information.”
Jesus once taught a lesson using the parable of the wine skins.
No one pours new wine into old wineskins. If he does, the wine will burst the skins, and both the wine and the wineskins will be ruined. No, he pours new wine into new wineskins.
The fermentation of new wine requires a vessel that can expand. New wineskins were flexible, but old wineskins no longer had the capacity for expansion. Jesus was saying that his message was new—a departure from the past—and that it could not be contained within the paradigms of the past. It was qualitatively different, and as such, following him would require a new perspective.
I’m trying to teach the lesson that’s on the flip side of this parable. Only marketers (people who are trying to sell you something) pour old wine into new wineskins. Doing so wastes the capacity of the new wineskins, because expansion is no longer needed, and it misleads buyers into thinking that they’re getting new wine that is actually old. Big Data isn’t new. The purpose of the term Big Data as it’s being used by technology vendors and most technology thought leaders is to create an illusion of newness that potential buyers can’t live without. It is a marketing campaign designed to lighten the wallets of organizations. An organization that cannot derive value from the data that it already has will not suddenly derive value from it by installing the latest technology. The business intelligence industry (and now the Big Data industry) has always been good at making promises that it rarely fulfills.
Big Data is just a big, exponentially growing pile of nuts. Value can be derived from data, regardless of size, but only to the degree that you’ve developed data sensemaking skills. Only then will Big Data lead to Big Information.