Tom Hanks big… data
Tom Hanks career has spanned 35 years. It started with TV in the early 80’s. Remember Bosum Buddies? He broke into movies with a Splash in 1984. And by 1988, Tom Hanks was big. Oddly, he almost wasn’t… the role almost went to Robert De Niro.
At the time Tom was considered a great comedian. He had a track record on TV, was a regular host on SNL, and had multiple box office wins with Splash, Bachelor Party, and Dragnet.
big was funny. big was successful. big was a feel good movie. And it very easily might have been the end…
Tom’s career after big is a bit less impressive. Turner & Hooch was not a horrible affair… by box office standards… Joe versus the Volcano was. And remember Punchline… didn’t think so.
Comedians come an go. In 1990, you would have been perfectly in the right to believe Tom Hanks was on his way out. His star was fading and he was grasping at roles like Tales from the Crypt.
So where is this analogy going? What does any of this have to do with data?
Today data is big. Big Data is mentioned routinely in popular culture and media. It is an area where a lot of money is being spent and made. It makes many CEO’s feel good. Or at least it did…
Actually, the big data star may already be fading. The self-deprecating humor has started (Hanks had his Girl Watcher skits). And the box office is not keeping up with the development costs.
In the early 90’s, Tom Hanks career began a transformation
Unlike so many others, whose careers faded to obscurity, Tom reignited his career by the mid-1990s. It started with a League of Their Own. It was a mild adjustment — with mild success.
He went much further with Sleepless in Seattle. But the change was still a subtle one. Sleepless was not a huge departure. It was a romantic comedy. His co-star, Meg Ryan, had been part of the disappointing Joe vs the Volcano. The subtle adjustments however provided box office gold. And Hanks was just getting started…
Tom completed his transformation with a one-two punch; Philadelphia and Forrest Gump. These movies were true and total transformation. Not the last for Tom, as he has gone on to add directing and voice overs to his portfolio. But the changes he made to take on those two roles assured Tom Hanks would not fade into obscurity.
Back to Big Data
Big Data needs to undergo a transformation of its own. For too long, it has relied on data mining driving statistical modeling. And honestly, it is getting old.
Not that there is anything wrong with data mining, and statistical models can be very valuable. But Big Data is struggling to connect with the audience, and throwing in a volcano or K9 co-star is not likely to turn this ship around.
Big data needs to adjust. It needs to make those subtle changes that served Tom Hanks so well. Hanks pivoted his style, dropped the absurd story-lines and focused on leveraging his charisma and likability. This made his story easier to connect with and far more compelling. Big data needs to connect.
Statistical models have long been difficult to connect with. To most, they are black boxes or, if you’ll allow, rumbling volcanoes waiting for a sacrifice. The natives just don’t understand them.
Big Data needs to become a Big Help
To connect with the audience, don’t confuse them. Don’t scare them. Connect them. Data tells the story of the customer. That story is compelling and that is the story the client needs and wants to hear.
Eventually the audience connected so well with Hanks, they allowed him to not just play the roles, but to direct the stories as well. Soon after, he was welcomed into the world of animation. Big Data can take that journey as well, directing business action and bringing immersive and engaging new visualizations to life. But it needs to connect the client first, in order to establish that trust.
This transformation will take some time. It is not something you wish for over night. Or at least you probably shouldn’t… one more lesson from big.
Quintessentially is an article format created by Corsair’s Institute to increase the reader’s comprehension of key concepts by providing several distinct views on a central theme. For more articles from Data, Quintessentially — click here.
