These examples set a new standard for rapid access to context information to accompany the new information for breaking news. In the case of street maps and aerial/street views, this information required extensive investment long before the event occurred. In the case of the more recent information (street congestion, weather radar imagery, landslide risk assessments) there was a need for prior investment for models and technologies to provide this information on a timely basis. These investments were made on a global scale where the vast majority of this readily available capability may never been needed for matching with a breaking news story. But when a breaking news story does occur, we welcome the ready access to this information specific to the broader context of the story.
Oral story telling was the original big data. The various oral stories were saved in persistent memory and captured a large volume and variety. The invention and adoption of written works displaced the oral tradition and that brought and end to that earlier big data. In this sense, our current excitement about big data may be a rediscovery of a capability available our ancient ancestors. Big data and oral story telling tradition both offer inexpensive and durable means to manage a large number of distinct and very individualized stories. In the modern era, we are rediscovering the need to collect individual stories and thus granting them ability to circulate like what happened in the preliterate society of oral story tellers.
As in the computer interpolated images to simulate a faster frame rate, the reader sees this manufactured information as part of the same story. The the story becomes hyper-real. In movies the faster frame rate gives the impression of a cheaper production more frequently associated with daily soap operas. In journalism, the injection of author’s opinions leaves the reader with the impression of reading a cheap novel. Neo-NeoCon sums this up nicely as “When I read Erdely’s piece, it seemed to me that its style resembled a romance novel gone bad”.
Data-driven analytics currently thrives on wealth of available data that happened to be freely volunteered by others. The ultimate realization of the value of the data comes after the analytics and visualization presents a story that attracts an audience. To be relevant to the difficult and urgent problems, data science projects need to find ways to propagate the financial benefits of the final results back to the information sources in order to provide the necessary incentive to uncover the difficult to obtain data. The journalism market needs new incentives to redirect their skills toward collecting a vast number of first-person accounts. As I described earlier, the journalist’s skills are exemplified by the input and the output stages of data science projects. The mathematics and software for analytics and visualization need data to work with and story-tellers to attract audiences to the results.
Data science needs the skills of journalists for the data they can uncover instead of the narratives they can write. The challenge is how to pay them for data.