I must confess: I was slightly reluctant before joining the Inaugural Colloquium of D&N Society. I was afraid we will hear a lot about the mantra of data without even scratching the surface of “what data is really about?” and “how can we make the most of it without making it larger than life?”. Fortunately, I couldn’t have been more wrong. The colloquium was a success and bellow I will describe why I felt reluctant and what saved the day for me during our first meeting.
The number of journal articles and academic volumes dealing with “data” and “journalism” has risen exponentially in the past years. As someone who is slowly getting more acquainted with the hype, I have noticed two annoyances I will describe bellow.
The first one is very visible: data is (almost) all the time described as self-sufficient and as liberating. “Forget everything you knew, data is the new king!”, “how on earth we lived so far without more data?!?”. Sounds familiar? This approach is not only too simplistic but also wrong. Data is an asset, and as any asset, it is up to its users how well they deal with it. Data is neither negative or positive per se. It takes the shape of the context and of the author’s own purposes. Of course, this is nothing new, as statistical data used to be misrepresented long before the arrival of “big data”. What I feel (sorry for this non-academic slip-up) to be new is the high expectations we have from data. Instead of using data to help explain a particular event or phenomena, we are gazing at data and sometimes we don’t bother to see beyond. All the visualizing tools around data are understood as having a self-describing aura. We scrap excel files, clean them, and produce visually-rich animations. That’s great, but shouldn’t we go beyond that? More often than not, we refuse to move beyond our data and try to come up with over-arching explanations on why the data looks the way it does. We don’t discuss our limitations, we don’t even acknowledge data to be more useful for answering “who” and “what” type of questions and to remain tricky when it comes to “how” and “why” sort of questions.
The second annoyance is to see journalists jumping in the data bandwagon and proclaiming themselves as data specialists overnight. I call this to be the king of the hill achievement. Of course, one can self-proclaim herself/himself a data specialist if no one else around is dealing with data. Jumping from a tape recorder to many terabytes of data is more difficult than it looks to be at first sight. Most of the times, journalists getting acquainted with big data overnight will continue to have the same type of stories, only this time with more visual artifacts. Instead of making use of good data, you will see journalists getting lost into data and trying to make complicated statements with little to no evidence. Just as we used to do when we were kids and thought we had the best toys in the world…
Hence, I was pleasantly surprised to see Mr. Hu Pili, director of Initium Lab Hong Kong as the first speaker of the colloquium. He was brave enough to highlight both the strengths and the limitations of data-driven journalism. As exemplified through his working projects, Hu Pili does not expect for data to provide all the answers in the world. However, he makes the data work for his journalistic stories and to uncover angles that otherwise would stay hidden. An eloquent example is a project scrutinizing the Legco voting patterns. For me, this was the highlight of Hu Pili’s presentation because it shows how data and journalistic values and norms come together. While data-driven, the project is journalistic valid. In this case, the data is used to enrich the journalistic story, not to suppress it.
I can only hope for the field to slowly move away from data determinism and towards a stage where journalistic values and norms are properly combined with the richness provided by data. In the end, data will be a game changer, but not a game substitute. Old journalistic values will continue to be necessary. The journalists who will thrive in the future are the ones who will be able to have the appropriate balance between data and skills.