For the last few years, I’ve been skulking round the edges of the whole “data journalism” thing, pondering it, dabbling with related tools, technologies and ideas, but never really trying to find out what the actual practice might be. After a couple of twitter chats and a phone conversation with Mark Woodward (Johnston Press), one of the participants at the BBC College of Journalism data training day held earlier this year, I spent a couple of days last week in the Harrogate Advertiser newsroom, pitching questions to investigations reporter and resident data journalist Ruby Kitchen, and listening in on the development of an investigations feature into food inspection ratings in and around the Harrogate area.
Here’s a quick debrief-to-self of some of the things that came to mind…
There’s not a lot of time available and there’s still “traditional” work to be done
One of Ruby’s takes on the story was to name low ranking locations, and contact each one that was going to be named to give them a right to response. Contacting a couple of dozen locations takes time and diplomacy (which even then seemed to provoke a variety of responses!), as does then writing those responses into the story in a fair and consistent way.
Even simple facts can take the lead in a small story
…for example, x% of schools attained the level 5 rating, something that can then also be contextualised and qualified by comparing it to other categories of establishment or national, regional or neighbouring locale averages. As a data junkie, it can be easy to count things by group, perhaps overlooking a journalistic take that many of these counts could be used as the basis of a quick filler story or space-filling, info-snack glanceable breakout box in a larger story.
Is the story tellable?
Looking at data, you can find all sorts of things that are perhaps interesting in their subtlety or detail, but if you can’t communicate a headline or what’s interesting in a few words, it maybe doesn’t fit… (Which is not to say that data reporting needs to be dumbed down or simplistic…) Related to this is the “so what?” question..? (I guess for news, if you wouldn’t share it in the pub or over dinner have read it – that is, if you wouldn’t remark on it – you’d have to ask: is it really that interesting? (Hmm… is “Liking” the same as remarking on something? I get the feeling it’s less engaged…)
There’s a huge difference between the tinkering I do and production warez
I have all manner of pseudo-workflows that allow me to generate quick sketches in an exploratory data analysis sort of way, but things that work for the individual “researcher” are not the same as things can work in a production environment. For example, I knocked up a quick interactive map using the folium library in an IPython notebook, but there are several problems with this:
- to regenerate the map requires someone having an IPython notebook environment set up and appropriate libraries installed
- there isn’t much time available… so you need to think about what to offer. For example:
- the map I produced was a simple one – just markers and popups. At the time, I hadn’t worked out how to colour the markers or add new icons to them (and I still don’t have a route for putting numbers into the markers…), so the look is quite simple (and clunky)
- there is no faceted navigation – so you can’t for example search for particular sorts of establishment or locations with a particular rating.
Given more time, it would have been possible to consider richer, faceted navigation, for example, but for a one off, what’s reasonable? If a publisher starts to use more and more maps, one possible workflow may to be iterate on previous precedents. (To an extent, I tried to do this with things I’ve posted on the OU OpenLearn site over the years. For example, first step was to get a marker displaying map embedded, which required certain technical things being put in place the first time but could then be reused for future maps. Next up was a map with user submitted marker locations – this represented an extension of the original solution, but again resulted in a new precedent that could be reused and in turn extended or iterated on again.)
This suggests an interesting development process in which ever richer components can perhaps be developed iteratively over an extended period of time or set of either related or independent stories, as the components are used in more and more stories. Where a media group has different independent publications, other ways of iterating are possible…
The whole tech angle also suggests that a great stumbling block to folk getting (interactive) data elements up on a story page is not just the discovery of the data, the processing and cleaning of it, and the generation of the initial sketch to check it could be something that could add to the telling of a story, (each of which may require a particular set of skills), but also the whole raft of production related issues that then result (which could require a whole raft of other technical skills (which are, for example, skills I know that I don’t really have, even given my quirky skillset…). And if the corporate IT folk take ownership of he publication element, there is then a cascade back upstream of constraints relating to how the data needs to be treated so it can fit in with the IT production system workflow.
Whilst I tend to use ggplot a lot in R for exploring datasets graphically, rather than producing presentation graphics to support the telling of a particular story. Add to that, I’m still not totally up to speed on charting in the python context, and the result is that I didn’t really (think to) explore how graphical, chart based representations might be used to support the story. One thing that charts can do – like photographs – is take up arbitrary amounts of space, which can be a Good Thing (if you need to fill the space) or a Bad Thing (is space is at a premium, or page (print or web) layout is a constraint, perhaps due to page templating allowances, for example.
Some things I didn’t consider but that now come to mind now are:
- how are charts practically handed over? (As Excel charts? as image files?)
- does a sub-editor or web-developer then process the charts somehow?
- for print, are there limitations on use of colour, line thickness, font-size and style?
Print vs Web
I didn’t really consider this, but in terms of workflow and output, are different styles of presentation required for:
- data tables
If you want data tables, there are various libraries or tools for styling charts, but again the question of workflow and the actual form in which items are handed over for print or web publication needs to be considered.
Being right/being wrong
Every cell in a data table is a “fact”. If your code is wrong and and one column, or row, or cell is wrong, that can cause trouble. When you’re tinkering in private, that doesn’t matter so much – every cell can be used as the basis for another question that can be used to test, probe or check that fact further. If you publish that cell, and it’s wrong, you’ve made a false claim… Academics are cautious and don’t usually like to commit to anything without qualifying it further (sic;-). I trust most data, metadata and my own stats skills little enough that I see stats’n’data as a source that needs corroborating, which means showing it to someone else with my conclusions and a question along the lines of “it seems to me that this data suggests that – would you agree?”. This perhaps contrasts with relaying a fact (eg a particular food hygiene score) and taking it as-is as a trusted fact, given it was published from a trusted authoritative source, obtained directly from that source, and not processed locally, but then asking the manager of that establishment for a comment about how that score came about or what action they have taken as a result of getting it.)
I’m also thinking it’d be interesting to compare the similarities and differences between journalists and academics in terms of their relative fears of being wrong…!
One of things I kept pondering – and have been pondering for months – is the extent to which templated analyses can be used to create local “press release” story packs around national datasets that can be customised for local or regional use. That’s a far more substantial topic for another day, but it was put into relief last week by my reading of Nick Carr’s The Glass Cage which got me thinking about the consequences of “robot” written stories… (More about that in a forthcoming post.)
Lots of skills issues, lots of process and workflow issues, lots of story discovery, story creation, story telling and story checking issues, lots of production constraints, lots of time constraints. Fascinating. Got me really excited again about the challenges of, and opportunities for, putting data to work in a news context…:-)
Thanks to all at the Harrogate Advertiser, in particular Ruby Kitchen for putting up with my questions and distractions, and Mark Woodward for setting it all up.