7 Feb 2016

"Data for data's sake": When is it the right time to invest hours in big visualisation projects?

When I started as a data journalist at The Telegraph, I was warned of "doing data for data's sake".

At the time, I wasn't exactly sure what this meant. Was I supposed to not take lots of time analysing complicated datasets, in favour of quick, snappy data-lead pieces?

Now I understand better: it means that, as a data journalist, there's a vast array of storytelling approaches for you to select. There's a choice between longer investigative or explanatory pieces, or quicker, news-driven posts, or approaches in between the two.

Among these, there are visualisations that can be produced in a matter of seconds with chart builders, and then there are others that have been specially designed and honed for one particular story.

Selecting the right approach is incredibly important. "Data for data's sake" means not taking the latter of these options, spending hours or days on complex graphical representations, when the story doesn't require it.

Why is it important to really think about how you are presenting your data?

News organisations are businesses. And, as an employee of that business, you need to be an efficient and productive employee. 

This can pose an issue as a data journalist, who by definition will often spend longer on stories - in order to check statistics, produce authoritative context and present stories in quality ways. We analyse  vast quantities of information, process the important bits and show it to our readers. And this takes time.

When it comes to presenting the story to your readers, data journalists have to think about the methods they use. They have to consider the amount of time they're spending on one specific project; if the hours they're using to create one visualisation is helping the story.

Does it assist the readers' understanding? Is it too complex? Is there a valuable return for this resource-intensive project? Is there a simpler, or reusable, option which tells the same story in an equally effective way?

Ultimately, as journalists, we need to write for our audience

As fun as they can be, complex visualisations - no matter how well designed they are or how many hours have been invested in them - can actually hinder a reader's experience.

Sometimes, a "data for data's sake" approach can lead to data journalists producing a resource-intensive interactive because it's attractive, and this can lead to journalists getting carried away and forgetting the core principle: is this helping to tell the story to the reader?

If a reader's first reaction to a visualisation is "that's confusing" or "that's complex", it probably isn't aiding their experience.

Investing time is important - for the right project

Of course, there is always the right moment to invest lots of time in data sourcing, analysis and visualisation.

The presentation of data shouldn't be "dumbed down". I am not saying that every data-led story can be reduced to simplistic bar charts.

It's just about assessing the specific project you're working on and working out the suitable strategy for it. This may be a comprehensive visualisation that shows the reader the full picture on the topic at hand; it could be a couple of line charts highlighting a couple of the story's main aspects; or it may be a more traditional report, in the form of words, based on the data you've found.

If a story is complicated and important to your reader, a sophisticated interactive may be the best approach. If a graphic helps communicate a story and makes it more accessible to the reader, then data visualisation is worth the time.

Some interactives, however, risk being overly sophisticated, intricate or complex. While none of these features are bad on their own, if they hamper the ability to tell the story, then they're not doing their job. 

When is it right to not visualise data?

A common mistake for students of data journalism is to get over-excited by visualisations. They are, after all, often the most fun, interactive and creative part of data-led projects.

But sometimes the best way to tell a data-led story is to write it. With words.

When it comes to the later stages of working out how to present your story, design for your audience and focus on what you're trying to tell. Ask yourself whether your visualisation aids the story, no matter how much you've personally invested in one particular idea.

If the answer is no, then perhaps the most suitable way to present your data is to write about it in an accessible manner. This approach to data journalism is often overlooked, but can be a powerful way to communicate data-led stories.

Avoid data for data's sake, and instead use data for what it's good at: opening up new possibilities in journalism and storytelling, in order to improve the reader's experience.