Data Journalism is Here to Stay

Since its inception, the data-driven storytelling has been a part of the journalism industry, but it is now seeing a revival. Graphics desks, a department outside of the duties of newsrooms, are now becoming an integral aspect of their functioning. Those who create news graphics and data journalism are full-fledged journalists who collaborate closely with the reporters and editors.

Unfortunately, most Pakistani universities’ media and mass communication departments haven’t caught up. They don’t teach students how to think about data visualisation, how and why it works, and how to execute it correctly. Data visualisation is graphically displaying data to explore and uncover patterns, analyse and make sense of the patterns and communicate our results.

Media students should be taught how to create graphs similar to The New York Times, Vox, Pew and FiveThirtyEight.

When I teach a course in data journalism to Gen-Z, they are mostly surprised to learn that the data journalism has been around for a long time. I am often asked, which sort of chart should I use? Two online tools that attempt to increase the chart literacy are The Financial Times Visual Vocabulary and The Data Visualisation Catalogue.

The Financial Times Visual Vocabulary is a resource that assists journalists in selecting the right sort of visualisation for their subject. It displays nine categories of data connections (for example, deviation, correlation, and change over time); under each class is a selection of data visualisation examples from which you may pick to explain various types of trends graphically.

Similarly, The Data Visualisation Catalogue is a free online reference collection covering several sorts of information visualisation. You may sort the visualisation types by function to get the best chart, map, graph, and diagram for your data. Each visualisation type catalogue page offers a detailed explanation and structural breakdown. There are also links to samples of the visualisation type in action and tools to help you design your own data visualisation. Try browsing these two sites the next time you’re looking for the proper data visualisation to get some early ideas for which visualisation style would work best for your data and the narrative you’re attempting to tell.

Once you’ve mastered the fundamentals of charts and graphs, you may go to more complex data representations. If you don’t comprehend the fundamentals, you’re building on weak ground, much like learning how to be a good writer, keeping sentences brief, keeping the audience in mind and not overcomplicating things to appear clever, but instead transmitting meaning to the reader. You should also avoid going excessive with the data. Starting small and gradually expanding only when necessary is the most efficient technique to convey the narrative.

During the late 70s, Nigel Holmes brought the info-graphic to popular culture. He liked to utilise comedy and had a passion for distinctively portraying statistics, while working at the Time magazine from 1978 until 1994. Here’s one example: an info-graphic showing a lady with a bowed leg corresponding to the development of diamond prices.

Homes said, “From ankle to knee, the price gradually rises from the lowest to the greatest. By merging a picture with the graph, viewers could recall the graph’s spike form more simply.”

Edward Tufte was a Yale University professor who published “The Visual Display of Quantitative Information” in 1983. It is the most important book on data visualisation ever published. Tufte’s contribution to the graphical fidelity is taught globally in media departments. Graphic integrity is the accuracy with which the visual components portray the data. On the surface, this seems simple, but visual representation may be challenging. It can lead to erroneous perceptions of the facts and inaccurate conclusions. Tufte developed several rules to achieve good pictorial integrity.

They are as follows: tell the truth about the data; graphics must not quote data out of context; avoid distortion of what the data has to say; the representation of numbers, as physically measured on the surface of the graphic itself, should be directly proportional to the numeric quantities represented; there must be clear, detailed labelling; and well-known units are best for representing money.

Data must not be quoted out of context in graphics.

Tufte developed the “Lie Factor”, the relationship between the magnitude of the effect represented in the visual and the size of the impact revealed in the data. The physical depiction of numbers on the surface of the graphic should be precisely proportionate to the amounts depicted.

When creating or viewing a proportionate shape chart, remember this basic rule: when we double the radius of a circle, we quadruple its area, not double it. Changes in the data being represented should always match the changes in the visual scale. In addition to graphical integrity, Tufte advises designers to minimise design variance. Too much design variety causes viewers to focus on other things rather than the facts shown in the graph. Don’t use 3D, for example, unless you’re genuinely mapping the third dimension. Fancy 3D charts have a certain allure to them. However, in most situations, the third dimension — or depth — is mainly cosmetic and complicates data processing.

Most data visualisation specialists believe that utilising 3D special effects is unnecessary and, at worst, misleading.

Just like as a news reporters we were taught to use the fewest words possible, data journalists who practice simplicity make more sense than those who use all the bells and whistles.

(The writer is the CEO of Iqra University Extension)