Data visualisations is the process of turning into graphics to make information easier to understand or correlate; however, it does far more than this.
The data can be interpreted using various types of visual imagery dependant on the services being analysed. There are many ways it can be done.
If it is information or address-based tools like Google Fusion Tables can be adopted to visualise the data on a map, whereas if the data is numerical tools such as Many Eyes are ideal for presenting it in the form of a chart.
Aside from the obvious reasons for visualisation, there are several other positive outcomes of implementing this style.
On one hand a story could be something that explains the meaning of a certain dataset; however, a story is also generally something which can be built upon – think of ideas that you can do with the data. For example, certain websites like dbpedia.org are capable of aiding crowd sourcing and language translation, whilst making sure that forms are machine-readable.
Data has been visualised to strengthen campaigning approaches and this concept of successfully marketing the information and engaging a specific audience throughout the course of the campaigns journey and as events unfold, is gaining increasing popularity as time goes on.
Non-governmental organisations such as charities are capable of sourcing, preparing or providing open datasets that were previously unavailable, thus supporting an extensive assortment of visualisations and mash-ups. These can be added to a single page with the inclusion of how well various datasets combine; amplifying the effect of any support campaign.
Using tools that create interactive visualisations or widgets such as the Mashup Hub, will allow the public access to content they can re-hash for their own blogs or websites, which will follow the campaigns general story.
Uniting statistics and stories – including photos, audio and video – highlights the impact of distant actions. It also encourages the visualisation for stories of consequences.
Data journalism is primarily the process of communicating the data story effectively and with a fast turn-around. Assuming the library of data is planned accordingly; campaigners should be able to help journalists find stories sooner, aid the important data-journalistic pattern of revealing “where a political or business system is not working or is corrupt and then digging down to find individuals responsible,” and reciprocate news stories through the fast creation of their own infographics, to acquire benefit from any general interest in the story.
By supplying freely-available data, individuals can explain the story of their own experience or knowledge. Whilst moderation is a factor, the key is to allow the freedom to let people’s data stories flow naturally.
Nothing can account for this greater than giving people space to build on their data and allow it to evolve; encouraging patterns and trends. Often bringing together the data from different groups can create interesting mash-ups, through which specific data relationships can be discovered and so their stories can be greatly enforced.
What are the best examples of stories using data you’ve found? We’ll feature them on our website. Even better, what stories could you tell using our data?
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