{"id":1322,"date":"2017-03-12T23:43:59","date_gmt":"2017-03-12T23:43:59","guid":{"rendered":"https:\/\/blogs.scu.edu\/dataviz\/?p=1322"},"modified":"2017-03-12T23:43:59","modified_gmt":"2017-03-12T23:43:59","slug":"telling-stories-or-solving-problems","status":"publish","type":"post","link":"https:\/\/blogs.scu.edu\/dataviz\/2017\/03\/12\/telling-stories-or-solving-problems\/","title":{"rendered":"Telling Stories or Solving Problems"},"content":{"rendered":"

The data visualization landscape can be divided into two broad categories:<\/span><\/p>\n

Hedonistic Visualization<\/span><\/strong>: It only shows how cool something is or represents an “Interesting to know” \u00a0information. Here is an example –\u00a0<\/span><\/p>\n

https:\/\/fivethirtyeight.com\/features\/when-donald-trump-attacks-gop\/<\/a><\/p>\n

Narrative Visualization<\/strong>: It supports a narrative (often journalistic, sometime scientific).\u00a0Data Journalism is all about telling a simple story in the most attractive way to entertain the audience. But the problem here is there is a high chance of over exaggeration to bring out an interesting story.\u00a0While data journalism is a tough art, the other extreme of narrating a scientific story is harder. In order to demonstrate the hypothesis scientists have generated, they face the challenge of where to stop overloading the audience with too much information. Do they have an option? Will the scientists be able to convince the audience if they condense their visualizations?<\/p>\n

How about Problem Solving Visualizations?<\/strong><\/p>\n

This is a less commonly known category that is gaining importance. Singapore\u2019s MRT Circle Line was hit by a spate of mysterious disruptions in recent months, causing much confusion and distress to thousands of commuters. Data scientists at GovTech\u2019s Data Science Division in Singapore used visualization to discover the origin of this recurring problem.\u00a0The below article discusses about the interesting case:<\/p>\n

https:\/\/blog.data.gov.sg\/how-we-caught-the-circle-line-rogue-train-with-data-79405c86ab6a#.sebeshx7o<\/a><\/p>\n

Important point to note here is for most of the problems in automated systems, solutions really need to come from human reasoning(in this case through a series of visualizations). Hence visualizations are very powerful problem solving tools.<\/p>\n

References:<\/p>\n

http:\/\/fellinlovewithdata.com\/<\/p>\n

https:\/\/blog.data.gov.sg\/how-we-caught-the-circle-line-rogue-train-with-data-79405c86ab6a#.sebeshx7o<\/p>\n

When Trump Attacks!<\/a><\/p><\/blockquote>\n