We also understand that not everyone is a data expert. Data visualization can help us spot trends and patterns that we might not otherwise notice.Īt Wyndetryst, we understand the importance of visual data. Additionally, visual data can be more engaging and interesting to look at than raw data. When data is presented in a visual way, it can be easier to understand and interpret. First and foremost, it helps us to make sense of data. There are many reasons why data visualization is so important. Our graphic designers work hard to create beautiful and informative graphics that bring your data to life! The Importance of Visualizing Data That’s why at Wyndetryst Graphic Design Studio, we take data visualization seriously. The article is available in English and in French.When data is visualized correctly, it can be easy to understand and appealing to look at. The second element is a good understanding on whish statistical analysis will make this data speak most truthfully about the population(be it people, equipments, or business enterprises)įor example analysing income distribution according ti their levels or mumber of enterprises acording to their number of employees or their yearly income requires special techniques to prepare date before obtaining a valid graphical representation…įor thoes interested, I have published on my website a paper on Pareto distributions” which enable a proper representation of such data on large populations that can be ranked by size and that empirically observed experience all tend to follo a log-normal distribution, Pareto distributions beeing a sub categori of these dietributions in the part of the curve when it is becoming a straight line on a log log scale. The data analysis served me as basis to forecastin models for networking products but also for very advanced business opportunities.įor me the first element of a good graphical representation is input data quality I’ve had about 25 years of data analysis and model making for IBM, If, however, you’re looking for some inspiration for colors and form or just want some beautiful pictures to put on your coffee table, this book is for you. If you want lessons on how to visualize and analyze data or are only looking for traditional statistical graphics, then steer clear of Data Flow 2. It is presented like an art book after all. The paper doesn’t feel cheap like some books these days. Many of the examples in the book, you’ve probably seen here on FlowingData.Īlso like the first, the book feels nice in your hands. There are lots of good examples of of data graphics and data art, mixed with a healthy dose of creative thinking. There is of course plenty of good Data Flow 2 has to offer. Darn my limited American multilingual education. I do wish I was able to understand the few pages of commentary by Andrew Vande Moere ( information aesthetics), Manuel Lima ( visual complexity), and Steve Duenes (New York Times) though. What I’d like to see is a book that categorizes visuals by the stories they tell, like correlation, outliers, relationships, time patterns, and geography. So the part about data graphics that excites me the most is the story that the graphics tell, and less about the shapes that are used. I think for me, after seeing so many graphics, especially this past year, a lot of work starts to look the same. I initially thought Data Flow 2 was much more rooted in the art and design aspects of data graphics than the first (because I really liked the first), so I flipped through the first book, and both are actually pretty similar in that regard. Essentially you’ve got data graphics presented as art.Īs I thumbed through the book, I couldn’t help but feel kind of bored this second time around, which might say more about me than it says about the book. Datalogy are graphics that pertain to health and dataesthetic are works that are more art than data graphic. Like the first, graphics are categorized by their aesthetic traits, as opposed to what the data is about: datalogy, datamaps, dataprocess, dataesthetic, datablock, datanets, datacurves, and datacircles. The title is exactly the same, save the 2. The two are really similar in layout and in the way the graphics are split up. Basically, if you liked the first Data Flow and could use some more inspiration, you’ll probably like this second edition. Last year, the first Data Flow was published, featuring the data graphics of some fine designers. So this review is actually my impression of Data Flow: Design Graphique et Visualisation D’Informations as a picture book with titles, which in a way it kind of is anyways. Unfortunately, I only understand English. Note: The review copy I received is in French.
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