Data visualisation has been around for a long time now all the way since the 17th century. It has been important to communicate information in the most effective manner to the audience, due to which constant innovation in the field is visible. Overtime, people have discovered new ways and new effective tools to visualise data. Tableau , Infogram, Plotly, Datawrapper are just few of the examples of the tool that make your life easy when it comes to data visualisation. And then I came across this infogram post that took me back to my 10th grade essay question “Technology, A blessing or A curse?”.

https://infogr.am/d073c128-c212-4521-b1d2-1fea642456e5

Usually I start my blog with what interests me about the chart or the positive points about it. However this time I sat there staring at the chart analysing it, trying to figure out what its trying to say. After a time lapse I came to concluded that the only positive point that this chart has is that we know the title of its story. It answers the basic description question: what? Followers on social media.

When DataViz tools makes the creators life easy, the viz should make the audiences life easier. But this chart doesn’t seem to do that. Below are the reasons why:

Fails to answer the description questions:

While the chart tells us it is talking about the followers on social media, it refrains from giving us other information such as which time period was this data collected.

Fails to have a claim

The chart compares the followers on social media for the companies: Apple, Google, Coca-Cola, Microsoft and Toyota. While Apple, Google and Microsoft are tech companies, Coca-Cola comes under the food and beverage company and Toyota is in automobile sector. Why compare the followers of companies that are not related to each other.

Repetitive attributes

On the Y axis we see that Youtube, Instagram, LikendIn, Twitter, Facebook being repeated over and over again. Where too much information cramped into a visualisation is a problem here the creator has put the same set of attributes five times.

Selection of Colour

All the companies have been assigned the same colour hence there is no way of differentiating based on colour. Although the x-axis has the company names listed and the colour is not needed there is again unwanted information at the bottom added to the visualisation.

Does not specify the unit of measure of the data

When you click on the circles it gives you a number, but no where it is specified what that number means ..Is it a percentage ? Is it in thousands , millions or billions.

Hence this chart has failed the three basic criteria of Data visualisation : Data, Claim , Aesthetics. Data Viz tools are no magic spell, they are here to help not do your work. Sometimes all people do is make use of tools to come up with a fancy chart but little do they think about the objective and subjective dimensions. Tools are a blessing indeed only if you know how to use them the right way. This chart however is a curse!

Redesign : https://public.tableau.com/profile/pooja5766#!/vizhome/FollowersonSocialMediaoftoptechcomapnies/Sheet2

 

 

India !! One of the fastest developing nations! The nation is growing they say, an ever growing progress rate is seen they say, we are reaching places they say, less conservative and more open they say ….and yet the women here are unsafe in their very own motherland. Its a man’s world indeed ! “Save the girl child” is the motto for the anti-female foeticide/infanticide campaign ,and they saved her or did they?

Women are subjected to violence on a daily basis. Not only do they fear getting out due to eve teasing, molestation, assault, acid attacks , but also the horror of domestic violence shatter their soul. The above graph tells us how the women in each state of India are treated. It gives us the percentage of domestic violence , minor , severe violence and sexual violence. It tells us the sob tale of India’s Daughter.

The Chart definitely shows us that the author is here to spread her word, she has done her research and wants to share it with the world in the hope for a better tomorrow. The intent and motive truly inspires me however the visualisation fails to do so. The data visualisation must tell us a story , it must be the picture that is worth a thousand words but instead the chart in itself feels like a thousand words. A bar chart is a simple way to showcase a point but when the criteria and data exceeds a limit it becomes task to read and interpret the story causing a loss of audience.

Today Data visualisation is seen as a form of art and by the looks of the graph the author is definitely no artist. The looks isn’t appealing and the overflow of data strains the viewers eyes. The colour selection also fails to impress when using distinct colours was the least that could be done. The order in which the states are placed in the x-axis got me puzzled, its isn’t is ascending or descending order of its location(ex.north to south) or its status of development or anything. It seems to be placed in descending order of the violence by the husbands, leaving the other causes scattered all over the place. Clearly aesthetics doesn’t exist here so lets check on the objective dimensions.

The chart does tell us its description : Violence against women in Indian States, although it fails to answer us when did this story occur? What year is this data from? Is it recent or old? Leaving the audience wonder is this India now or way back in history. The graph will explain you how women faced violence, was is domestic, sexual, minor or severe. And while no chart or form of visualisation can help us explain why India, a country where they worship multiple Goddess’, fail treat their women right, this visualisation doesn’t help the audience make any prediction either. For example , does this have a trend or pattern, which parts of the country do the women suffer more, is the south safer than the north? So with the available data if i were to portray the story I would simply map it. Below is the link to my recreation:

https://public.tableau.com/views/ViolenceonWomenIndia/Sheet4?:embed=y&:display_count=yes

Using the filled map feature that tableau provides we can highlight the region where the violence percentage is high giving us an idea as to what is happening where. It doesn’t overload us with data at the same time doesn’t hide any of it, hovering over the states will give us the stats for the same. This way we can put light to whats important while still including all the data. Also this way of visualising the data reveals that the south, west and extreme north provides a better environment for women while women in the mid, north and east still need help. It is perfect to spread awareness and call for help, showing us the states coloured with the blood of these women.

I would like to conclude this by saying that intent, motive, research, data are not enough to spread the word. You need to catch the eye of your audience and thats when aesthetics comes to the rescue. Saying that make sure your visualisation is truthful, functional, beautiful yet insightful and enlightening. Master the art of data visualisation and your story will spread across like fire in a hay farm.

Graph picked from Article : http://www.ideasforindia.in/article.aspx?article_id=105

 

 

Intriguing! Isn’t it? The above chart is a representation of the results of a year long quantified self project of diabetes control. It plots the blood sugar levels of Doug, who conducted the experiment, the day for the year 2012 and also shows us the miles ran on that day. At the first glance I couldn’t say that something this pretty could actually mean something this serious.

Doug has been a diabetic patient for 32years now, 2012 as he claims was the healthiest year of his life and he proves it through the results of his experiment. He tracked every blood sugar readings, every insulin dose, every meal and all my activity data. He certainly in his visualisation has covered the dimensions of data visualisation. To list a few : The chart is visually appealing to the audience and hence has the beauty dimension covered, it contains his personal experiences and hence insightful and his results definitely encourages other patients to work towards blood sugar control and hence enlightening.

Getting into the details, the chart answers your describe questions : What? Blood sugar level and miles ran, When? Year 2012, Who? Doug. Also answering you the explanatory questions: Why ? To control diabetes, How? By self tracking and exercise. It hasn’t stepped back from predicting that this procedure helps you lead a healthier life and prescribing self tracking and exercise to diabetes patients. Not only has he plotted his test results and miles ran, but has noted the important life events helping us get a better understanding to his story he is trying to tell us through his chart.

What amazes me the most is that this chart contains the data from 91,251 blood sugar readings.The month initials in the inner circle is really helpful to track the time period. Having said all the positive reviews that had me awestruck at this piece of art,I however can spot a drawback. While the choice of colour is what makes this chart art, the choice of having white is what creates a problem.It makes it difficult for the audience to connect the warm and cold colours and hence not easy to know the minimum and maximum  blood sugar per day. A smoother transition would be more effective.If i were to redo this it would be the only thing I would change about the visualisation.

A picture is worth a thousand words, indeed! However today we see data visualisation done as modern art. Many times the main purpose of the visualisation is lost, the chart is now beautiful but means nothing to the audience other than an appealing visual to your eye.The chart above shows us that we can use the tinniest dimension such as colour and create art while also telling our story. The chart should catch the audience’s attention but must have the content to keep them wanting to know the story.

Please do visit the site to get a better view of the images : http://databetic.com/?p=304

 

 

 

 

 

 

Paradox of Choice

–Pooja Kotian

Excellence is never an accident. It is always the result of high intention, sincere effort, and intelligent execution; it represents the wise choice of many alternatives – choice, not chance, determines your destiny.

-Aristotle

In today’s world, options are not something we lack. Years of research and ever growing technology have given us the luxury of doing complex things in just a few clicks. The same applies to data visualization, multiple tools have been built to help us create charts which were a tedious task in earlier times. But is more actually less? Lets take the below figure as an example to further discuss this:

 

 

The above chart uses the dual-axis combo feature that tableau provides.Tableau makes data visualisation a cake walk. It provides many features making it easy for users to build charts , graphs, etc. But making the right choice is the key.

What is interesting about the chart is how it clearly distinguishes its data. Reading into the chart will tell you what it’s trying to tell you. It answers the two of the main description question : What (furniture, office supplies, Technology) and When (year 2011 to 2014) and explains how much sales were or discounts given for those products over time. However, is this the right choice of presenting the data?

While the graph tells us quite a bit, some pieces still remain unknown leaving the viewer puzzled. It fails to answer us ‘Who'(which company) and ‘Where'(location). All we can say is that the company sales were x dollars in a particular year and discount percent in that year was y%. Also the time axis seems repetitive, simply using different colours for different products would make it simple and precise.

There are many more ways of representing the data in a more effective manner. But what to choose will always remain the question. When we have technology by our side we often tend over complicate stuff. In the above example, a simple method of having two graphs (one for sales and one for discount) side by side would do the trick.

In conclusion, I would like to say that there may be multiple solutions to a data visualisation but what we choose must serve the purpose in the most effective way. The data visualisation must be truthful and functional while being beautiful and insightful and most importantly be enlightening. The audience must be the key factor of our decision making process.