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Maximizing The Impact of Data Visualisation

    Maximizing the impact of your data visualisation is a process that you should start with basics. Once you have a chart with default settings, you can simply add value to your visual. Each addition would light another bulb in your mind. Keep this process until you are satisfied and before the visual is getting too complex. It is not a good idea to keep pushing all the information. So, it is better to be focused on a piece of specific information.

    In this post, we will enhance the impact of a scatter plot (if you are not familiar with scatterplots, see our chart details). Actually, we start with a basic scatter plot and make it an infographic. I am inspired by a Reddit post devoted to Kobe Bryant. So, we use stats data from NBA (all-time leaders scored more than 5000 points), to highlight legendary Black Mamba.

    Step 1 – Draw Defaults

    First, we need to see what we have exactly. I make a jsfiddle sample using Highcarts and put all the data in the following format. And just below it, you can see the chart visual.

           “labelText”:    “Kobe Bryant”,
           “x”:    1346,
           “y”:    33643

    As you can see, this chart visualizes correlation and distribution. Besides, there are some outlier values specifically at the upper right side of the plot. So, let’s start to add value to our chart because there is nothing much to say about it.

    Step 2 – Point Out Your Data

    Now, we have a basic chart visual, and as a second step, you have to highlight the data you want to show at the beginning. At that point, it is Kobe Bryant. Remember, our first intent is to show how great he was. In order to do this, I deleted labelText content for other players. In this way, our chart can create the below visual.

    So, now we can understand that he was an outstanding player. Notice that, in this data set, outlier values are important. Usually,  we do not want to use outliers and omit them. Because they cause wrong decisions if you are in the data mining field. In this example,  outliers can be considered as NBA legends. Knowing your domain is essential for data visualization as we discussed in our post.

    Do you think is it enough to emphasize his quality? I do not think so, we can make it better.

    Step 3 – Highlight The Difference of Your Data (Compare with Regulars)

    Making a comparison is a good way to show differences. If you want to highlight something, easily you can compare it with something regular. Moreover, it is more effective if the comparison is between an individual and a group. In that way, you can say it is outstanding.

    We will use the information that scoring 25 points per NBA game is a success. In common sense, great players keep scoring above this average value. So, let’s put this value as a line on this chart and see if it is true.

    He is right on the point. What we see in this chart is Kobe Bryant scored 25 points per game during his career(1346 games). Compared to the first chart we made, this visual shows his greatness in a better way. Yet, we are not satisfied, right?

    Step 4 – Comparison Between Similars

    So far, we indicate stats of a single man and it is enough to see how successful he is. But, if you want to make a great impact, you have to race with legends. We know he was a star player, so we can compare him with other legendary players. Actually, this is not a comparison, our intent to show where he stands among other great players. Below infographic is a great data visualisation sample retrieved from here.

    In short, you can maximize the impact of data visualisation with easy additions. As you can understand, these additions come from domain knowledge. In this example, it is clear that without knowing basketball and the NBA, you cannot affect people enough.