One of the biggest problems about being a QlikView Developer is that every time you see an interesting visualization, you immediately start wondering how you can use it in your own dashboards. For instance, a couple of days ago, I was wandering around in Twitter when I saw a message which, apparently, had something to do with the quality of the air in Berlin (sorry, but my German is not very good).
As you can see, the structure of this chart is not overly complex: classic bar chart using a time dimension and a standard expression. However, a simple visual cue composed by a reference line and a different color makes it easier to identify where (of in this case, when) an element surpasses the threshold. In my opinion, it is an elegant and useful way to present data.
Since I didn’t have access to this particular data set, I decided to work with a sales QVD from one of the projects I’m currently working on and, after spending a few minutes in QlikView, this was the result:
I don’t know if this kind of representation has an official name, but I called it “Threshold Bar Chart” <<Patent pending>>. I know… I’m awful at naming things so, if you have a better title, be sure to share it in the comments section! As usual, you can download the QVW with the final chart here. The recipe is simple:
The Qlik platform is all about analyzing data and making discoveries. However, in order to get valuable insights for your organization, you can’t just go around loading any data source and creating random charts. On the contrary, a good QlikView developer will always strive to use the most appropriate objects for each type of analysis.
Even though classic visualizations such as bar, line or pie charts are essential components of most applications, complex inquiries usually require more sophisticated tools to gain full understanding of the situation and make the best decisions possible. In this regard, one of my favorite visualizations is the scatter plot (Well, scatter plots and histograms, but we’ve already talked about those).
Although not very common, when used adequately, these charts can be real eye-openers. Sadly, its usage is still covered in a veil of mystery for the majority of the business users who –for a strange reason– seem to fear its power. But anyways, back to the story…
This chart stands out due its ability to elegantly handle great amounts of data. Though its simplest form only combines one dimension and two expressions plotted along the x and y axes, you can enrich them in several ways. Let’s start with an easy example:
Each bubble in this chart represents one of On Nom Nom Nom’s food trucks. As the y-axis embodies the sales amount, the higher the bubble is, the “stronger” the food truck. On the other end, the x-axis represents the Margin %. Therefore, a bubble far in the right could be categorized as “more intelligent” due to its higher profitability. In this case, the best scenario for the company would be to have most of the bubbles in the upper right corner, meaning that all the food trucks sell a lot but also have good margins.
To make this visualization clearer, we can add reference lines and define static of dynamic thresholds with variables and traditional expressions: Continue reading