QlikAdvice: Give context to your data

Hello everyone and welcome to QlikFreak. This will be our first post in a new category called QlikAdvice, where we’ll talk about dashboard design in a broader sense. Instead of focusing on technical stuff (we already cover that in our Just Qlik It posts), this section will contain functional tips that will help you present information in an efficient and elegant manner. These principles will be applied to Qlik apps (QlikView and Qlik Sense), but you’ll find out that most of them are useful in other domains as well, from web design to NPrinting Reports, or even PowerPoint presentations (or should I say Qlik Sense Stories?). Today’s topic is an old classic you should never forget: context.

QlikAdvice: Give context to the data to improve the decision-making process

When it comes to analyzing data, context is everything. If you present isolated figures, users will have a hard time trying to find out the real story behind them. As a Qlik developer, it’s easy to fall in the trap and start shooting random KPIs right away. For example, I could start my sales dashboard with something like this:


OK, we sold 3.56 M in 2016, that’s cool. However, what does this tell me? Was it a good or a bad year for our sales reps? Well, it all comes down to a simple question: “compared to what?”. In order to evaluate their performance, it would be better to have some kind of reference. For instance, the budget:

31_02 Continue reading

Just Qlik it: Electoral Gauge Chart

Hey everyone! Ready for another installment of our beloved section Just Qlik It? What? You forgot about that section? Well, for nearly two years so did I, but don’t worry, because it’s time to bring it back!

When I started this blog in 2014, I wrote a couple of posts under a category called Just Qlik It. These were supposed to be small recipes that everyone could just copy and paste in their apps. My idea was to contribute to your personal Qlik library (that messy file we all have where we store cool visualizations and useful chunks of code to reuse them later on). However, for some reason I forgot about that concept and kept going with other kind of posts… up until now!

Today’s snippet is a chart that you’ve seen a thousand times in the last few days: the electoral gauge chart. Even though it is really simple, its rectangular shape makes it very flexible when it comes to fitting into difficult spaces (you can make it wider or taller without impacting its aesthetics or functionality).


It is also one of those noble visualizations that go well with almost any other object, just like a white collared shirt in your wardrobe (or so says my girlfriend). By mixing it with some images and text objects, it can become a great way to display the most relevant metrics in your dashboards.

As usual, you can download all the related files here or here, and the comment section is ready for your enquiries. On to the recipe!

Electoral Gauge Chart

1.- Create a new gauge chart. Don’t include any dimension and use a dummy expression like this: Continue reading


When QlikView meets the blockbusters

The associative model is definitely one of the best features in the Qlik platform. It is simple, elegant and intuitive but, at the same time, it is a very powerful tool that helps us unveil the stories behind our data.

When I attended my very first QlikView training, I remember Karl Pover used a generic demo called Movies Database to explain the navigation schema. Even though the app wasn’t exactly breathtaking, it was a great way to understand that every selection turns green, the associated elements remain white, and the unrelated items become gray.


From that day on, every time I had to explain the associative model, I relied on the always-available, rarely-updated but still-pretty-good, Movies Database demo that is installed with any version of QlikView. Up until now…

A couple of weeks ago, I was working with Daniela Lucero, one of the youngest consultants in our team, when I realized that my examples about The Matrix, Fight Club and Titanic were not making much sense to her because… well, she was 3 years old when those movies came out… So, we decided to take advantage of the Qlik REST Connector and refresh this old classic.

Daniela: Hello everyone, I’m Daniela and I’ll be using the orange font throughout this post! We took this opportunity to experiment with some atypical features in QlikView. To start with, we chose a dark background (which is usually a big gamble). We also decided to hide the tab row and use a custom-built menu instead, explore different wireframes, and use eye-catching visualizations such as infographics, image-based tables and even some extensions.


As usual, we’ll walk you through the most relevant features in the app (download here or here) while sharing technical recipes and useful tips regarding data visualization. We had a lot of fun creating this app, so we hope you like it! Continue reading


When QlikView meets Pokémon

Some people build QlikView dashboards to review the financial outcomes of their companies. Others use it to monitor their everyday operations in plants and warehouses. There are individuals who even create applications to analyze fun stuff like the Olympic Games or TV Series. In my opinion, QlikView should be exclusively used to answer humanity’s greatest inquiries and analyze relevant topics that affect our lives and our future; important things such as Pokémon 😛

When I was a kid, I used to play Pokémon games all the time. Blue, Yellow, Silver, Sapphire, Stadium, Snap, Pinball, Trading Card Game, you name it. And well, since everyone’s going a little crazy about Pokémon Go these days, I decided to create a QlikView document based on the original 151 Pokémon in the first generation (speaking about mixing good stuff).


Today’s post isn’t exactly a tutorial. I’ll just share the QVW I created and highlight some interesting features I think you can use to improve your own applications. Even if you’re not exactly the biggest Pokémon fan, be sure to check it out. I’m sure you’ll find something that strikes your attention!  Continue reading


About Slopegraphs and Bumps Charts

Hey everyone! Today’s post answers a request from one of our readers and features two not-so-common visualizations that you can use to make time comparisons: slopegraphs and bumps charts. Although these objects have a very simple nature, they’re also quite elegant and intuitive, so there’s a good chance that they will help you get some unexpected insights in your analyses.

We’ll also discuss one or two tips that can help you improve their readability and make them more meaningful by adding extra context to the data. As requested, the football dataset that we’ve been using lately will give life to our examples! 😉


Slopegraphs are a great way to compare two points in time. However, instead of describing the ups and downs along the way like traditional line charts, they focus on how the journey started and how it ended. This visualization is the perfect candidate for “then and now” analyses (much like that TV show that presents celebrities and how they looked 30 years ago). * Note to self: stop making that kind of references, people will laugh at you. You should recommend Cole’s blog instead. *

When working with these charts, our brain can easily spot patterns and recognize the distinctive slopes of certain elements in terms of direction or magnitude. It is also an intuitive way of showing how the rankings changed from a point in time to another, as the lines will intersect each other and end up in a different order.


In this example, we can visualize the performance of all the teams in the Premier League over the last two seasons. Each squad is portrayed by a line and its position and slope depend on the rank achieved in both the 2015 and 2016 tournaments. In order to improve the chart’s readability, you can highlight the selected team by adding some color and changing its line’s width.


* Fun Fact: This chart is so cool that it made it to the cover of one of my favorite books about data visualization, The Functional Art by Alberto Cairo. Continue reading

Tile Grid Maps

Have you ever had one of those “of course I can do that in QlikView” moments? As a Qlik developer it’s not uncommon to find yourself struggling to create a chart or pull off a complex script that nobody asked for, but for some strange reason, you decided to tas a personal challenge. Well, I just had one of those moments.


I couple of days ago I was wandering around my usual blogs when I found a post by Alberto Cairo  where he talked about an interactive visualization created by the FiveThirtyEight team regarding the political preferences in the United States. [By the way, if you don’t follow those blogs you are missing a lot of great stuff!]


Besides being a stunning way to present What-If scenarios, I liked the usage of the Tile Grid Maps, which have become a pretty popular visualization lately. What is a Tile Grid Map, you ask? Well, think of it as a mix between a choropleth map and a table heat map. If you want to see more examples, you can visit Bloomberg, The Washington Post or The New York Times.25_30

It is true that most of the times we strive for more accurate charts but ironically, this technique’s boon is that it disregards the actual size of the regions and translates them to equal-sized shapes (in this case, squares) so it becomes easier to see even the smallest areas. Therefore, this solution is excellent when the geographic size is not the most important thing, but maintaining a loose spatial distribution is.

So anyways… In today’s tutorial we’ll build something like this:

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QlikFreak Football

What happens when you mix QlikView with the best football leagues in the globe? In today’s post, I’ll share a little app I created to analyze data about my favorite soccer tournaments like the English Premier League, the German Bundesliga or the Serie A from Italy. So let’s take a break of business oriented dashboards and spend some time reviewing the wins, losses and goals of the last few years.

If you’re a football fan, you’ll stay for the discoveries. If you’re a QlikView enthusiast, you’ll stay for the tips regarding scripting and visualization. If you’re not neither of those, you’ll visit our Random page in order to see funny videos like a pug playing ‘Enter Sandman’ from Metallica in the drums.

As usual, you can download all the related files here. Ready? Let’s get started!


The first challenge in this endeavor was to find a good data source. However, a static XLS file wouldn’t do the trick. Since we’re in the middle of the season and there are matches every week, we need a way to update our file constantly. Even though it’s not the most common way to extract tables, don’t forget that QlikView can retrieve records from external websites (Data from Files > Web Files).


After browsing for a while, I found a great sports portal called SkySports where we could get all the information we needed. In order to load it to our model (and since I didn’t want to copy and paste the same code dozens of times), I relied on one of our oldest friends: the FOR… NEXT loop. The structure is not very complex, so I’ll let the code speak for itself: Continue reading