This post is a just a bit of fun using Qlik and contains two videos of apps/movies made I developed in Qlikview and used in my presentation at Edinburgh Qlik Dev Group earlier this week. The videos are of “moving” data.
I had the honour of meeting up with Brian Booden from Neolytics recently and talk all things Qlik. We didn’t have time to fit everything into one catch up so Brian kindly agreed to an interview about his 2nd year as a Qlik Luminary, his new blog and the excellent Qlik Dev group (see you at the next meet up in Edinburgh soon!). Brian is a pioneer, having contributed sleek Qlik Sense extensions, and an enthusiastic promoter of the benefits of business intelligence.
This blog post started as one thing and ended up another, but I’m actually glad it did. I set out with the intention of using a range of Premier League data to examine Qlik Sense Chart chart types and best practices. However, after coming up with a list of around 20 different measures off the back of just the first 4 columns I decided it would be interesting to see what Qlik Sense can do with even the smallest of data sets. With just 4 columns; Club Name, Season, Position and Points, there’s enough to take you wherever your mind dares and Qlik Sense provides the tools to put your thoughts onto screen. The tools are not just the visualisations but the ability to rearrange and aggregate data within your front end calculations; in the forms of set analysis, aggr() and firstsortedvalue() functions.
It’s a huge honour to be invited onto Qlik’s Luminary programme for 2017. I can’t wait to get going. Congratulations to everyone who made the cut and I hope to see you at a Qlik event soon.
For Qlik’s post and for a full list of 2017’s luminaries Continue reading “Qlik Luminary Award”
Since it was announced in the 2015 Conservative party manifesto the TEF (a measurement of excellence that will allow high performing colleges and universities to increase tuition fees) has caused quite a stir in the world of higher education. There has been a lot of opposition, non more so than the National Union of Students (NUS) which has discussed encouraging a boycott. Despite this, it is set to go ahead and institutions are preparing by trying to predict their scores. Beyond these first results they will have to decide how to improve their scores and which metric to target first. However, which metric should they start with and are all the metrics related so that a positive impact on one will have a positive impact on the others? Using Scatter Charts in QlikView we take a look using UNISTATS data.
I’ve finished work for Christmas so I’ll be taking a short break from the blog to eat, drink and be merry with my friends and family. Driving back home from work earlier today Chris Rea’s dulcet tone came floating out of the car speakers with his aptly named song ‘Driving Home for Christmas.’ Continue reading “Driving Home for Christmas”
I recently wrote an article for Inside Data magazine on choosing the right business intelligence tool and it’s been published today.
As a nod to the release of Star Wars Rogue One (and and excuse to try out a dark theme dashboard) I’ve put together a Qlik dashboard with some of the freely available Star Wars stats and data from around the internet.
You can do all sorts with charts, and people do. They should quickly convey accurate and insightful trends but they can be abused – twisted or exaggerated to tell whatever story the developer wants. They can also be mishandled by a well-meaning developer who has accidentally ticked the wrong option.
So where does Fox News fit into this? Continue reading “The Fox News Technique – How NOT to Report the Truth in Qlik”
Often it’s easier to get the point across when it’s quicker to get to the point. That’s why we use charts. As a visualisation they should draw us to the key trends and figures without having to trawl through rows and columns of data. However, aren’t they a bit boring? Can’t we get the point across in a more immediate and engaging way? So let’s strip away those axes and titles then see what we can do with a data set on pet ownership in the UK which I’ve taken from http://www.pfma.org.uk/regional-pet-population-2016 and stripped down to just the northern regions. Here’s the raw data: