2017 has been a disastrous year, politically and environmentally, but something I will always cherish about the year will be my trip to Qlik in Lund, where the company was founded back in 1993.
We love our recipes in Qlik land, none more so than master chef Rob Wunderlich (qlikviewcookbook), and they’re great but they do make me hungry. We use recipes as a way to describe processes but this time I thought it might be fun to use one as a way to describe an overall approach to building a dashboard in Qlik. These best practice tips are the kind of things I wish I’d known about when I first started out with Qlik and that even now are all too easily taken for granted. The link to open/download is below.
Link: A Recipe for a Dashboard
This post is a preview/taster for a forthcoming 2 part post based on my presentation at Edinburgh Qlik Dev Group earlier this week:
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.