New Qlikview Timeline Spread Analysis (aka ‘Ribbon Chart’)

What better way to celebrate the arrival of the Qonnections 2012 event inMiami(where hopefully lots of new ‘Front End’ possibilities are going to be unveiled) than with a new Qlikview chart.

This chart is based on a similar principal to my recent replication of the Windows Defragmenter chart that can be found here: https://qvdesign.wordpress.com/2012/03/29/qlikview-contiguity-chart-aka-that-chart-from-the-windows-disk-defragmenter/ However in this instance I’ve taken things a little further and hopefully created something potentially very useful. I won’t go too much into the technical ‘how to’ as most is covered in the Contiguity Chart post and as always an example .qvw is available for download.

What are we showing here? In much the same way as the Contiguity Chart shows days where we’ve had sales and days where we haven’t the Ribbon Chart adds another level and can show us for example days where a target has been reached, days where sales have been within a percentage range and much more depending on requirements thus reaching areas of the data that other chart types may fail to.

In the example below (fig 2) there are 2 slider objects that allow us to highlight in Red those daily sales that fall within the bottom X% of the maximum daily sales value from within the range and in Green those in the top X%. For example if the highest sales value we’ve had in a day is £1,000 then those days in Red have sales of less than £250 (Bottom 25%) and those in Green greater than £750 (top 25%). ‘So what’s the use in that? – why not just use a Line Chart to show sales over time?’ I hear you cry. Whilst I’m in no way advocating that this chart is better than a Line Chart at showing sales (or similar) over time I do feel it can show things that a Line or Bar chart never could. For instance; let’s say you’re looking at sales of a particular product set (as in the example), firstly we’ll look at a Line Chart representation of Sales over time (aggregated to Month) and despite some peaks and troughs overall sales are relatively stable; there’s no long term trend up or down; all is well – nice bonus to the Product Manager.

Not according to the Ribbon Chart.

By choosing to highlight the top and bottom 25% sales days we can see that 2010 contains nearly all the days where sales were in the bottom percentile range and 2009 has many more in the top percentile, the reason for the apparently level monthly sales is that 2009 had more days where no sales whatsoever occurred (for which there may or may not be a valid business reason).

We can also look at the same data via a Ribbon Chart showing threshold adherence:

Here we can analyze the number of days and their regularity where a threshold has or hasn’t been reached. Again 2009 looks better; where there have been sales they’ve almost always been above the upper threshold whereas in 2010 we have more regular sales but many don’t even make the lower threshold.

This is just a demo dataset so there are no reasons behind the 2009 / 2010 data change but if in the real world this would be ringing alarm bells.

Of course there are many alterations we could make to this chart to fit specific requirements; we could count all days with no sales as not meeting the lower threshold, use a single threshold, calculate the % based on Average Sales etc etc; it really depends on the data and the user requirements. The Ribbon could be used at the base of a Bar or Line chart to show another metric such as if there were any complaints on the day. We could also add many more metrics to the dashboard; counts, %, sales above, sales below etc but this is only intended as a Proof of Concept so I’ll leave it to others to build upon the foundation.

You’ll notice from the examples that I’ve added a bar chart above the Ribbon, this is to show roughly how much each days sales are; the split between 2009 and 2010 could be even in days where the threshold has been reached but it’s good to know whether 2009’s were way above vs 2010 just scrapping past. However in the example below it’s also showing another key benefit of the Ribbon Chart; the Ribbon gives equal weight to 0 sales as it does to sales of say 1500. In both charts we’re highlighting above and below 250; in the Bar it appears as though we’ve failed to hit the threshold on more days in 2010 than 2009 but because there are days with no sales we can’t show all the days failing to reach the threshold because how do you highlight nothing? The true picture can be seen below in the Ribbon Chart which is set to mark all days below the threshold AND those with no sales as being red; now 2009 looks to be the worse of the 2 years.

Personally I’d always choose this method – sales of zero are just as ‘under’ a target as sales of 1 but as with all visualizations; it depends on the situation; there may be a perfectly good reason for zero sales; it’s a Sunday and you weren’t trading so why highlight in red? – etc.

Of course the chart has to be used where appropriate, it certainly isn’t suitable everywhere but it should provide a useful addition to the Qlikview toolbox.

As stated earlier this is only a Proof of Concept so try not to judge it by the data examples above but try to think how it would fit into your datasets; pretty much any data with dates should be able to make use of this chart in some way; show day’s where (not)enough was produced, calls handled by handler X, sales made in region Y; the possibilities are almost endless.

You can download the .qvw here: https://docs.google.com/open?id=0BxloTMUod74tQ3BYMjdpY01UUGc

As always I hope you find it useful.

All the best,

Matt

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Comments
2 Responses to “New Qlikview Timeline Spread Analysis (aka ‘Ribbon Chart’)”
  1. Geister says:

    Interesting! Although I do think this type of chart really needs a user that has good knowledge of the data and how to gain an advantage of it it’s a really nice concept as all of your examples. Will try and understand this a bit better :). Thanks!

    • qvdesign says:

      Geister,

      I totally agree – this isn’t a chart for a ‘casual dashboard’. It’s one where the user as you say has a good knowledge of the data and really knows what’s being shown and how it relates to the business/situation.

      All the best,

      Matt

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