Visual Analytics for Excel

One of the biggest improvements in 9.2 is undoubtedly in the area of interactive charting. We’ve hugely extended the capabilities of Small Multiples through a new charting engine which brings rich interactive Visual Analytics to Excel (and web, and mobile…).

The ‘Small Multiple’ concept of many charts with a shared axis is very powerful, but in some cases users just need a single interactive chart and 9.2 caters for both scenarios. We have added zoom controls, sliders and a play axis to help users quickly focus in on and further explore specific areas of interest within the chart.

Zoom controls are available through the chart properties – right-click on anywhere in the chart and select Properties > Animation.

Let’s look at the Animation Zoom options in a bit more detail.

Initially you can select an area to zoom in on directly on the chart, however, you can also use the Zoom mode setting to select either a Slider or a Mini Chart.

Selecting a Slider adds a control to the bottom of the chart:

You can use the slider to narrow the display area, and then slide it across the range of data. This can be particularly useful in comparing relative trends across multiple charts.

Mini charts are another option available within Zoom mode – this shows a smaller version of the chart beneath the x axis and allows you to select an area to focus  in on, while retaining the smaller chart to retain the overall perspective.

File:SmallMultPreview small.gif

A Play control allows the user to see how values change over time.  To enable that open the Task Pane and add the required time hierarchy into the Animate chart by container.

Select the time periods you want to cycle through and click Play on the control beneath the x axis – it’s that easy! You can now step back through the periods one at a time, or replay the sequence as needed.

AnimateBy.gif

We hope you’ve found this blog useful and you’re inspired to visually explore your data with these new features!

As always, we value your feedback and any suggestions on how you would like to see our interactive charting extended further.

Bump Charts in XLCubed

So today’s blog is about adding Bump Charts in Excel using v8 XLCubed.

Initially a Bump Chart looks the same as a line chart – the difference is they plot the rank position rather than the actual value.

Let’s imagine that I sell a product in a marketplace with 10 other competitors. I may like to see how the rank position of my product and the competition changes over time to check if I’m gaining or losing market position. It’s a common scenario in pharma, where we have a good customer base.

You will usually want dates on the category axis so the trends are shown across time. The series then holds the items to be compared, in this case the products.

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Our example has been set up with Measures on Headers, Product Categories on Series and Date Calendar on Categories.  For more information on using Small Multiples in XLCubed please visit Small Multiple Charts.

The currently selected measure is Reseller Order Quantities (selected though the Measures slicer)

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for the eleven months prior to April 2008 (selected through the Date slicer)

 

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for a subset of products.

Looking at the bump chart you can see that I’ve selected Road Bikes and Mountain Bikes for easy comparison.  You can quickly see that the rank position for Road Bikes dropped quite dramatically from May 2007, picked up again in September before dropping again in November and rising in December through to February 2008.  The change for Mountain Bikes, on the other hand, was less dramatic, rising and falling slightly, steadying in February 2008 before dropping again the following month.

To create a bump chart just select Line – Bump as the Chart Type on your Small Multiple chart. The neat part is that all the rankings are worked out for you behind the scenes, without the need for lots of complex Excel gymnastics trying to work through the full result set month by month.

Some Excel BI myths debunked #2: Inflexible Charting

#2: Inflexible Charting

Continuing our discussion of common criticisms of Excel focused BI, let’s take a look at charting.

“Excel charts are static, inflexible and you need to start from scratch if you want to change them.“

The flipside is that everyone knows how to use them, and in reality many charts in business reporting are in effect static – the numbers being charted change, but the chart layout and number of elements being charted stays the same.

Of course there are cases when charts can vary considerably with the data, or perhaps you would like to be able to drill into more detail on the chart, or to quickly display multiple charts split by one variable. Excel charting can’t handle those scenarios, but XLCubed caters for it through Small Multiples.  The example below depicts river water quality in different regions of England. It could be built in native Excel, but would be a painful and time consuming process. With XLCubed it’s a drag and drop process in our small multiple designer.

waterqualitysmalt

If the number of regions being reported changes, the number of charts being plotted will automatically stay in sync, and there is a direct data connection rather than having to maintain Excel ranges etc.

Sometimes with charting small is beautiful. Perhaps we just want the key numbers with a Sparkline alongside, or a bullet graph or bar chart to display actual to target. Native Excel 2010 and 2013 can handle the Sparkline, but not the ability to then drill the report and have the Sparklines extend, and there is also the issue of needing to bring the data itself into Excel before charting it.

XLCubed Grids can contain dynamic in-cell charts which build the charts as part of the query, and as such are drillable and remove the need to maintain a data range in Excel as shown below.

DrillIncell

So XLCubed brings the type of dynamic charting being described to Excel, and provides a simple web and mobile deployment option.

 

 

Small Multiples on River Quality

The phrase small multiple was popularised by Edward Tufte, and has become a generic term for a visual display using the same chart or graphic to display different slices of a data set. Their close positioning and shared scale make comparisons very easy and shared trends or outliers can be quickly spotted. Various other terms are also used to describe this charting approach, or specific aspects of it, including Trellis Charts, Lattice Charts, Grid Charts and Panel Charts.

The most common use case for small multiples is separate line charts to compare trend across a large number of varying elements. Placing them all within one chart would cause either a ‘spaghetti chart’ , or lots of occlusion as shown in the comparison below. Here we use a standard Excel line chart, and an XLCubed small multiple to chart the same data. Separating the charts while keeping a consistent axis scale makes for a much easier comparison than in the single chart.

We took a slightly different approach when using small multiples to take a look at differences in river water quality across regions of the UK. Our source data was not absolute numeric values, but 14 years of results categorised into four bandings (bad, poor, fair and good). We wanted to provide a ‘one-pager’ which gave a feel for the trend within each region, but also access to the annual breakdown of the different water qualities.

In the end we settled on a Small Multiple display of 100% stacked columns as shown below.

A percentage base seemed a sensible way to approach the data, as different regions will have differing numbers of rivers and of samples taken. Using this approach we’re able to see a comparison of the relative water quality rather than dealing in absolutes.

The user selects a geographic area of the country to view the regional breakdown within the selected area. The water quality for a particular year can be analysed by locating the region, and the specific year to see the percentage breakdown for each of the four categories.

The colouring of the 4 categories was chosen to aid ‘at a glance’ recognition of the overall water quality by region, and also of the trend. Dark blue signifies bad quality water (opaque), and light blue signifies good quality (think ‘you can see right through it….’).

So to read the display overall, or for trend:
• Dark colour signifies water quality problems.
• Light colour signifies good quality water.
• Reading left to right, increasing colour saturation shows declining quality over time.
• Reading left to right, decreasing colour saturation shows improving quality over time.
• Any region can be zoomed in on to see a larger chart and understand the breakdown in more detail.

Fairly quickly, and from just this one display we can draw a number of conclusions as below:
• Across the region, as a broad brush summary, water quality has improved since 1992.
• Doncaster has shown strong and steady improvement.
• Kingston upon Hull has the worst quality overall in the region, and varies significantly year on year.
• If you’re off for a swim in a Yorkshire river, Richmondshire looks a good bet!

We’ve designed a pre-set view in this case to work for the data in question, but the small multiple concept is also very powerful when interactively exploring data. A picture can tell a thousand words as they say – take a look at our youtube videos on small multiples: Video1 Video2