Excel heat maps made easy!

With the recent release of version 8 we’re going to blog about a number of the new features, starting with how to create a heat map in Excel.

Here’s a fairly large table showing sales for thirty six products across twenty six US states:


There’s a lot of data here but it’s not giving us any helpful information as the table is too large to see any pattern or comparison.

A heat map could be a useful way to give a quick visual picture of the spread of the sales volume. Let’s add a simple heat map, new in version 8 of XLCubed.

Select the data area in the table, and then from the XLCubed ribbon select the InCell-Chart group, and heat map:



As we have already selected the data area to be charted this prompt is already showing the correct cell locations.

Choose the formula destination (where the formula controlling the chart will be located), and the Chart destination (where the top left cell in the chart area will be located).

We can now define the look of the heat map in the Chart Format dialog:




We have set the low and high colours to define a blue colour gradient.

Outlying values could potentially skew the chart so you have the option to exclude these by setting minimum and maximum values.  Select the icon to use, squares in our case, and the number of steps or bands to split the range of values into.

We have pre-arranged the Excel cell sizes to be squares, and this is the resulting heat map:



You can now quickly assimilate the spread of values in a glance, and note the higher sales volumes in Maine, Michigan and Missouri for Road, Touring and Mountain Bikes.

To alter the formatting of the chart simply double click on any one of the squares in the heat map, or on the chart formula to bring up the formatting dialog.

If you are not already a user of XLCubed you can get started with an evaluation of XLCubed by going to our registration page.

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.


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.


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



Bandlines in XLCubed

In early January this year Stephen Few introduced the concept of Bandlines. He identified a useful extension to Sparklines, making use of shaded or coloured horizontal bands to provide more information on the context of the trend line itself. See Stephen’s article on Bandlines and the thinking behind them for a detail description.

The Sparklines are ideal for showing individual trends in a small amount of screen real estate, and we use them extensively in dashboards, typically in a ‘visual table’. By definition Sparklines are small, and to make the trend easily readable, they are typically scaled individually so that each Sparkline uses the whole vertical axis. This means they do not give any impression of the scale of the numbers involved across different rows. It’s possible to use a common scale, and while sometimes that works more often it means many of the rows with smaller values are excessively flattened.

Bandlines address this by introducing horizontal shaded areas depicting the lower, middle and upper quartiles and the median represented by a line. The user can determine the context of the bands. The two most common examples would be plotting recent trend in the context of a longer period, or plotting individual rows in the context of the overall set of data being displayed.

We think Bandlines add real value, so hats off once again to Stephen, and we’re pleased to say that Bandlines are now available in the current version of XLCubed (see here for more detail).

The screenshots below show two examples, displayed in two colour schemes.



The charts depict historic margin by store. The ‘Banding across all stores’ charts show the 30-day trend for the individual store, set in the quartile context of the data for all 11 stores in the table. We can see that for the Gilroy store in row 1, while the margin has varied, it remained in the top quartile when set against all stores for almost the whole period.

The ‘Banding by store, 90 days’ charts show the individual 30 day trend, set in the context of the previous 90 days for the individual store. This helps provide much more historical context, but the line itself still focuses on the more recent trend. Stockton is probably most noteworty here as across the 30 day period it has dropped from the top quartile into the 1st quartile across the whole 90 day period.

We’d love to hear your thoughts (and also which colour scheme works best!), we will also be adding Sparkstrips in the near future so watch this space.





Something on the Horizon

We had an interesting scenario while helping a customer extend an existing Excel dashboard.

We had recently performed some work to solve some performance and design issues they had with their existing Analysis Services cubes. They now had more of their underlying data available and the ability to query longer periods without the performance hit (a year’s worth of data vs 28-days).

They wanted to make the most of this by charting changes in daily sales data over the previous 12 months, broken down by their four main business groups. Ideally the chart would become part of the existing Management Report, the difficulty was the lack of report real estate to add the extra information. This is something we have all come across previously and of course typically solved by using In-Cell charts.

Plotting the data on an Excel chart in the space available would give us this:



Converting to Sparklines gave us a slightly better view, but given the number of data items being plotted still not ideal.



Luckily our customer had recently upgraded to V6.1 of XLCubed so we were able to use one of our newest incell chart types: SparkHorizons. There is a good explanation of Horizon charts as part of the research paper: Sizing the Horizon: The Effects of Chart Size and Layering on the Graphical Perception of Time Series Visualizations and Stephen Few has covered them previously.

Essentially a line chart is split into colored bands – degrees of blue for positive numbers and degrees of red for negative numbers. In XLCubed this is 3 bands of each colour. The separation of the vertical scale means that horizon charts can be a lot more effective than standard sparklines where the scale of the numbers vary significantly, but you still want to retain a common scale view.

In this case plotting the same data as horizon charts makes things a lot clearer:

It now becomes quite clear when sales a trending up vs down. It’s also possible to flip the negative values so they appear on the same direction as the positive values:


We are always looking at ways of developing and extending XLCubed, SparkHorizons were added because they looked like they had the potential to be useful where the data suited them, so it was pleasing to be able to use them in a real-world situation.

It’s also worth mentioning that although, in this case the data came from Analysis Services Cubes, because they are available as Excel formula they can be used to plot any Excel data, here’s an example of the formula:


This will plot the data from Sheet1!$V$2:$V$262 as a SparkHorizon graph in Sheet1!E10.


Data Visualization – a real world example

In the following example we work through a real world example of a data visualization. We’ve chosen an example that involves Operations data – this is fairly non-domain specific so hopefully it can demonstrate some important points. The first, and most important point is that you have to define your audience.

We receive many questions about “what is the best chart for this situation” or “what colour should I use for emphasis”. These questions are usually attacking the problem from the wrong angle. The one question you need to ask before anything else is “who is this visualization going to be seen by and how?” Is it in a boardroom on a printed sheet or across a trading floor on a plasma screen. Are the consumers domain experts?

This example features data about an investment bank’s operations processing, the audience being the clients of the Operations department.

Starting Point

Initially the project started out as simply trying to record what operational problems were encountered on a daily basis across different product lines. A reporting system was built and various generic reports produced:


Unfortunately the reports either didn’t contain data at a granular enough level or it was difficult for the product managers to see where the issues were occurring and what the trends were. In reality the report showed what the major problems had been – unfortunately this was already known, as when something major goes wrong you remember getting shouted at!

What was requested

The client wanted a report that showed where the problems were occurring across business lines (rather than operational units) and how they were doing historically in a single page that could be included in a weekly MIS pack (they currently had four pages per product line (8) so a total of 32 pages. As a first pass they simply wanted an Excel worksheet they could update manually:


We felt this solution lacked clarity and it was very difficult to spot trends across products.

What we proposed

We designed a solution using MicroCharts to allow small multiples of charts to show a variety of views:


This solution allowed the user to view the data simply as a cumulative set of data by Product (top line) or by Root Cause (vertically) and then look deeper into historical trends in the centre of the chart. For example, its fairly easy to see spikes in the Root Cause data historically and see that the overall trend has improved over time. By ranking the Products and Root Causes you immediately give some sense of scale to the data. For example you can see that there are many more Application failures than any other type of problem, but the majority of root causes are otherwise fairly evenly distributed.

One other point worth noting was that the original colour scheme was much more muted, but the client got very upset that it looked like a competitor’s corporate colour and wanted it to be “louder”.

What was the user reaction…

Ecstatic, 1 page replaced 34 and they could see at a glance how the entire (large) organisation was working but also quickly find out detail for a particular area and identify trends.