Category Archives: Sparklines

The Missing Link in Excel BI

When viewing a high level summary report or dashboard, users often want to delve into more detail on a specific area. In some cases that may be a drill down, in many others it may be to a different view of the data or to an entirely different report. In the XLCubed example below, users can link from any one of the summary KPIs to a detail report showing product level detail for the selected KPI.

This is a fairly common requirement in reporting. In a standard Excel context, it would be easy to add a hyperlink formula to jump across to another sheet, but that’s just part of what’s needed. In this example we need to link in the context of the selected KPI, otherwise we would need a separate sheet for product detail on each KPI, far from ideal, especially in row-dynamic reports.

This type of limitation is one reason why you’ll often see workbooks with huge numbers of worksheets, which become unwieldy and horrible to maintain.

We need hyperlink functionality but also an ability to pass parameters (and of course a way for the pivot table to accept the incoming parameter…).

XLCubed makes it straightforward for non-technical users to build this type of contextual linking into reports through the XL3Link() formula.  XL3Link has arguments which determine what is displayed in the cell, where it hyperlinks to, and what cell(s) parameters are passed from and to.

Unlike Pivot tables, XLCubed Grids and formulae can reference cell content as a filter, so the data on the ‘link to’ worksheet can update as soon as a new value is passed into the driving cell, retrieving the relevant data from whichever data source is involved.

The beauty of the approach is its simplicity. It’s something which most users can get to grips with quickly, and opens up huge flexibility in joined up reporting.

Last but not least, web and mobile deployment takes a matter of seconds. The report is published to XLCubed Web and from there browser and mobile app based users have access to the same report with the same chain of thought links. The links can be to different content in the same report, to a separate report, or a url to another application or website.

 

(This piece revisits content from our blog  from several years back the missing link part 1   . The business requirement it addresses is now even more common, and still one not handled in native Excel.)

Excel BI myths debunked – #6: No report sharing & distribution

Here we continue our theme on the myths which get propagated about Excel based BI. The next argument is that Excel BI cannot handle widespread report sharing and distribution. Base case we actually agree with this one, and that’s why we invested in developing XLCubed Web Edition specifically to address it.

Understandably, sharing an Excel workbook around hundreds or thousands of users is not something which many companies will consider. A web based distribution approach is much lighter and easier to manage. The drawback is that most web based report design environments lack the flexibility and latent user skill base of Excel. XLCubed provides a simple way to push data-connected reports developed in Excel to a portal based environment, where report consumers don’t require any software installed locally, other than a browser. The reports can also be accessed interactively through our native mobile apps for Apple, Android and Windows phone 8.

XLCubed Web is self-sufficient and does not require SharePoint. For customers with SharePoint and keen to retain it as a centralised environment – no problem, XLCubed Web can integrate so tightly within SharePoint the end users won’t even know it’s there.

Excel based users can become web and mobile report designers in minutes. XLCubed uses Excel as a key part of the BI solution rather than as the entire BI solution, and it’s that which allows us to address the sharing problem, along with the other myths we have identified in this blog series.

 from any version of Excel:

ipxl

…to web…

ipwb

…to mobile.

ipip

Some Excel BI myths debunked: #3 – limited dashboards

#3: Limited and difficult to Maintain Dashboards

Third on our list of common criticisms of Excel focused BI, is the limitations of Excel Dashboards.

“Excel dashboards are ugly, limited, and inflexible…”

It’s possible to build a truly awful dashboard in pretty much any dashboard tool. No tool is magic, ignoring the Doctor’s Sonic Screwdriver of course, and if you make bad design choices when building a dashboard the end result can be a mess. Similarly you can build a pretty decent dashboard in most tools. So even in base Excel with no additional software you can build a dashboard which looks good, and many people do.

In native Excel there are undoubtedly some limitations around the available chart types, and the handling of dynamic charting. However you do have the benefit of very fine grain control over the layout and positioning of tables and charts. The camera object also lets you break out of the fixed column width which is sometimes seen as a limitation.

XLCubed extends the core charts available in Excel with a rich library of in-cell charts, small multiple/trellis charting, mapping and TreeMaps. It means you can deliver more in Excel visually, rather than have to leave the environment totally. Dashboards mean different things to different people, for some a dashboard can be a table with a chart, but most contain significantly more than that. The example below uses a mixture of native Excel charting and XLCubed in-cell charts.

FinanceSShot

It’s based around a sample personal finance data set, and brings a lot of information together in hopefully a visually appealing and effective way.  If you want to build a highly formatted and relatively densely populated dashboard like this, it’s going to take more than a few minutes in any tool, no matter what the marketing says. In reality you’ll most likely struggle to get the exact layout in a widget based dashboard tool as you lose some of the fine-grain control over table and chart sizing which you have in Excel.

Dashboards can be fundamentally simpler than the first example, but require more specialised chart types like the example below. In this case it’s a dashboard built in XLCubed Excel Edition and published to the Web, looking at fuel pricing for a downstream oil company (fictitious data). It’s a ranked table of data for a selected county in Florida, and is then using an extended boxplot to display the price distribution in the market, and a map to show the Revenues and Volumes geographically.

RampMap

One major issue with Excel dashboards can be the maintenance. If it’s an Excel-only dashboard, bringing in the new data, and checking all the links can be a time consuming process. In an XLCubed environment the cube is updated behind the scenes and the next time you open the report you’ll get the updated data, the ongoing burden of maintenance is largely removed.

So in summary, Excel when well used, is a very good dashboard tool, and XLCubed extends the capability further still in terms of available chart types, flexibility and maintenance.

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.

 

 

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.

 

Bandlines3

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.

 

 

 

 

Breakout and Propagate – Oldies but Goodies

We’ve been on-site with various customers in the last few months, it’s always good to see how the product is being used, and we value customer feedback, which often feeds into our development cycle.

With two long-term customers we found that they weren’t aware of two very useful pieces of functionality which have been in the product for many years. In case there are others in the same boat…..

1) Breakout

Breakout is available on the right-click menu of any XLCubed report, grid or formula. It’s a way to understand how the number is split into elements of another hierarchy. On a right-click, you can switch to the breakdown of the number by any other hierarchy in the cube. It’s particularly useful where a reported number seems too high or too low, and needs further investigation. In the example below, we’d like to understand how the June 2004 number for the US is made up in terms of Products. We’ve chosen breakout on the right-click menu, specified the Product Model Categories hierarchy and the level to run the breakout at. The breakout result is shown on the bottom right, and we can quickly see that in June Touring bikes were contributing almost 33% of the US revenues. In this example we’ve also included a sparkline to give a feel for trend over the last 2 years (previous 23 months, plus the current month).

The breakout result is still linked to the report selection criteria, so can be used as a dynamic part of the report ongoing.

 

2) Propagate across Sheets

Propagate across sheets is a way to quickly replicate a report onto additional sheets, where just one variable is changed. Typical-use cases for this are entity-based reporting, where for example there is a standard P&L template across the business, and the user wants to quickly generate a P&L for each legal entity. You can build the report as normal, and then when done, right-click on the selected member for the hierarchy you want to propagate, and choose the elements which you want to create additional sheets for. On the new sheets, the formatting and print layout are identical, with the only change being the selected member on the propagated hierarchy.

This example shows the income statement about to be propagated for the four selected departments.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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:

=XL3SparkHorizon(Sheet1!$V$2:$V$262,Sheet1!E10)

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:

DVBlog1

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:

DVBlog2

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:

DVBlog3

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.

Graphical Table – Abortion Data 1980 – 2003

Here the history of the Abortion data set

1) Jorge used some U.S. Census Bureau data (original Excel file) to visualize the abortion ratio as a small multiples chart,

2) In Small Multiples – Abortion Data 1980-2003, I have written about Jorge’s panel chart. I like the panel (small multiples) chart, but felt that it can be improved.

3) Jon Peltier posted on Re: Abortion Ratios 1980-2003 and Interactive Multiple Line Chart some nice Excel techniques to create interactive charts to analyze the Abortion data set.

Here is a different view on the data set as a Graphical Table using MicroCharts. The Graphical Table is a hybrid between the panel (small multiples) chart and a plain table of numbers. It nicely integrates the numbers with small charts, so-called sparklines, in a table. The sparklines show you the trend of the abortions over the years and the in-cell bar charts give you a feel for the distribution over the age groups:

 

Graphical Tables – An Alternative to Treemaps

Sean blogged the other day about using a treemap to visualize the drivers of the Australian Inflation. He got inspired to create a treemap by an NYT article that used an interactive version of the following treemap:

NYTTreemap

This chart looks nice on the first view. It makes nice use of muted colors, the shapes look well balanced and certainly the graphic designer did a good job. However, from a data visualization perspective this chart has a couple of flaws.

Ben Shneiderman designed Treemaps to visualize deep directory tree structures.

Ben explained treemaps in an article as:

“Among the growing family of visual analytic tools, treemap are flourishing in organizations that require daily monitoring of complex activities with thousands of products, projects, or salespeople. Tabular reports, bar charts, line graphs, and scattergrams are important tools, but for complex activities where there are numerous sales regions, manufacturing plants, or product lines the hierarchical structures provided by treemaps can be helpful. While tabular displays and spreadsheets can show 30-60 rows at a time on typical displays, the colorful presentations in treemaps can accommodate hundreds or thousands of items in a meaningfully organized display that allows patterns and exceptions to be spotted in seconds.[…] Treemaps are a space-filling approach to showing hierarchies in which the rectangular screen space is divided into regions, and then each region is divided again for each level in the hierarchy.”

The first problem the NYT chart has is that it does not visualize the hierarchy as rectangular areas. The inflation drivers are visualized as asymmetric round shapes. It is difficult to compare the relative size of rectangular shapes but it gets almost impossible for asymmetric shapes. Also does this treemap lack labels for the smaller inflation drivers.

Sean published in his blog post a treemap which does not have the problems mentioned above:image

 

 

 

 

 

 

 

 

 

 

 

Ben designed treemaps to visualize thousands of regions, products, etc ; but the Inflation chart only comprises 20 Inflation Drivers grouped into 7 categories. A simple sorted table would do a better job communicating the numbers as Kaiser Fung from Junk Junks wrote in his post.

Inspired by this post and my comment Sean came up with this graphical sparkline table designed with Excel and MicroCharts.

image

This is already quite an improvement on the treemap, as we can see increasing and decreasing inflation trends and sparklines rather than traffic light colors as in the tree map version. Also it is much easier to read for non expert users.

Some minor things we can improve in Sean’s chart are:

  • We can sort the inflation drivers by Weight, to have the most important ones at the top
  • Changing the area to the sparkline puts emphasis on the trend rather than the absolute value of the values (as the area chart does)
  • Inline deviation charts allow us to visualize the MoM and YoY % changes