Data Analysis using Performance map / Heat map / Tree map visualization ( missing in Performancepoint Services 2010 )

Data Analysis using visualizations: Data analysis is a very complex process depending upon the volume of data, nature of data and nature of data analysis. The most basic form of data representation is in a tabular or matrix format, but the amount of data analysis that can be done straightaway with such representation is very limited and time-consuming. Especially when the volume is huge and the nature of analysis is complex and detailed, a better mechanism of data representation is required to facilitate better data interpretation by the data analyst. Check out this gallery to get an overview of different kinds of visualization available for data representation. Those who are innovative and have special interest in creating and using innovative data visualizations should check out Google Visualization API Gallery.

One of the commonly used data visualizations is Tree Map and Heat Map. They have quite a lot of resemblance, but the difference is the purpose for which they are used. Heat map is generally used more in scientific applications, and Tree Map is the visualization that is used widely for portfolio analysis like Securities data, Performance data, Statistical distribution analysis and others. Tree Maps are also interchangeably known as Performance maps. Those who are curious to learn in detail about the scientific theory and usage of Tree Maps, can check out more from here.

Analyzer Recipe: Proclarity used to have Performance maps, but after the evolution of PerformancePoint Server 2007 which now exists in the form of PerformancePoint services in SharePoint 2010, this visualization is still missing. Developers still try to emulate creation of tree maps using mathematical algorithms, which shows the importance and need of this visualization. This post is focused on discussing how data can be analyzed using Strategy Companion's Analyzer with this data visualization.

Let's get started with creating a simple dataset to analyze. I have used the AdventureWorks cube as the primary source of data for reporting and analysis. To get an idea of the Analyzer report authoring environment, please go through the previous Analyzer tip.

1) Create a pivot table, which shows data in an advanced tabular / matrix format.

2) On the row axis, I have selected the namedset "Top 50 Customers". On the column axis, I have selected the "Calendar Year" hierarchy from the "Date" dimension. On the values / details area, I have used the "Internet Sales Amount" measure. After you have configured your pivot table with this data, your report should look something like the below screenshot:

3) Before we move ahead, I would suggest to give a thought on what can you analyze out of this report and how quickly can you analyze this data. From the menu option of the PivotTable control, select the "Analyze in TreeMap" option. Configure the options as shown in the below screenshot. To get sufficient screen space, I have opted to create the map in a new sheet.

4) After your Tree Map has been created, your report should look like the below screenshot. If you hover over individual boxes / rectangles, you would find the values displayed in the tooltip.

Just give a thought now as to what can you analyze from this report and how quickly? As per the configuration set by us, the white color is the indication of the lowest value and brightest blue is the indication of the highest value. If you analyze carefully, you can easily make out clusters of worst, average and best sales values and customers associated with them. Analysis can be done within a single year as well as a comparative analysis can be done across years from the size of box created for each year.

5) To take our analysis a level further, go to the sheet containing the pivot table and select "Expand Members" at the "Calendar Year" hierarchy. Now come back to this sheet, and the report should look like the below screenshot. Just looking at this screen, I was able to make out that H2 - 2006 and H1 - 2007 was not good in terms of internet sales. There is much more analysis that can be done using this Tree Map.

Read-world Application: A real-world example of a company using this Analyzer visualization is Citigroup, which stores performance and capacity-planning related data about all of its global servers and applications (which as you can imagine is a lot of servers and applications) in Analysis Services cubes. Citigroup uses Analyzer Tree Maps to visualize all of this data, and uses the traditional red and orange colors to indicate current and potential problem areas, such as a server reaching its capacity limit. They store this information in a hierarchy of server-application-hour of day, so they can drill down to a specific hour of a specific day for a specific application running on a specific server, anywhere in the world. The colors automatically highlight for them which servers or applications should be looked at more closely.

I suggest that one should at least download an evaluation version of Analyzer and give a try to this recipe. Analyzer as a reporting solution supports the Tree Map visualization, which is still a limitation with PPS 2010 and SSRS 2008 R2 too. I am sure this must be on the plans of PPS and SSRS teams. To realize how valuable it is to have this visualization created with a single-click, try to simulate this effect in your SSRS reports and you would feel the value of this visualization and worth of Analyzer as a reporting solution that supports this visualization.