Explore the tabs below to see the features available within Heat Map Explorer. To explore further, download a free trial of the desktop, or contact us to set up a trial of our server and SDK products.
Squarified tree maps arrange data items from the largest size value to smallest size value, starting in the upper-left of each group. Cells are arranged to appear as square as possible, enabling labels to easily be displayed and making it easier to compare sizes.
These maps are especially useful for any type of portfolio analysis, where you need to understand the distribution of items and values across groups and sub-groups, and see how individual items relate to the whole.
Strip tree maps arrange data items side-by-side in a series of rows, maintaining the order of items. The strip data is useful when the order of the data is important, such as with real-time data.
Horizontal and vertical chain maps stack data items within rows or columns to make it easy to see the number of items in each category. These maps are especially useful for pipeline, time and progress analysis.
Histogram maps divide the selected numerical column into 100 equal bins and stack individual data items into bins that correspond to their value for that column. These maps show how items are distributed across the range of values and are very useful for identifying outliers or seeing shifts in results or behavior across different categories.
The column and column bar maps use the settings for Group By for rows and Then By for columns in order to build a uniform grid, with individual records then arranged in a squarified or bar map within each grid cell. These maps make it easy to identify gaps within categories since equivalent Then By categories are lined up vertically. Column maps are particularly useful for looking at how data in different categories changes over time by setting Then By to a time-period column.
The group bar map builds a bar chart with bars determined by the Group By category, but arranges items within each bar as a regular squarified map. This map makes it straightforward to compare the relative contributions of different categories while still offering visibility into the full portfolio of results.
The basic bar map arranges data items as bars of varying height side-by-side in a single row like a bar chart. Like the strip map type, data cells maintain their original order, or can be sorted. Bar maps are useful to compare ordered data between multiple groups and to perform fine-grain comparisons of the size values.
See your data geographically. Geographic maps shows how your data relates to areas around the globe. Import your own custom territory maps to see how your data maps onto your sales territories and markets.
Understanding how your business data relates or impacts different parts of your organization can be critical. Convert your org chart into an interactive heat map that shows project status, change saturation or financial metrics in the context of your organizational structure.
Process Flow Diagrams
Understanding how your data maps into your organizational processes can give you key insights into problems that wouldn’t show up on standard visualizations. Convert your process flow diagrams into heat maps that show you the state of your organization in a relevant context.
Heat Map Explorer expands the range of visual analysis you can perform with the new Detail Level slider, and allows you to better share your insights with the new Notes tab.
The color slider is one of the most powerful controls in Heat Map Explorer. It is the visual representation of the color scheme. It is and indicates which colors exist in the color scheme, and which data values those colors map to.
But it’s not a static legend. By dragging the thumbs on the slider, you can adjust the values for each color, enabling you to fine tune the color scheme to your data set. Or you can right-click for additional options to set the color range or specific values for a color change.
Detail Level Slider
When the map is composed of a very large number of individual records, it can be convenient to hide individual details until you have zoomed in and are ready to consider them. The Detail Level slider, when activated, shows only groups, colorized by the aggregated value of all line items within each group, as shown below.
When analysis brings insight that you need to share, or when you need to record thoughts or follow-up items and keep them together with the map that gave rise to them, the new Notes tab gives you a place to record them. These notes are included with you print a heat map or generate a PDF.
Heat Map Explorer has several styles that can be applied to maps to make them more readable for specific types of analysis.
The Window style emphasizes the hierarchy of your data, making it easy to see the distribution of groups at each level and to identify which group an individual cell belongs to. The large group border also makes seeing a group’s information easy; just hover over the border and the information will appear in the info panel.
The Cluster style colors group borders based on the aggregate color value of each group. Group colors make it easy to see group-level performance issues that may not be obvious just by viewing the collection of cells. Combined with customizable aggregations, the Cluster style can be a powerful way to analyze data.
In the picture below, color is keyed to a performance measure with an average aggregation, allowing us to easily see the average performance for each group as a whole.
The Classic style is a compact style which eliminates the clutter that can happen with the Window and Cluster styles when there are lots of groups, and minimizes size distortion among leaf level cells. This makes it ideal for large data sets with lots of top-level groups.
When maximum display space is needed for your data, Zen style provides a basic and compact view of your data. It eliminates all borders and only displays labels for leaf-level cells. It is ideal for very large data sets where the goal is to get a broad overview of the data.
Cell benchmarking allows you to select a cell as a standard, then visually see how all the other cells in a heat map compare to that standard. When a cell benchmark is created, it calculates the percentage difference between the value of the color field for each cell in the heat map and the benchmark cell. Cells are colorized based on this percentage difference.
For instance, the left picture below shows the original data where color has been mapped to the % change of each stock for the NASDAQ 100. The right picture shows the same stocks benchmarked against Intel. Stocks with a larger % change than Intel are colored green, while those with less of a % change than Intel are colored red.
Group benchmarking allows you to visually see how cells compare to others within their group. When a group benchmark is created, it calculates the average color value of each group, then calculates the percentage difference between this average and the color value of each cell within the group. Cells are colorized based on this percentage difference.
For instance, the left picture below shows the original data where color has been mapped to the % change of each stock for the NASDAQ 100. The right picture shows the same stocks benchmarked against the average of each stock’s sector. Stocks with a larger % change than their sector are colored green, while those with less of a % change than their sector are colored red. As you can see, most stocks in most groups are not far from the average, but there are a few outliers that may be easily seen in the topmost left and right groups.