Your business data has been collected, now what? How data is represented is very important for analyzing and sharing this information. You can achieve better results when analyzing your data if it is mapped out and displayed efficiently. InsFocus is developing the use of http://fairchanceproject.com/timeline?share=email “treemaps” as another visual option for displaying your collected data in their business intelligence suite. So, what are treemaps and what is their significance in business?
order prednisone online Treemapping is a method for displaying hierarchical information through the use of nested figures, which are most commonly rectangles. Hierarchical data can be explained as “tree-structured” from a visual standpoint, having data of layered importance and grouping. Each branch of the tree is depicted as a rectangle, then it is tiled with smaller rectangles that represent sub-branches. There are also leaf node rectangles that have a size in relation to the dimension of the data they represent. Often, the leaf nodes are colored differently to show them as a different dimension of data.
Using color and size to correlate with the structure of a tree in treemapping makes it easier for you to see patterns within the data that might be difficult to spot otherwise. For example, a certain color might be particularly relevant. Another advantage of using tree maps is their efficient use of space. Due to how they are constructed, they can legibly show thousands of items on the screen simultaneously for companies with a lot of data to represent. Once again, the use of color and size helps you spot significant patterns and trends even with thousands of items without having to strain.
InsFocus is currently working on its buy Rizatriptan uk next version of InsFocus BI, which will implement the visualization method of treemaps. In order to create a treemap, a tiling algorithm must be outlined that generally consists of two data axes. InsFocus will be using a size axis and color axis. The size axis uses a value point that determines the size of each rectangle, and the color axis uses another value point that determines the color of each rectangle. From there, the rectangles use hierarchical dimensions, such as state or city, to group rectangles together.
Alaminos Let us look at some InsFocus case examples of how treemaps work:
Case #1 – Product Distribution in State
In order to display the product distribution in state, the analysis set shows products grouped by state. The size axis uses the value of premiums over the last 12 months, and the color axis is set for none (all the same color). This helps the viewer find the biggest products in each state while also comparing product distribution between states. In this example, the visualization of size is used without using color at all.
Case #2 – Agency Profitability by Size
In order to display the agency profitability by size, the analysis set shows agents grouped by branches. The size axis uses the value of agent premiums over the last 12 months, and the color axis is set for agency loss ratio with higher values turning to red and lower values turning to white. This helps the viewer to focus on relatively large agents with a bad loss ratio.
Case #3 – Payment by Claim Type
In order to display the payment by claim type, the analysis set shows claim types. The size axis uses the value of number of claims opened, and the color axis is set for average claim incurred in claim type. This helps the viewer to see what claim types produce high average claim incurred values versus the number of claims in this type.
These case examples reveal how treemaps make it easier to visualize your data and analyze the information. This is significant for your business because patterns are more easily apparent with ease and efficiency. Also, more data means less of a problem for you since you can display thousands of items at once.
InsFocus is excited to bring this new visualization of data to all clients with their new version of InsFocus BI. Check back for updates on this new development that will make analyzing your data easier than ever before.