Knowledgebase: RadarCube WinForms MSAS
Visual analysis with RadarCube OLAP Chart
Posted by - NA - on 17 July 2008 04:20 PM
The visual OLAP-analysis is an advanced technology that creates graphical OLAP-data slices suitable for visual manipulation of data to reveal "hotspots", hidden interrelation between different kinds of data and form graphical reports.
The screenshot below illustrates two ways of presenting the same data: textual and graphical, so that you can see the difference between them.
Graphical and textual OLAP-analysis is based on the same technology: presenting data in the form of OLAP-slice that can be pivoted (pivoting is rotation of the Cube), drilled, filtered, sorted or grouped.
In addition to that, the graphical OLAP has its unique technologies, such as:
Now, let’s discuss these visual analysis technologies in detail.
Visual analysis and comparison of measures
This is the easiest way of revealing hotspots in the analyzed data. With the correct way of graphic representation, an experienced analyst will evaluate the situation at a glance and make the necessary decision.
Using all the facilities of the graphic OLAP-analysis implies that you understand the methods of processing data OLAP-slices. We’ll discuss them below.
Revealing the interconnection between the measures in view of the inflectional factors
Placing different measure to the X and the Y axes, lets you detect the correlation between the measures even with different detaining conditions. For such analysis of data, place one of the measures into the Rows area, and the second – into Columns. The detailing hierarchies are situated in the Color, Shape and Details areas. For example:
Analysis may reveal other correlation types:
This type of analysis reveals groups of the detailed positions and their influence on the measure values. For example, for the Internet Gross Profit and the Internet Average Unit Price measures in view of products, we get the following chart:
As we can see, the points are grouped in clusters, within which they have approximately the same corresponding axis values. Select one of the groups and watch the information about the selected points (click the chart, after the area is selected, click the right button of the mouse and choose "Show Underlying data"):
As we see, all the selected points have the same value in the "Product Category" and "Product Subcategory". Let’s suppose all the other products are grouped according to the same principle. To confirm our idea, let’s place the "Product Subcategory" to the color modifier and the "Product Category" – to the shape modifier.
Thus, our idea was correct: the clusters consist of the products of the "Bikes" category (circles on the chart), and the most dense once belong to the same subcategory (circles of the same color).
Using modifiers for more efficient visual OLAP reports
If you need to display the values of several meausres on the same Cube axes, then using series or even a few charts is not your only option, neither, in most cases it is in fact effective.
If among the displayed measures we can point out the main measure, more important than the secondary ones, whose values are not crucial, then it makes sence to use color or size modifiers for displaying those secondary measures:
This chart shows the changes of the “Sales” measure with time. The color of lines and points of the chart depends on the “Order Count” measure values and changes from red to green. The same as in the previous chart. The difference is: the value of the “Order Count” measure, placed to the size modifier as well, influences the size of the point.
Not only measures can be placed to modifiers area, but hierarchies as well. In this case, each hierarchy member is represented on the chart by its own series.