Textual presentation of data is more precise, but seriously lacks clearness	The approximation of graphical presentation is easily compensated by its clearness

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:

• Visual analysis and comparison of measures, using an adequate presentation of data;
• Revealing interconnections between measures in consideration of influential factors;
• Revealing clusters.

Visual comparison of two measures - For example, Cases Count and Total Cost – in view of problem categories by financial years.

Revealing correlation between Tax Amount and Gross Profit measures in view of the products divided into sales channels. As you can see, there is a clear correlation between these two measures for the products sold through the Internet, but there is none for those sold by resellers

Selection of the products that bring the most significant gross profit. Later we can apply a filter that will leave out only the elements of the selected area, or view and, if necessary, copy the information about the selected objects

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.

Diagrams of cost per problem category, summed up by half-years. Moving the mouse cursor over the point in the diagram, you can get the detailed information about it.

The summed up cost by technicians. The tree most significant regions are highlighted.

Density of distribution of the cost’ summed results by department.

Revealing the interconnection between the measures in view of the inflectional factors
Using all the facilities of the graphic OLAP-analysis implies that you understand the methods of processing data OLAP-slices. 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:

This chart presents a clear correlation between the 'Cost' and the 'Time Spent' measures in view of problems (the Details axis), where the Cost is directly proportional to the Time Spent.

Analysis may reveal other correlation types, such as:

A fuzzy direct proportional relation	A fuzzy inversely proportional relation	No correlation

Revealing Clusters This type of analysis reveals groups of the detailed positions and their influence on the measure values.
For example, for the Cost and the Average Time Spent measures in view of problem categories, 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 "Problem Category" and "Problem Type". Let’s suppose all the other problems are grouped according to the same principle. To confirm our idea, let’s place the "Problem Type" to the color modifier and the "Problem Category" – to the shape modifier.