Data Visualization

We have seen that the graph type that is the most useful cannot be completely derived from the characteristics of the data. The message of the graphical presentation should first be identified in order to make sure that the graph matches the goal of the user. As discovered earlier the goal of the user depends on the phase in the decision process and the state of the data. Furthermore, each graphical technique is linked to the effectiveness of the perception of an observer (Mackinlay, 1986).

Effectiveness criteria for data visualization

According to research performed by Cleveland and McGill there is a difference in the accuracy in quantitative, ordinal and nominal tasks (Mackinlay, 1986). Higher tasks are accomplished more accurately than lower tasks. Tasks to perceive different positions rank as best and differences in shapes are the perceived as being the worst.

Criteria of expressiveness of the data

Variations that should be used for designing expressive graphs and satisfying the users information needs, should at least consider the following data characteristics (Roth and Mattis, 1998b):

Intelligent presentation of the right visualization

In the previous blog, we saw how different characteristics of the data can guide the process of automatic selection of the best control. In order to support the manager with an attractive and effective interface of the information stored in the database, intelligent graphics presentation might be employed. The process of intelligent graphics presentation is directed by five components as is depicted in the figure below.

Mapping data objects to interaction objects

Data is the basic ingredient of information. In the previous blogs, I outlined those parts of the steering model that are subject to variation. Those information-shaped elements are extracted from various internal and external data sources. The internal structure (meta data model) of the data source can give a large amount of extra information that is useful in designing effective interfaces in particular in rendering graphs.

Relationships within hierarchies: important rule

Often the attributes linked to the dimensions in a hierarchy are closely related to each other and are often attributes of the same entity. A customer dimension is related to the region dimension that in turn is related to the country dimension and this can be a reason to make a hierarchy in order to reduce the amount of categories of the dimensions. Dimensions of higher order (those dimensions that have the lowest amount of categories) should reside higher in the hierarchy. The product-line dimension is of a higher order then the product dimension and therefore the hierarchy is as follows: Product-line à Product.

Categories of a dimension

At run time, a dimension is filled with categories. The number of categories can be an indication to:

• Present a different type of graph;
• Decide to use replacement instead of insertion of the categories of the level below;
• Split the categories automatically into smaller groups.

If the number of categories exceeds the limit of 6, a pie chart would be inappropriate (Zelazny, 1996) or the rest of the categories should taken together in a single group ‘Other...’. Moreover, pie charts cannot be used if among the categories there are positive as well as negative numbers. In that case, a bar chart is a better choice.