Advanced Visualization Techniques

Advanced Visualization Techniques: The most remarkable advance in visualization techniques is the transition from static charts in dynamic interactive presentations.

Chart Manipulation: A user can rotate it chart or dynamically change the chart type to get a clearer view of the results. With complex visualization types such as constellation and scatter plots, a user c.iin select data points with a mouse and then move the points around to clarify the view.

Drill Down: The visualization first presents the results at the summary level. The user can then drill down the visualization in display further visualizations at subsequent level of details.

Advanced Interaction: These techniques provide a minimally invasive user interface. The user simply double clicks a part the visualization and then drags and drops representations of data entiticts. Or the user simply right clicks and chooses options from a menu. Visual query is the most advanced of user interaction features. For example the may see the outlying data points in a scatter plot, then select a few or them with the mouse and ask for a brand new visualization id -just those selected points. The data visualization software generates the appropriate query from the selection; submits the query to the database and then displays the results in another representation.

Multiple Data Types

Multiple Data Types

When you build the first iteration of your data warehouse, you may just include numeric data. But soon you will realize that including structured numeric data alone is not enough. 13e prepared to consider other data types as well.

Traditionally, companies included structured data, mostly numeric, in their data ware-houses. From this point of view, decision support systems were divided into two camps: data warehousing dealt with structured data knowledge management involved unstructured data. This distinction is being blurred. For example, most marketing data consists of structured data in the form of numeric values. Marketing data also contains unstructured data in the form of images. Let its say a decision maker is performing an analysis to find the top-selling product types. The decision maker arrives at a specific product type in the course of the analysis. llc or she would now like to see images of the products in that type to make further decisions. !low can this be made possible? Companies are realizing there is a need to integrate both structured and unstructured data in their data warehouses.

What are the types of data we call unstructured data? Figure 3-4 shows the different types of data that need to be integrated jr the data warehouse to support decision making more effectively…

Figure 3-5 Data visualization trends

Let us now turn to the progress made in the industry for including some of the types of Cr three-dimensional representations. Executives and managers, who need to monitor performance metrics, as like digital dashboards that allow them to visualize the metrics as speedometers, thermometers. or traffic lights. In the last few years, three major trends have shaped the direction; or data visualization software.

SIGNIFICANT TRENDS

 SIGNIFICANT TRENDS

Some experts feel that technology has been driving data warehousing until now. These experts declare that we arc now beginning to see important progress in software. In the next few years, data warehousing is expected make big strides in software, especially for optimizing queries, indexing very large tables, enhancing SQL improving data compression, and expanding dimensional modeling.

Let us separate out the significant trends and discuss each briefly. Be prepared to visit each trend. One by one- everyone has a serious impact cm data warehousing. 

Figure 3-3 Data warehousing product by function

 

As we walk through each trend, try to grasp its significance and be sure that you perceive its relevance to your company data warehouse. Be prepared to answer these questions? What must you do to take advantage of the trend in your data warehouse?