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.