Data Pivot is a process of rotating the view of data. In databases where there is high level volume of data, it is often very difficult to get a view of a particular data or report. A pivot table helps overcome this problem by displaying the data contained in the database by means of automated calculations which are defined in a separate column side by side with data column of the requested data view.
There are several advantages to using pivot tables. One advantage is that a pivot table summarizes the data contained in a long list into a compact format. A pivot table can also help one find relationships within the data that are otherwise hard to see because of the amount of detail. Yet another advantage is that a pivot table organizes the data into a format that is very easy to convert into charts.
A pilot table also includes many functions including automatic sort, count, and total the data stored in a spreadsheet and create a second table displaying the summarized data. A user can set up changes of the summary structure by graphically dragging and dropping, the name pivot or rotating gave the concept its name.
Typically, a pilot table contains rows, columns and data or fact fields. Most application offering data pivot features can easy invoke the feature with a pivot table wizard in few steps. Commonly, the first involves specifying where the data is located and whether a chart as well as table should be displaced.
The second step is simply about identifying the list range. It is common to have an insertion point so that the wizard can just define the list range in an automated way. And then the final step is just specifying the graphical layout of the final data pivot view.
There are generally two ways to make a pivot table layout – Discrete and. Continuous Variables. Commonly, a discrete variable has a relatively small number of unique values and these unique values are of different names.
For example, discrete variables include such as values as department names, model names, or customer names in a company database. Discrete values are more suitably used as row and column variables in a data pivot table but they can of course be used also as data fields. However, if discrete values are used as data field, the data pivot table can only display a summary by count.
On the other hand, a continuous variable can take on a large range of values. Some examples of continuous variables include units sold, profit margin and daily precipitation. In general, it may not be a good idea to use a continuous variable as a row or column variable in a data pivot table because the result would be an impossibly large table.
For instance, to analyze income for, say, 500 firms, using the firm income as a column variable in the data pivot table you possibly make the software application run out of display space. A continuous variable is commonly used as the data field in a data pivot table in cases where one wants to see the sum or average or other summary calculation of its values for different levels of discrete variables.
There is a method available in order to significantly reduce the amount of data to be displayed. This method is called "Grouping".
For example, dates used as row or column headers can be grouped. Data pivot table is an indispensable too in data warehouses as disparate data of high volumes need to be formatted in order to get industry and business operations related reports that reflect trends and pattern.