Building basic data visualizations and queries is, in effect, a simple 2 step process. The process is designed to be fast, intuitive and ultimately riven through a point-and-click experience without active intervention or scripting. As the user makes their selections and changes, the queries run automatically or can be manually executed - each time redrawing the data into the chosen visual as dictated by the drop zones.
- Select a data visualization, which will dictate which "drop zones" are visible. Drop Zones tell us what data elements to put where in drawing the visual in the next step.
- Place items from your data model in to the appropriate drop zone to drive the way the visual is rendered. This usually requires a selection of hierarchies and measures:
- Select hierarchies (columns) of data elements from your data model (like 'product', 'time', 'customer' etc)
- Select values (measures or metrics) from your data model (like 'sales', 'quantity', ' row count')
These steps can happen in almost any order, and its not unusual for people to select and change as they go along in an ad-hoc manner (aka "ad-hoc data analysis"). In making selections and adjusting drop-zone assignments, users can build a huge variety of different queries and data visualizations without using any advanced logic or functionality.
Basic to Advanced Querying
Once a user has created a basic data visualization, there invariably is a need to add more sophistication to the query or the visual. To accomplish the more advanced demands for queries, visualizations and reports, the Discover tool exposes a huge amount of advanced features and functions that allows the user to "change" the basic query selections described in the section.
- Click here to see Changing Queries which covers functionality like, slice, dice, drill, filters, sorts, pivots, totals, eliminations, advanced analytic functions.
- For even more sophistication, see Calculations which covers functionality like adding formulas, logical lists and query parameterization
Usually, queries auto-execute once selections are made. As a query executes it automatically starts populating the visualization with graphical representations of the data selected. In some scenarios the visualization will not materialize until a minimum amount of structural elements have been selected. Queries can be also be set to run manually, and there are other aspects that can govern the way queries run - including caching and meta data.
- Click here for more details on running queries.