Friday, 23 May 2014

OBIEE 11g Concepts (IV) – What are Views? Which Views are Supported?


There are number of questions business users ask around OBIEE/OBIA implementations. Some of them are specific to functional stuff e.g.  #invoice on hold represents # hold invoices or # invoices items?  In addition some of the ambitious questions are around product features and basic product offerings e.g. what is difference between filter and selection steps?    How many different types of views OBIEE supports etc. 

This blog series is an attempt to give a simple layman definition of number of concepts or OBIEE 11g terminology /offering. Prompts can be used to limit data for specific users; dashboard prompts can be used in several dashboards, and modified, with changes applying to all dashboards using the prompt, reducing cost of ownership. In this blog I am covering presentation capabilities (views) of OBIEE 11g.The definitions are extracted from Oracle Standard Product documentation.

What are Views? 

Views use the presentation capabilities of Oracle BI Enterprise Edition to help users look at results of analyses in meaningful, intuitive ways. User can add a variety of views to the results, such as graphs and pivot tables that allow drilling down to more detailed information, explanatory text, a list of filters that were used to limit the results, and more.

Default Views

When users display the results of a new analysis, the following views are displayed by default in the "CompoundLayout" in the "Analysis editor: Results tab".

A title view, which displays the name of the saved analysis.

A table or pivot table view, which displays the results of the analysis, depending on the types of columns that the analysis contains:

If the analysis contains only attribute columns, only measure columns, or a combination of both, then a table is the default view.

If the analysis contains at least one hierarchical column, then a pivot table is the default view.

Customize Views

User can customize or delete the existing views for an analysis, add other views, and combine and position views anywhere in the pane. Preparing multiple views of results can help you identify trends and relationships in data. If you are customizing results for display on a dashboard, then you can preview how the combination and position of views looks when viewed on a dashboard. You can then save the analysis with the collection of views.

Views Types

The following is a list of all views supported by OBIEE 11g along with the description of views. Privileges control whether one can create all views or specific views only.

View Name
Description
Title
Displays a title, a subtitle, a logo, a link to a custom online help page, and timestamps to the results.
Table
Displays results in a visual representation of data organized by rows and columns. A table provides a summary view of data and enables users to see different views of data by dragging and dropping rows and columns.
Pivot Table
Displays results in a pivot table, which provides a summary view of data in cross-tab format and enables users to see different views of data by dragging and dropping rows and columns. Pivot tables structure data similarly to standard tables that contain column groups, but can display multiple levels of both row and column headings. Unlike regular tables, each data cell in a pivot table contains a unique value. By organizing data in this way, a pivot table is more efficient than a row-based table. Pivot tables are ideal for displaying a large quantity of data, for browsing data hierarchically, and for trend analysis.
Graph
Displays numeric information visually, which makes it easier to understand large quantities of data. Graphs often reveal patterns and trends that text-based displays cannot. However, when precise values are needed, graphs should be supplemented with other data displays, such as tables. A graph is displayed on a background, called the graph canvas.
Funnel
Displays results as a three-dimensional graph that represents target and actual values using volume, level, and color. Typically, funnel graphs are used to graphically represent data that changes over different periods or stages.

For example, funnel graphs are often used to represent the volume of sales over a quarter. Funnel graphs are well suited for showing actual compared to targets for data where the target is known to decrease (or increase) significantly per stage, such as a sales pipeline.

In funnel graphs, the thresholds indicate a percentage of the target value, and colors provide visual information for each stage. User can click one of the colored areas to drill down to more detailed information.
Gauge
Shows a single data value. Due to its compact size, a gauge is often more effective than a graph for displaying a single data value.
Gauges identify problems in data. A gauge usually plots one data point with an indication of whether that point falls in an acceptable or unacceptable range. Thus, gauges are useful for showing performance against goals.

Depending on the data in the analysis, a gauge view might consist of multiple gauges in a gauge set. For example, if you create a gauge view to show the sales data for the last twelve months, the gauge view consists of twelve gauges, one for each month. If you create one to show the total sales in the US, then the gauge view consists of one gauge.

A gauge or gauge set is displayed on a background, called the gauge canvas.
Trellis
Displays multidimensional data shown as a set of cells in a grid, where each cell represents a subset of data using a particular graph type. Data can be represented with graphs, micro charts, and numbers.
The trellis view has two subtypes: Simple Trellis and Advanced Trellis.
Simple trellis views are ideal for displaying multiple graphs that enable comparison of like to like. Advanced trellis views are ideal for displaying spark graphs that show a trend.
A simple trellis displays a single inner graph type, for example a grid of multiple Bar graphs. The inner graphs always use a common axis; that is to say, the graphs have a synchronized scale.
An advanced trellis displays a different inner graph type for each measure. For example, a mixture of Spark Line graphs and Spark Bar graphs, alongside numbers. In this example, the Spark Line graph might show Revenue over time and the Spark Bar graph might show Units Sold. A measure column displaying numbers might be placed adjacent to the Spark Line graphs, showing the Revenue measure as a total value for a year. In an advanced trellis, each measure column operates independently for drilling, axis scaling, and so on.
Filters
Displays the filters in effect for an analysis. Filters, like selection steps, allow user to constrain an analysis to obtain results that answer a particular question. Filters are applied before the query is aggregated.
Selection Steps
Displays the selection steps in effect for an analysis. Selection steps, like filters, allow you to obtain results that answer particular questions. Selection steps are applied after the query is aggregated.
Column Selector
Adds a column selector in the results. A column selector is a set of drop-down lists that contain pre-selected columns. Users can dynamically select columns and change the data that is displayed in the views of the analysis.
View Selector
Adds a view selector in the results. A view selector is a drop-down list from which users can select a specific view of the results from among the saved views.
Legend
Adds a legend to the results, which enables you to document the meaning of special formatting used in results, such as the meaning of custom colors applied to gauges.
Narrative
Displays the results as one or more paragraphs of text. You can type in a sentence with placeholders for each column in the results, and specify how rows should be separated.
Ticker
Displays the results as a ticker or marquee, similar in style to the stock tickers that run across many financial and news sites on the Internet. User can control what information is presented and how it scrolls across the page.
Static Text
Adds static text in the results. User can use HTML to add banners, tickers, ActiveX objects, Java applets, links, instructions, descriptions, graphics, and so on, in the results.
Logical SQL
Displays the SQL statement that is generated for an analysis. This view is useful for trainers and administrators, and is usually not included in results for typical users.
User cannot modify this view, except to format its container or to delete it.


This blog series is an attempt to expand my blog reach to BI End User or Business Users along with BI Developers/Architects.

Thursday, 22 May 2014

OBIEE 11g Concepts (III) – How Do Inline and Dashboard Prompts Differ?


There are number of questions business users ask around OBIEE/OBIA implementations. Some of them are specific to functional stuff e.g.  #invoice on hold represents # hold invoices or # invoices items?  In addition some of the ambitious questions are around product features and basic product offerings e.g. what is difference between filter and selection steps?    How many different types of views OBIEE supports etc.

This blog series is an attempt to give a simple layman definition of number of concepts or OBIEE 11g terminology /offering. Prompts can be used to limit data for specific users; dashboard prompts can be used in several dashboards, and modified, with changes applying to all dashboards using the prompt, reducing cost of ownership. In this blog I am covering differences between Inline and Dashboard Prompts. The definitions are extracted from Oracle Standard Product documentation.

Inline and Dashboard Prompts

The two differences between inline prompts and dashboard prompts is where they are stored and their run-time behavior.

Inline Prompt

A prompt that is created at the analysis level is called an inline prompt because the prompt is embedded in the analysis and is not stored in the Oracle BI Presentation Catalog and, therefore, cannot be added to other analyses.
Inline prompts allow the end users to specify the data values that determine the content of the analysis. An inline prompt can be columns prompt variable prompt, image prompt, or currency prompt. When you create an inline prompt, you select the columns and operators for the prompt and specify how the prompt is displayed to the users and how the users select the values. The user's choices determine the content of the analyses that are embedded in the dashboard. An inline prompt is an initial prompt, meaning that it only displays when the analysis is rendered. After the user selects the prompt value, the prompt fields disappear from the analysis and the only way for the user to select different prompt values is to re-run the analysis.

Dashboard Prompt

A prompt that is created at the dashboard level is called a dashboard prompt because the prompt is created outside of a specific dashboard and is stored in the catalog as an object, which can then be added to any dashboard or dashboard page that contains the columns that are specified in the prompt. Dashboard prompts allow the end users to specify the data values that determine the content of all of the analyses and scorecard objects contained on the dashboard. A dashboard prompt can be a column prompt, variable prompt, image prompt, or currency prompt. Dashboard prompts are reusable, because you can create one prompt and use it many times. When the prompt object is updated and saved, those updates are immediately displayed in all dashboards where the prompt is used. A dashboard prompt is a specific kind of filter that, when created, saved, and applied to a dashboard or dashboard pages, can filter all or some of the analyses and scorecard objects that are embedded in a dashboard or analyses and scorecard objects that are embedded on the same dashboard page.

A dashboard prompt is interactive and is always displayed on the dashboard page so that the user can prompt for different values without having to re-run the dashboard. Users can create and save dashboard prompts to either a private folder or to a shared folder.

Note:  For a dashboard using a column that was renamed in the Business Model, the existing dashboard prompts based on the renamed column do not work with newly created analyses. The workaround for this issue is to use Catalog Manager to rename the column in the catalog.

This blog series is an attempt to expand my blog reach to BI End User or Business Users along with BI Developers/Architects.

OBIEE 11g Concepts (II) – How Do Filters and Selection Steps Differ?


There are number of questions business users ask around OBIEE/OBIA implementations. Some of them are specific to functional stuff e.g.  #invoice on hold represents # hold invoices or # invoices items?  In addition some of the ambitious questions are around product features and basic product offerings e.g. what is difference between filter and selection steps?    How many different types of views OBIEE supports etc. 

This blog series is an attempt to give a simple layman definition of number of concepts or OBIEE 11g terminology /offering. In this blog I am covering differences between filters and selection steps. The definitions are extracted from Oracle Standard Product documentation.

Filters and Selection Steps

Filters and Selection Steps are used to limit the results that are displayed when an analysis is run, so that the results answer a particular question

Together with the columns that user selects for an analysis, filters and selection steps determine what the results contain. Based on the filters and selection steps, only those results that match the criteria are shown. For example, depending on the organization in which user work, you can use filters and selection steps to learn who are the top ten applicant sources, what are the work load is for a particular group of recruiters, the types of requisition have the fastest time to fill, and so on.
Another kind of filter, called a prompt, can apply to all items in a dashboard. Prompts can be used to complete selection steps and filters at run-time.

Oracle BI provides the Filters view and Selection Steps view, which user can add to an analysis to display any filters or selection steps applied to the analysis. Adding these views can help the user understand the information displayed in the analysis.

How Do Filters and Selection Steps Differ?

Filters and selection steps are applied on a column-level basis and provide two methods for limiting the data in an analysis. A filter is always applied to a column before any selection steps are applied. Steps are applied in their specified order. Filters and selection steps differ in various ways.

Filters

Filters can be applied directly to attribute columns and measure columns. Filters are applied before the query is aggregated and affect the query and thus the resulting values for measures.

For example, suppose that there is a list of members in which the aggregate sums to 100. Over time, more members meet the filter criteria and are filtered in, which increases the aggregate sum to 200.

Selection Steps

Selection steps are applied after the query is aggregated and affect only the members displayed, not the resulting aggregate values.

For example, suppose that you have a list of hierarchical members in which the aggregate sums to 100. If you remove one of the members using a selection step, then the aggregate sum remains at 100.

Attribute & Hierarchical Columns 

One can create selection steps for both attribute columns and hierarchical columns. Selection steps are per column and cannot cross columns. Because attribute columns do not have an aggregate member, the use of selection steps versus filters for attribute columns is not as distinctive as for hierarchical columns.

Measure Columns

While measure columns are displayed in the Selection Steps pane, you cannot create steps for them so steps do not affect them. Measures are used to create condition steps for attribute and hierarchical columns, such as Requisitions open for more than one year.

This blog series is an attempt to expand my blog reach to BI End User or Business Users along with BI Developers/Architects.