Business objects PREDICTIVE ANALYSIS 3.1 FOR WINDOWS User Manual

BusinessObjects Predictive Analysis User Guide

BusinessObjects Predictive Analysis 3.1

Windows

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2008-09-03

Predictive Analysis 7Chapter 1

Predictive Analysis populations 9Chapter 2

Adding a population in Predictive Analysis................................................10

Editing a population in Predictive Analysis................................................11

Removing a population in Predictive Analysis...........................................12

The Populations list in Predictive Analysis................................................12

Derived Variables in Predictive Analysis 13Chapter 3

Derived variable as fact table aggregates.................................................14

Creating derived variables in Predictive Analysis......................................15

Creating a binning in Predictive Analysis..................................................17

Editing derived variables and binning in Predictive Analysis.....................18

Deleting derived variables and binning in Predictive Analysis...................18

Predictive Analysis models 21Chapter 4

Adding a model in Predictive Analysis.......................................................22

Copying a model in Predictive Analysis.....................................................24

Editing a model in Predictive Analysis.......................................................25

Deleting a model in Predictive Analysis.....................................................25

Regenerating a model in Predictive Analysis............................................25

Regenerating statistics in Predictive Analysis...........................................26

Predictive Analysis model data types...................................................23

BusinessObjects Predictive Analysis User Guide 3

Contents

Predictive Analysis model-based metrics 27Chapter 5

Adding a model-based metric in Predictive Analysis.................................29

Aggregation functions for model-based metrics...................................30

Goal Level............................................................................................32

Deleting model-based metrics in Predictive Analysis................................33

Refreshing model-based metrics in Predictive Analysis............................33

Purge a model-based metric in Predictive Analysis..................................33

Publishing model-based metrics in Predictive Analysis.............................34

Goal & Metric Publishing Wizard..........................................................34

The model-based metrics list in Predictive Analysis..................................40

Predictive analytics 43Chapter 6

Selecting a predictive model to analyze in a Predictive analytic...............44

Predictive analytic samples in the Analytics Catalog.................................44

Goal-based Influencer Detail analytic based on predictive models...........45

Configuring the Goal-based Influencer Detail analytic.........................46

Setting the display options in Goal-based Influencer Detail and Influencer

Detail analytics.....................................................................................46

Individual List based on predictive models................................................47

Configuring the Individual List analytic.................................................48

Influencer Detail analytic based on predictive models...............................48

Configuring the Influencer Detail analytic.............................................49

Setting the display options in Goal-based Influencer Detail and Influencer

Detail analytics.....................................................................................50

Influencer Gains Chart analytic based on predictive models.....................51

Configuring the Influencer Gains Chart analytic...................................51

Key Influencers analytic based on predictive models................................52

Configuring the Key Influencers analytic..............................................54

Metric Forecaster analytic based on metrics.............................................54

Configuring the Metric Forecaster analytic...........................................55

4 BusinessObjects Predictive Analysis User Guide

Contents

What is a metric forecaster detrended mean?...................................103

Model Gains Chart analytic based on predictive models.........................104

Configuring the Model Gains Chart analytic.......................................104

Model Gains Chart tips.......................................................................105

Variable Profile Box Plot based on predictive models.............................106

Configuring the Variable Profile Box Plot analytic..............................107

Predictive Analysis Terms........................................................................108

What is binning?.................................................................................108

What is a Boolean?............................................................................109

What is a derived variable?................................................................109

What is a goal?...................................................................................110

What is goal status?...........................................................................111

What is goal-based binning?..............................................................111

What is a grain?.................................................................................112

What is an influencer variable?..........................................................112

What is a joiner?.................................................................................114

What is a leaver?................................................................................114

What is a metric?................................................................................114

What is a population?.........................................................................114

What is a predictive model?...............................................................115

What is the root-mean-squared-error?...............................................115

What is the rules engine?...................................................................115

What is a sampling?...........................................................................115

What is a variable?.............................................................................116

Predictive Analysis Terms 117Chapter 7

What is binning?......................................................................................118

What is a Boolean?..................................................................................118

What is a derived variable?.....................................................................118

What is a goal?........................................................................................119

What is goal status?................................................................................120

BusinessObjects Predictive Analysis User Guide 5

Contents

What is goal-based binning?...................................................................120

What is a grain?.......................................................................................121

What is an influencer variable?...............................................................121

Predictive Analysis model data types.................................................121

What is a joiner?......................................................................................123

What is a leaver?.....................................................................................123

What is a metric?.....................................................................................123

What is a population?..............................................................................123

What is a predictive model?.....................................................................124

What is the root-mean-squared-error?....................................................124

What is the rules engine?........................................................................124

What is a sampling?................................................................................124

What is a variable?..................................................................................125

Get More Help 127Appendix A

Index 131

6 BusinessObjects Predictive Analysis User Guide

Predictive Analysis

BusinessObjects Predictive Analysis allows you to build models that show the relationship between variables and determine the key influencers of that specific or goal variable.

In Predictive Analysis, you work with:

• Populations

You identify the groups that you want to analyze. This is not necessary if you are creating a Metric forecaster analytic.

• Derived Variables

You can customize variables that refine your results and do binning of the variable contents to show trends by groups. This is not necessary if you are creating a Metric forecaster analytic.

• Models

You configure a model analysis that includes populations, derived variables, influencers, and goals that you can put into an analytic. This is not necessary if you are creating a Metric forecaster analytic.

• Metrics

You can create model-based metrics that you use to improve your models.

• Predictive Analytics

Predictive Analysis provides you with pre-defined analytics that you can use with your models.

8 BusinessObjects Predictive Analysis User Guide

Predictive Analysis populations

Adding a population in Predictive Analysis

Any analysis requires the definition of the data to be analyzed. The data for the following influencer analytics is defined in a population:

• Influencer Detail

• Key Influencers

• Influencer Gains Chart

• Model Gains Chart

• Variable Profile Box Plot

• Individual List

This section shows you how to work with populations.

You can do the following:

• Add, edit and remove populations

• Change the population list view

Related Topics

• Influencer Detail analytic based on predictive models on page 48

• Key Influencers analytic based on predictive models on page 52

• Influencer Gains Chart analytic based on predictive models on page 51

• Model Gains Chart analytic based on predictive models on page 104

• Variable Profile Box Plot based on predictive models on page 106

• Individual List based on predictive models on page 47

Adding a population in Predictive Analysis

1. In Predictive Analysis > Populations, select from the drop-down list a

population area in which you want to create your population.

2. Click Add.

The Create Population wizard opens.

3. In the Set step, select a set type from the list.

Note:

You can view the source set list by sets or groups. To create a set, see the Set Analysis documentation.

• If you select Single Set, you need to select a subset.

10 BusinessObjects Predictive Analysis User Guide

Predictive Analysis populations

Editing a population in Predictive Analysis

• If you select Migrants, Overlapping Members, Same Time Joiners

or Same Time Leavers, you need to select a destination set.

4. Specify the time period:

• To specify a period, activate During last and set the number of

periods.

• To specify a month, activate For and select the month and year.

5. Click Next.

6. In the Filter step, select conditions in the Available condition list and

use the double arrow button to move them to the Selected conditions list.

7. Click Next.

8. In the Attributes step, type the name for the population you are creating.

9. If you want others to be able to use this population, activate Public.

10. Click Finish.

The population you created appears in the Populations list. The right-side panel shows the details of your population.

Related Topics

• What is a joiner? on page 123

• What is a leaver? on page 123

• What is a population? on page 123

Editing a population in Predictive Analysis

1. In Predictive Analysis > Populations, select from the drop-down list a

population area in which you want to create a population.

2. Select the population you want to edit.

3. Click Edit.

4. The Edit Population wizard that appears follows the same workflow as

that for adding a population.

Related Topics

• Adding a population in Predictive Analysis on page 10

BusinessObjects Predictive Analysis User Guide 11

Predictive Analysis populations

Removing a population in Predictive Analysis

1. In Predictive Analysis > Populations, select from the drop-down list a

population area in which you want to create a population.

2. Select the population you want to remove.

3. Click Remove, then OK.

The Populations list in Predictive Analysis

In Predictive Analysis > Populations, you can view the Populations list in the following ways:

• Flat list

Select this option to see a flat list of the populations for a Population area.

• By Sets

Select this option to see the list of populations by sets.

Note:

This option is only available if the population is based on a set universe.

• Show Groups

Select this option to see the list by set groups.

• By subsets

Select this option to see the list by subsets, for example by joiner or leaver subsets.

12 BusinessObjects Predictive Analysis User Guide

Derived Variables in Predictive Analysis

Derived variable as fact table aggregates

When you are setting up an analysis, you can customize a variable to extract specialized information.

For example, from the variable 'Owner' you can extract the owners in a certain age range. Instead of creating a variable in the universe, you can create a derived variable in Predictive Analysis. In addition you can refine the results for 'Owner' by binning the results by age.

You can use measures with arithmetic or logical operators to form more complex expressions.

With derived variables, you can:

• specify different date restrictions on measures with calculated

aggregations from fact tables.

• create duplicates of universe measures for cross-sell analysis.

• create a variable that represents membership in a specified set (for

example, member of "Frequent Buyers").

• experiment with private variables representing alternative approaches

before choosing one to make available to others.

You must create a boolean goal variable if you want to use the following analytics:

• Influencer Gains Chart

• Model Gains Chart

In Predictive Analysis > Derived Variables, you can create, edit and remove derived variables and binninsg.

Related Topics

• Influencer Gains Chart analytic based on predictive models on page 51

• Model Gains Chart analytic based on predictive models on page 104

Derived variable as fact table aggregates

Predictive Analysis allows you to define derived variables using a formula editor. Derived variables allow users to use fact table data in Predictive Analysis.

14 BusinessObjects Predictive Analysis User Guide

Derived Variables in Predictive Analysis

Creating derived variables in Predictive Analysis

Derived Variable Syntax for Measures

The general syntax for measures used in derived variable definition is as follows (square brackets [] represent optional parameters):

<m:Measure name,Null value replacement constant,[<t:Aggregate

type>, [Date expression], [Date expression]]>

For example:

<m:Profit, 0,<t:Sum>, {1/1/2002}, CurrentDate()> Sum of profit from 1/1/2002 until today, with nulls replaced by 0 <m:Order Size, 100,<t:Maximum>, CurrentDate() - 30*6, Current Date()> Maximum order size in the last 180 days, with nulls replaced by 100 <m:Order Size, 0, <t:Maximum>, Date(CurrentYear(), CurrentMonth()-6, CurrentDay()),Current Date()> Maximum order size in the last 6 months with nulls replaced by

Creating derived variables in Predictive

Analysis

1. In Predictive Analysis > Derived Variables, select a variable subject

area from the dropdown list.

2. Click Add.

3. Select New Derived Variable.

4. In the Create Variable panel, type a name for the variable in the Variable

name box.

5. If you want others to use the variable, activate Public.

6. Select the Data type:

• Boolean

• Date

• Numeric

• Character

Note:

Formulas using a date can be treated as a data or numeric value, representing a monotonically increasing number of days. When used in a model definition, avoid explicit dates such as 'date of birth'. Use instead

BusinessObjects Predictive Analysis User Guide 15

Derived Variables in Predictive Analysis

Creating derived variables in Predictive Analysis

numbers representing differences between dates, such as 'age' defined, for example, as currentDate() - DateOfBirth.

7. Specify variable derivation in the Formula box by double-clicking items

from the Objects and Functions and Operators lists.

You can use the following source data:

• Dimensions from the universe corresponding to the selected subject

area

• Measures from the universe

• Membership in a set associated with the selected subject area, if the

subject area is set-based

• Date functions that return the current date or current month, for

example, based on the application server clock

Combine or transform date functions using operators, functions and aggregations to arrive at a derived variable definition.

There are two types of measures from the standpoint of derived variable definition: measures with prompts and measures without prompts. Measures without prompts are used 'as is.' An example is a measure representing a numeric field of a dimension table, like 'age.' The other kind of measure is a time-based aggregate, such as 'sum of revenue in the past 30 days' or 'time (sum of days) since last order.' In these cases, additional information is required to resolve the value:

• Null value substitution constant

• Aggregation type

• Starting date

• Ending date

Note:

After inserting a measure that represents a time-based aggregate into the formula area, enter appropriate values for the parameters just before the closing angle bracket (">") for the measure definition in the formula area.

To use membership in a population for a derived variables definition, you must use the isinpopulation() function with the Population Name parameter.

8. To add a specific day to the formula, click the icon next to Dates.

9. Click Save.

16 BusinessObjects Predictive Analysis User Guide

Derived Variables in Predictive Analysis

Creating a binning in Predictive Analysis

Binning is a way of compressing all values for a variable into a smaller number by subdividing the range of possible values into groupings or "bins".

1. In Predictive Analysis > Derived Variables, select a variable subject

area from the dropdown list.

2. Select the variable on which you want to create the binning.

3. Click Add.

4. Select New Binning.

5. In the Create Binning panel, type a name for the variable in the Binning

name box. By default the variable name is used, however it is best to use a unique name.

6. To allow others to use this variable activate Public.

7. Select the Variable type.

8. Set the number of bins.

9. Select a Calculation option:

• If you are binning based on numeric variables, the options are:

• Equal count for each bin

The option creates bins in which the number of records in the specified working population that fall into each bin is approximately the same ("binning into quartiles").

• Equal width for each bin

The option creates bins whose value range is the same width, for example, a binning with five year intervals.

• If you are binning based on Nominal variable, the options are:

• Equal count for each bin

The option is the same as that for numeric variables, except that there is no assumed ordering in which values are grouped.

• Equal number of categories

The option arranges for each bin to group the same number of distinct values. For example, a binning for State of Residence can have each bin contain the same number of states. For Nominal variable, choose a small number of bins, for example, two, and then create additional bins via Edit.

BusinessObjects Predictive Analysis User Guide 17

Derived Variables in Predictive Analysis

Editing derived variables and binning in Predictive Analysis

10. Select the population that you want to use to define the initial bin

definitions.

The reference population provides quick feedback on the number of records that fall into specific bins. After the bin definitions are saved to a binning, the population is no longer retained.

Note:

You can change the view of the Available Population list.

11. Click OK.

Related Topics

• What is binning? on page 118

• The Populations list in Predictive Analysis on page 12

Editing derived variables and binning in Predictive Analysis

1. In Predictive Analysis > Derived Variables, select a variable subject

area from the dropdown list.

2. Select the item you want to edit.

3. Click Edit.

• If you are editing a variable, the Edit Variable panel matches the Create

Variable panel and workflow.

• If you are editing a binning, the Edit Binning panel matches the Create

Binning panel and workflow.

Related Topics

• Creating derived variables in Predictive Analysis on page 15

• Creating a binning in Predictive Analysis on page 17

Deleting derived variables and binning in Predictive Analysis

1. In Predictive Analysis > Derived Variables, select a variable subject

area from the dropdown list.

2. Select the item you want to remove.

18 BusinessObjects Predictive Analysis User Guide

Deleting derived variables and binning in Predictive Analysis

3. Click Remove, then OK to confirm.

Derived Variables in Predictive Analysis

BusinessObjects Predictive Analysis User Guide 19

Derived Variables in Predictive Analysis

Deleting derived variables and binning in Predictive Analysis

20 BusinessObjects Predictive Analysis User Guide

Predictive Analysis models

Adding a model in Predictive Analysis

Once you have defined the population and optionally any derived variables, you need to build a model.

Note:

To create a model, you must have a population; however derived variables are not required.

Related Topics

• Adding a population in Predictive Analysis on page 10

• Creating derived variables in Predictive Analysis on page 15

Adding a model in Predictive Analysis

1. In Predictive Analysis > Models, select a model subject area from the

dropdown list.

2. Click Add.

The Create Model wizard panel appears.

3. In the Population step, select a population from the Available Populations

list.

Note:

You can change the view of the Available Population list.

4. Click Next.

5. In the Influencers step, select objects by doing one of the following:

• Double-click an object.

• Highlight an object and click the double-arrow button.

When an object moves to the Selected objects list, the data and binning types appear.

6. Select the data and binning types from the lists.

7. Click Next.

8. In the Goals step, select objects by doing one of the following:

• Double-click an object.

• Highlight an object and click the double-arrow button.

9. Click Next.

10. In the Attributes step, type a name for the new model.

11. To make the model available for use by others, activate Public.

22 BusinessObjects Predictive Analysis User Guide

Predictive Analysis models

Adding a model in Predictive Analysis

12. Set the Refresh type:

• Activate With Set to precipitate a model refresh whenever the set

being used in the associated population definition is refreshed.

• Activate Independent to have a refresh that is scheduled

independently of set refresh - for example, through Dashboard Builder Scheduler or a Performance Manager rule.

13. Set the Refresh scope:

• Activate Refresh Statistics only to associate the refresh of most

statistics and metrics with the model whenever the model is refreshed.

• Activate Regenerate Model to allow the refresh the statistics, as well

as the regeneration of the optimal binnings and computation the formulas used in generating model scores. This option is used usually with the Individual List analytic. A few statistics are also calculated only at the time of model regeneration - Net Relevance, in particular.

Note:

Regenerate Model requires much more computation time than Refresh Statistics only, because it requires multiple passes through the data.

14. Click Finish.

Related Topics

• Predictive Analysis model data types on page 121

• The Populations list in Predictive Analysis on page 12

• What is binning? on page 118

Predictive Analysis model data types

Objects influence how the data appears in the model.

Source data typeSources

Desktop Intelligence Objects

Numeric

BusinessObjects Predictive Analysis User Guide 23

Influence data type

Continuous, Ordinal

Goal data type

N/ANominalCharacter

Continuous, N/A

Predictive Analysis models

Copying a model in Predictive Analysis

Source data typeSources

Numeric

Continuous data type

The continuous data type is the default data type corresponding to numeric variables. Do not use this type for variables that represent numeric codes rather than actual numbers (for example, zip code); treat such variables as nominal variables.

Nominal data type

A nominal data type has a value that is not inherently ordered, for example gender. All character objects from a BusinessObjects universe are treated as nominal.

Influence data type

Continuous, Ordinal

Goal data type

N/AN/ADate

BooleanNominalBooleanDerived Variable

N/ANominalCharacter

Continuous, N/A

N/AN/ADate

Ordinal data type

Ordinal variables are ordered, but lack proportionality, as in the example of an undefined distance between adjacent values.

Related Topics

• What is a Boolean? on page 118

Copying a model in Predictive Analysis

1. In Predictive Analysis > Models, select a model subject area from the

dropdown list.

2. Select the model you want to copy.

24 BusinessObjects Predictive Analysis User Guide

Predictive Analysis models

Editing a model in Predictive Analysis

3. Click Copy.

A copy of the model appears in the list.

Editing a model in Predictive Analysis

1. In Predictive Analysis > Models, select a model subject area from the

dropdown list.

2. Select the model you want to edit.

3. Click Edit.

The Edit Model panel matches the Create Model panel and workflow.

Related Topics

• Adding a model in Predictive Analysis on page 22

Deleting a model in Predictive Analysis

1. In Predictive Analysis > Models, select a model subject area from the

dropdown list.

2. Select the model you want to delete.

3. Click Remove and then OK to confirm the action.

Regenerating a model in Predictive Analysis

You need to regenerate the model if you changed variables and data. This updates the model structure.

1. In Predictive Analysis > Models, select a model subject area from the

dropdown list.

2. Select the model you want to regenerate.

3. Click Regenerate Model.

BusinessObjects Predictive Analysis User Guide 25

Predictive Analysis models

Regenerating statistics in Predictive Analysis

You need to regenerate the model if you changed the data.

1. In Predictive Analysis > Models, select a model subject area from the

dropdown list.

2. Select the model whose statistics you want to regenerate.

3. Click Regenerate Statistics only.

26 BusinessObjects Predictive Analysis User Guide

Predictive Analysis model-based metrics

Adding a model-based metric in Predictive Analysis

Models create statistics, for example obsolescence. These statistics are exposed in Predictive Analysis as well as in time series analytics.

Typically, a statistic calculated by a Predictive model quantifies a relationship between an influencer and a goal variable, but it can also be a descriptive statistic that summarizes some aspect of the influencer's distribution in the population with which the model is associated, for example, the mean of Age or the max of Purchase Size in Last 30 Days.

Certain statistics can be valuable for automating model management, such as root mean squared error or goal variable obsolescence. These can be used in rules to detect when it is time to regenerate a model in statistics-only refresh mode or to trigger model regeneration when one whose refresh is unscheduled, for example, used for scoring lists, but not for statistics calculation.

Note:

You only get values from the most recent Predictive model refresh.

In the Metrics page you can do the following actions on Predictive model-based metrics:

• Add

Note:

In this documentation you learn how to create a Predictive model-based metric, which is the required metric for an analysis. If you want to create generic metrics and manual entry metrics, refer to the Performance Manager documentation.

• Delete

• Refresh

• Purge

• Publish

Related Topics

• What is the root-mean-squared-error? on page 124

• What is a variable? on page 125

• The model-based metrics list in Predictive Analysis on page 40

28 BusinessObjects Predictive Analysis User Guide

Predictive Analysis model-based metrics

Adding a model-based metric in Predictive Analysis

1. In Predictive Analysis > Metrics, select a subject area from the

dropdown list.

2. Select a set from the sets universe drop-down list.

Note:

The option is available only for the subjects containing sets.

3. Click Add.

4. Select New Model-based Metric from the Add sub-menu, the Create

Metric wizard appears.

5. In the Selection step, choose a model.

6. Optionally, select an aggregation function. The available functions depend

on the model you select.

7. To select an influencer for a model, activate Select influencer.

8. To select a binning for the model, activate Select binning and choose a

binning from the list.

Note:

The option is not available if no bins have been defined under derived variables and used in the model.

9. Click Next.

10. In the Attributes step, type a name for the metric.

11. Type a description for the metric.

12. Select the Calculation interval.

The calculation interval provides the date condition for the metric. The duration of the time period that makes up the metric is called the metric's grain.

Select a grain from the list. The list is created in the installation setup (see the Dashboard and Analytics Setup documentation for information on setting up the grain).

13. Select a Refresh type:

• Select With model so that the metric is refreshed when its model is

refreshed.

• Select Independent to allow only manual refresh of the metric.

BusinessObjects Predictive Analysis User Guide 29

Predictive Analysis model-based metrics

Adding a model-based metric in Predictive Analysis

14. Select the Storage options:

• Select Store all metric values to use the entire hstory of the metric

for trending purposes. Since metric values require little storage space, the option is recommended.

• Select Store last metric value only to store only the last metric value.

• Select Disable so that your model is not dependant on the last metric

value.

15. In the Parameters section, select a default smooth option.

The Default Smoothing list allows you to specify the statistical transformation used in metric reporting when the Default Smoothing selection is activated in the Smoothing list.

The nature of the data you are probing and which questions you hope to answer determine the type of smoothing that is most suitable. The administrator determines during installation and configuration the available transformations.

16. Select one of the following from the Trend is good when list:

• Increasing

Select Increasing if an increasing trend is good.

• Decreasing

Select Decreasing if a decreasing trend is good.

• On-Target

Select On-Target if an on target trend is good.

17. Select an owner for the model. You can choose from any name that is in

the Central Management Console.

18. Click Finish.

Aggregation functions for model-based metrics

Aggregation functions perform a calculation on a set of values to return a single value. The following tables illustrate the relationship between the data type and aggregation.

30 BusinessObjects Predictive Analysis User Guide

Predictive Analysis model-based metrics

Adding a model-based metric in Predictive Analysis

Non-Numeric Data TypeInfluencer Level

Importance

Obsolescence

Net Relevance

Measures usefulness of Influencer for "predicting" goal values

If > 5, optimal binning can be obsolete (regeneration needed)

• Advanced metric calculated only at model regeneration

time (Measures predictive power net of "stronger" Influencers)

• Measures predictive power net of "stronger" Influencers

%Missing, %Other, %OutOfRangeData Quality

What about % Failed and Root Mean Squared Error?

Numeric Data TypeInfluencer Level

Importance

Obsolescence

Net Relevance (regeneration time only)

Measures usefulness of Influencer for "predicting" goal values

If > 5, optimal binning can be obsolete (regeneration needed)

Measures predictive power net of "stronger" Influencers

Descriptive Statistics

Min, max, mean, variance, standard deviation

%Missing, %OutOfRangeData Quality

BusinessObjects Predictive Analysis User Guide 31

Predictive Analysis model-based metrics

Adding a model-based metric in Predictive Analysis

Goal-Based - Influence

Goal-Based - Distributional

Non-Goal-Based - Distributional

Numeric GoalsInfluencer Bin-Level

Bin Importance

Signed Bin Importance

Goal Divergence

Signed Goal Divergence

Weighted Goal Divergence

Goal Rank

Mean Goal Value

Mean Goal Ratio

Goal Variance and Standard Deviation

Count

Frequency (% distribution)

Mean (Influencer) Value

Binary Goals

Bin Importance

Signed Bin Importance

Goal Rank

Goal Rate

Goal Ratio

Count

Frequency (% distribution)

Bin Mean (for numeric Influencers)

Goal Level

Alternative Model Quality Metric:

• Importance metric for the "prediction" or "score" variable

• Good choice for general-purpose model quality metric

Model Quality Metrics only

32 BusinessObjects Predictive Analysis User Guide

Predictive Analysis model-based metrics

Deleting model-based metrics in Predictive Analysis

• Root Mean Squared Error (RMSE)

Obsolescence Detection

• Classification Rate (binary goals)

• R2 (continuous goals)

Deleting model-based metrics in Predictive Analysis

1. In Predictive Analysis > Metrics, select a metric group from the

dropdown list.

2. Select from the list the metric you want to delete.

3. Click Remove, then OK to confirm.

Refreshing model-based metrics in Predictive Analysis

1. In Predictive Analysis > Metrics, select a metric group from the

dropdown list.

2. Select from the list the metric you want to refresh.

3. Click Refresh.

A panel appears warning you that the operation can be long in execution.

4. Click OK to proceed.

Purge a model-based metric in Predictive Analysis

If you have refreshed a selected metric at least once, you can purge the metric history.

You need to purge a metric when you want to empty the data in the metric history section and delete the metric data from the ci_probe_value table in the Performance Manager repository.

1. In Predictive Analysis > Metrics, select a metric group from the

dropdown list.

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Publishing model-based metrics in Predictive Analysis

2. Select from the list the metric you want to purge.

3. Click Purge.

4. In the Metric Purge panel, select one of the following:

• Purge all periods

Select this option to purge all of the periods in the metric.

• Select previous periods to purge

Select this option to select a specific period, and then specify the number of periods to purge in the number box. Periods are defined by the calendar you selected when you created the metric.

5. Click OK.

Publishing model-based metrics in Predictive Analysis

1. In Predictive Analysis > Metrics, select a metric group from the

dropdown list.

2. Select from the list the metric you want to publish.

3. Click Publish.

The Goal & metric publishing wizard appears.

Goal & Metric Publishing Wizard

Once you create a goal, use the "Goal & Metric Publishing Wizard" to distribute the goal to individuals or groups. Goals are published for the following reasons:

• so that the people are focused on the right goals

• to enable collaboration via discussion threads and previous actions

• to recommend actions that link to goal performance, other reports and

actions

The "Goal & Metric Publishing Wizard" has the following components to help you publish a goal or metric:

• Strategy

• Who

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Publishing model-based metrics in Predictive Analysis

• What

• When

• Actions

• Summary

The Strategy step of the Goal & Metric Publishing Wizard

• Select strategy in which you want to create the role.

Continue to Who.

Related Topics

• The Who step of the Goal & Metric Publishing Wizard on page 35

The Who step of the Goal & Metric Publishing Wizard

1. Select a reporting role for yourself.

For example, you are Vice President of both Engineering and Testing, and want to send information only to Testing. You select the reporting role for Testing.

Do one of the following:

• Select a reporting group from the list.

• Click Select another role. In the "Reporting Role" panel, select the

group in which the role exists, choose a reporting role from the list, and click OK.

2. Select the people who can see the information:

• If you select People in my team, then select from the following options:

• If you selectEverybody in my team, members in the team are

displayed, along with the link Who are they that lists the members and their titles.

• If you select All my direct reports, all of your direct reports are

displayed, along with the link Who are they that lists these people and their titles.

• If you select Specific people in the team, a sub-panel appears

in which you can select recipients by team roles or individuals and click Add to add them to the "Selected members" panel. The link Who are they lists the selected individuals, those within the team roles, and their titles.

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Publishing model-based metrics in Predictive Analysis

• My manager

• If you select Other people, then select from the following:

• Org. Chart Roles - list of roles that you have already created.

• Individuals - list of all individuals

• Other Roles - list of other roles within your organization.

Note:

When you select a list, a list box allows you to select to see the list by entire company or within a specific group.

3. Click Next.

Related Topics

• The What panel of the Goal & Metric Publishing Wizard on page 36

The What panel of the Goal & Metric Publishing Wizard

The "What" step enables you to specify the goal slices you want to publish. Slices of a dimension can be secured.

Note:

If you publish a goal based on a secured sliced metric, by default the publication follows respects user security rules and contains the metric permissions.

1. Select the object that you want to send:

• If you select Goal, select the goal you want to publish from the scroll

list.

Note:

The scroll list can be populated with all of the goal slices or by specific goal groups in the list. If a goal seems to be missing, click Refresh. You can view the analytic for the goal by clicking View Goal.

• If you select Metric, select the metric you want to publish from the

scroll list.

Note:

The scroll list can be populated with all of the metrics or by specific metric groups listed in the list. If a metric seems to be missing, click Refresh. You can view the analytic for the metric by clicking "View Metric".

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Publishing model-based metrics in Predictive Analysis

2. Click Next.

Related Topics

• The When panel of the Goal & Metric Publishing Wizard on page 37

The When panel of the Goal & Metric Publishing Wizard

"Scheduled Distribution Information" indicates the schedule on which the metric or goal's metric is evaluated.

Click Next.

Related Topics

• The Actions step of the Goal & metric publishing wizard on page 37

The Actions step of the Goal & metric publishing wizard

1. Select the actions that you want to recommend to the recipients by clicking

one and then Ctrl+click to select others.

2. Clicking Add.

3. Select an action and click Edit Action and edit the parameters of the

action. Your action selection determines the default Recommended Action text.

Check Inventory

Create a new publication

Custom Action

The default text is...If you selected...

Call VendorCall Vendor

Click <a href="inventory.html" target="In

ventory">Here</a> to check product inventory

Create a <a href="../sb/flowwiz

frame.jsp?isclose=Y&contentType=3" tar get="Strategy Builder">new publication</a>

Type next step text here.

Note:

You can use the action to activate an action based upon the status of a sliced or non-sliced goal.

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Investigate related document

The default text is...If you selected...

Investigate <a href="defineurl.html" tar

get="new"> document</a>

Note:

You can pass a slice to a Desktop Intelligence document using the action. To do this, edit the URL to specify the prompt and a slice variable, either $SLICE_NAME$ or $SLICE_CODE$. For example: &nbPrompts=1&Region=$SLICE_NAME$

Investigate related TY-LY Analysis

Schedule Meeting with Concerned Parties

Send Email

Note:

Investigate <a href="definetyly.html" tar

get="new"> TY-LY Analytic</a>

<a href="Outlook:Calendar"> Schedule

Meeting</a> with concerned parties

Note:

If the default text is used, Schedule Meeting appears as a link that opens the recipient's Outlook calendar when clicked.

<a href="mailto:?subject= $strate gy.Name$">Send Email</a> to concerned parties

You can activate an action based on the status of a sliced or non-sliced goal.

4. To personalize the Recommended Action text, change the text manually

or click Personalize.

In the Personalize panel, select attributes by Strategy, Role, and User, and click OK.

5. Select a Help Insert Link if you want to insert a link:

• To a document, for example a vendor call sheet for the Vendor Call

action. Select a link from the All Documents list or from a Sub folders document list.

• To a web page, for example to a sales vendor site. Type the:

• URL

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To personalize the text, click Personalize. In the Personalize panel, select attributes by strategy, role, and user, and click OK.

• Link text

• Target

6. Click Insert Link, then OK.

7. The actions are executed in sequential order. To change the order of the

actions, use Move Up and Move Down.

8. On each action, set the recommendation action:

• If the action must go with any status, select Always.

• If the action must go only when the status is a specific color, select

Only when status is and the color of the status.

9. Click Next.

Related Topics

• The Summary panel of the Goal & metric publishing wizard on page 39

The Summary panel of the Goal & metric publishing wizard

The "Summary" panel shows the settings you have chosen for the goal or metric that you are publishing.

1. To change the strategy, click the selection and the wizard returns to the

"Strategy" panel.

2. The name of the report defaults to the name of the metric or goal that you

selected. To change this name, edit the text in the "Name" box.

3. Optionally, type a description to go with the goal or metric.

4. Select the priority.

5. To modify the recipients, click the selection and the wizard returns to the

"Who" panel.

6. To change the document shown, click the selection and the wizard returns

to the "What" panel.

7. To modify the advanced options, click Advanced.

In the "Advanced Options" panel, you have the following options:

• Select Send email notification to inform the goal recipients when the

metric is refreshed and that there is a "Goal Activity" in their "My Goals" inbox.

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The model-based metrics list in Predictive Analysis

• To modify the amount of flow and goal activities kept in the recipient's

"My Goals" inbox, change the setting in the "Quantity kept" box. This setting is for the "Flow Subscription" documents.

For example, if the quantity kept is two, the last two goal activities, triggered by the last two metric refreshes, are always kept for the recipient. Subsequent refreshes delete the oldest entry of the two previous goal activities.

• Activate Share actions for all recipients if you want to share actions.

Otherwise the default is Maintain as individual.

All goal subscriptions can have some actions associated with them. When actions are shared between the recipients then they can view the actions taken by the other recipients.

Shared actions are good for team-oriented tasks, and the recipients can divide the actions among themselves.

Maintain as individual implies that a recipient cannot view the actions taken by the other recipients. Individual actions are tasks that a goal recipient completes independently.

Click OK.

8. Click Create Publication.

Related Topics

• The Strategy step of the Goal & Metric Publishing Wizard on page 35

• The Who step of the Goal & Metric Publishing Wizard on page 35

• The What panel of the Goal & Metric Publishing Wizard on page 36

The model-based metrics list in Predictive Analysis

You can view the model-based metrics list in the following ways:

• Flat list

Select this option to see a flat list of the metrics.

• By Measures

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The model-based metrics list in Predictive Analysis

Select this option to see the list of metrics by the measures on which they are based.

• By Sets

Select this option to see the list of populations by sets.

Note:

This option is only available if the metric is based on a set universe.

• Show Groups

Select this option to see the list by set groups.

• By subsets

Select this option to see the list by subsets, for example by joiner or leaver subsets.

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The model-based metrics list in Predictive Analysis

42 BusinessObjects Predictive Analysis User Guide

Predictive analytics

Selecting a predictive model to analyze in a Predictive analytic

1. In Dashboard Builder > Create New Analytic, select an analytic the

"Predictive Analytics" category.

2. Select the universe on which the model exists.

3. Select the model.

Related Topics

• Model Gains Chart analytic based on predictive models on page 104

• Key Influencers analytic based on predictive models on page 52

• Influencer Gains Chart analytic based on predictive models on page 51

• Influencer Detail analytic based on predictive models on page 48

• Goal-based Influencer Detail analytic based on predictive models on

page 45

Predictive analytic samples in the Analytics Catalog

The analytics included in Predictive Analysis samples in the Analytic Catalog are based on the following analytics:

• Influencer Analytics

• Goal-Based Influencer Detail

• Influencer Detail

• Influencer Gains Chart

• Key Influencers

• Model Gains Chart

• Variable Profile Box Plot

• Lists and Forecasts

• Metric Forecaster

• Individual List

For information on working with these analytics, see the Predictive Analysis documentation.

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Goal-based Influencer Detail analytic based on predictive models

Related Topics

• Goal-based Influencer Detail analytic based on predictive models on

page 45

• Individual List based on predictive models on page 47

• Influencer Detail analytic based on predictive models on page 48

• Influencer Gains Chart analytic based on predictive models on page 51

• Key Influencers analytic based on predictive models on page 52

• Metric Forecaster analytic based on metrics on page 54

• Model Gains Chart analytic based on predictive models on page 104

• Variable Profile Box Plot based on predictive models on page 106

Goal-based Influencer Detail analytic based on predictive models

The Goal-based influencer detail analytic in Predictive Analysis sheds light on the precise nature of the relationship of a variable with a key outcome or goal.

The Goal-based influencer detail analytic provides:

• a histogram display of statistics calculated by the same modeling engine

that supports the Key influencers analytic.

• a detailed view of the relationship between a single influencer variable

and a single outcome or goal.

You use goal-based influencer detail when you want to:

• identify "hot spots" (areas with unusually high or low statistic values) in

the relationship between a variable and an outcome measure or indicator.

• profile variables that show up as important in Key influencers, in terms

of descriptive statistics by bins.

With goal-based influencer detail, you can answer questions like:

• Where are the key breaks in the Employee Tenure variable and attrition

rates are highest?

• What age groups account for the high importance of age as an influencer

of purchase frequency?

• What most influences equipment downtime and throughput? How?

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Goal-based Influencer Detail analytic based on predictive models

Note:

The Key influencers analytic (what) and the goal-based influencer detail analytic (how) answer this question.

Related Topics

• Key Influencers analytic based on predictive models on page 52

• What is an influencer variable? on page 121

Configuring the Goal-based Influencer Detail analytic

1. Select a predictive model.

2. Select the influencer and goal you want to display.

3. Select a model metric you want to display and click >>.

The metric appears in the Selected Metrics list.

4. Select the display options.

5. Click OK to save your configuration.

Related Topics

• Selecting a predictive model to analyze in a Predictive analytic on page 44

• Setting the display options in Goal-based Influencer Detail and Influencer

Detail analytics on page 50

• What is a goal? on page 119

• What is a metric? on page 123

• What is an influencer variable? on page 121

Setting the display options in Goal-based Influencer Detail and Influencer Detail analytics

1. Select the order in which you want the list sorted.

2. Select a binning option.

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Predictive analytics

Individual List based on predictive models

DescriptionOption

Base binning

Final binning for goal

Related Topics

• Goal-based Influencer Detail analytic based on predictive models on

page 45

• Influencer Detail analytic based on predictive models on page 48

• What is binning? on page 118

Select to have the final binning generated after the model is generated.

A Predictive model is based on a population, influencers and goals. Under derived variables you can define user-defined binning for influencers. For example, you can define a binning for Age by dividing Age into five ranges [0-25], [25-40], [40-60],[60-80],[80100]. This information is used during generation of a predictive model. If no user-defined binning is provided then one is generated automatically.

Note:

If you are configuring the Influencer Detail analytic you need to select the goal for which you want final binning.

Individual List based on predictive models

The Individual list analytic assembles a set of information for a group of individuals that includes model scores and derived variables.

Use individual list when you want to apply a predictive model to the members of a set, or to individuals who fulfill specific criteria for targeting, special handling, and/or integration with external systems.

As with other analytics, an Individual List refresh can be scheduled to occur at regular intervals. The list can be displayed and stored in XML format.

With individual list, you can answer questions like:

BusinessObjects Predictive Analysis User Guide 47

Predictive analytics

Influencer Detail analytic based on predictive models

• Which employees are the best candidates for senior management?

Related Topics

• What is a derived variable? on page 118

• What is a predictive model? on page 124

Configuring the Individual List analytic

1. Select a population and filter, if necessary, then click Next.

2. To select an item from the "Available objects" list, click it and then >>.

You can add extra columns, and select to display only a specific amount of rows.

3. To add a column from another model, click Add extra column, select an

object and click OK. The object appears in the "Selected objects" list.

Note:

To remove an object from the "Selected objects" list, select the object and click <<.

4. To restrict the number of rows in the Individual List, activate Only first

and enter the number of rows in the "Rows" text box or navigate to the number using the + or -.

5. Click OK.

Influencer Detail analytic based on predictive models

The Influencer detail analytic is identical to the Goal-based influencer analytic detail in that the categories ("bins") can be based on statistical optimization with respect to a key outcome or "goal." However, it is different from the Goal-based influencer detail analytic in that it profiles a variable by subrange (or bin) based on measures defined in a Business Objects universe.

The Influencer detail analytic exposes goal-based, bin-level statistics. The following deviation measures influence the analytic:

• goal deviation

• signed goal deviation

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Influencer Detail analytic based on predictive models

• weighted goal deviation

The following goal aggregates influence:

• goal rate

• mean goal value

• goal standard deviation

You can profile an influencer based on:

• measures in the Business Objects universe (Influencer Profile analytic)

• performance metrics with respect to one or more outcomes (goals)

(Influencer detail analytic)

• user-defined binning or model-derived binning, optimized for a selected

goal. Profile performance on Goal A based on subgroups optimized for Goal B.

Note:

Influencer analytics can display optimal binnings to reveal key breakpoints in influencer-goal relationships.

Related Topics

• What is a goal? on page 119

• What is binning? on page 118

• What is an influencer variable? on page 121

Configuring the Influencer Detail analytic

1. Select a predictive model.

2. Select the influencer and goal you want to display.

3. Select a model and subject metric you want to display and click >>.

The metric appears in the "Selected Metrics" list.

4. Select the display options.

Related Topics

• Selecting a predictive model to analyze in a Predictive analytic on page 44

• Setting the display options in Goal-based Influencer Detail and Influencer

Detail analytics on page 50

• What is a goal? on page 119

• What is a metric? on page 123

• What is an influencer variable? on page 121

BusinessObjects Predictive Analysis User Guide 49

Predictive analytics

Influencer Detail analytic based on predictive models

Setting the display options in Goal-based Influencer Detail and Influencer Detail analytics

1. Select the order in which you want the list sorted.

2. Select a binning option.

DescriptionOption

Base binning

Final binning for goal

Related Topics

• Goal-based Influencer Detail analytic based on predictive models on

page 45

• Influencer Detail analytic based on predictive models on page 48

• What is binning? on page 118

Select to have the final binning generated after the model is generated.

Note:

If you are configuring the Influencer Detail analytic you need to select the goal for which you want final binning.

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Predictive analytics

Influencer Gains Chart analytic based on predictive models

The Influencer gains chart analytic depicts the relationship between an influencer variable and a binary outcome or goal using a standard cumulative lift graph.

As with Influencer detail and Goal-based influencer detail, the influencer gains chart analytic profiles a variable by subrange, or bin, but it does so in a manner that takes into account both the outcome variable and bin frequency or count.

Influencer bins are placed in descending order of response rate along the horizontal axis. The model line shows the cumulative percent of signals achievable by targeting an increasing percent of the total population. Signal or response is assumed to be the least frequent value.

With the influencer gains chart, you can answer questions like:

• Is the high importance of age as an influencer of monthly email campaign

response based (a) on spikes in one or two specific age bands, (b) on the differential between two large age bands, or (c) on a gradual increase or decrease in the response rate with increasing age?

You use the influencer gains chart when you want to:

• identify "hot spots" (areas with unusually high or low statistic values) in

the relationship between a variable and an outcome measure or indicator in a manner that encompasses "percent of population accounted for."

• profile variables that show up as important in Key Influencers, in terms

of Cumulative Lift, the basis for "importance" calculation.

• identify the best subranges of a key influencer variable to use in segment

creation.

Configuring the Influencer Gains Chart analytic

1. Select a predictive model.

2. Select the influencer and goal.

3. Select a model metric and click >>.

The metric appears in the "Selected Metrics" list.

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Key Influencers analytic based on predictive models

4. Select metric options:

DescriptionOption

Final binning for a goal

Base binning

Best gain

Model gain

Related Topics

• Selecting a predictive model to analyze in a Predictive analytic on page 44

• What is an influencer variable? on page 121

• What is a predictive model? on page 124

• What is binning? on page 118

Select this option to have the final binning generated after the model is generated.

A Predictive model is based on a population, influencers and goals. Under derived variables you can define user-defined binning for influencers. For example, you can define a binning for Age by dividing Age into 5 ranges [0-25], [25-40], [4060],[60-80],[80-100]. This information is used during generation of model. If no user-defined binning is provided then one will automatically generated.

Select this option to display the best maximum lift response first for each bin.

Select this option to organize bins based on the model results.

Key Influencers analytic based on predictive models

Key influencers graphically displays the importance of different variables as an influencer, or predictor of one or more key outcomes.

Users can configure key influencers to display either a simple "variable importance" metric or a "net relevance" metric that nets out the influence of correlated variables.

You use key influencers when you want to:

• show which measures or dimensions best predict key outcomes.

• determine if the same or different variables are driving different types of

outcomes.

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Key Influencers analytic based on predictive models

The analytic exposes goal-based, influencer-level statistics. The following measures influence the analytic:

• Importance

Importance represents a measurement of how well the variable, taken alone, anticipates values of the goal. It is displayed in % of maximum possible value.

• Obsolescence

Obsolescence is an advanced user metric. It is calculated based on how much importance has changed since the last time the model was generated. Values over five suggest that the model needs to be regenerated to reoptimize the variable goal-based binning.

• Net relevance

Net relevance is an advanced metric that represents the utility of the variable for anticipating goal values, net of other influencers in the model with over-lapping effects.

It is the best metric to use for prioritization when simplifying a model. However, care must be taken, since a variable with high importance can show up as having low net relevance if it is highly correlated with another variable whose power was slightly higher in the most recent refresh (random variation in a subsequent refresh can cause two such variables to trade places).

With key influencers, you can answer questions like:

• Which attributes and behavior are most associated with high purchase

volume? Which are most associated with high risk of attrition?

• Where are the influencers of risk and revenue outcomes the same and

where are they different?

• Which dimensions or measures should I use for high-value, high-potential,

or high-risk segment creation?

• What factors determine the success of a sales professional?

• What most influences equipment downtime and throughput? How?

Note:

Key influencers (what) and the Goal-based influencer detail analytic (how) answer this question.

BusinessObjects Predictive Analysis User Guide 53

Predictive analytics

Metric Forecaster analytic based on metrics

Related Topics

• Goal-based Influencer Detail analytic based on predictive models on

page 45

Configuring the Key Influencers analytic

1. In the analytic edit panel, expand Select Model to analyze and select

the universe on which the model exists.

2. Select the predictive model from the "Selected Model" list.

If the model does not appear, click the refresh icon next to "Selected Model". The influencers and goals attributed to the model appear in the "Associated influencers" and "Selected Goal(s)" lists.

3. Select an influencer level from the "Metric to display" list.

4. In the "Display Options" section, configure the following options:

• If you do not want influencers that exceed the obsolescence of your

choice visible on the list, activate Hide influencers if obsolescence more than and type a limit.

• If you want the influencers listed in a particular order, activate Sort in

X order of X for goal X and choose parameters.

• To create a hyperlink from the legend to a document, activate Use

Legend as hyperlink to go to and browse to the document.

5. Click OK to save your changes.

Related Topics

• Key Influencers analytic based on predictive models on page 52

Metric Forecaster analytic based on metrics

The Metric forecaster analytic:

• graphically displays metric history and forecasts one or more points into

the future.

• automatically detects and adjusts for cyclicality, such as recurrent

day-of-week or day-of-month patterns.

• uses error bands and retrospective 1-point-forecasts help the user judge

forecast reliability.

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Metric Forecaster analytic based on metrics

You use metric forecaster when you want to:

• without needing any statistical knowledge, create forecast time-series

metrics multiple points into the future that are automatically updated.

• gain insight into forecast accuracy via error bands and retrospective

forecast overlay.

With the metric forecaster analytic, you can answer questions like:

• Is demand likely to reach levels that would exceed planned supply at any

end-of-month peak over the next 90 days?

• Are the sales representatives in danger of missing sales targets?

Note:

You can test all combinations of detrending method and cyclicality encoding, and select the best combination based on the following criteria:

• Eliminate models where Corr (predicted, actual) is not statistically

significant

• Select the model with smallest root mean squared error:

RMSE = sqrt(sum((forecasti-actuali)2)/N)

Keep in mind

• You need at least 18 periods to have a correct projection.

• Metric slices cannot be used with this analytic.

Related Topics

• What is a metric forecaster detrended mean? on page 103

Configuring the Metric Forecaster analytic

You can use a model-based metric for this analytic. Model-based metrics are created in Predictive Analysis. For more information see the Predictive Analysis documentation.

1. In the analytic edit panel, expand Forecast parameter and click Select

Metric.

2. Select a model-based metric.

3. To be able to select a different model-based metric each time the analytic

is refreshed, activate Add a Prompt and type prompt text you want to appear in the prompt window.

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Predictive analytics

Metric Forecaster analytic based on metrics

The prompt always defaults to the last model-based metric selected using the prompt.

4. Specify the number of forecast periods.

5. Expand Display Options and type a title for the analytic.

6. To set a hyperlink to a Central Management Server analytic from the

chart legend, activate Use Legend as hyperlink to go to and click Browse to locate the analytic.

The hyperlink text appears in the text box in an openAnalytic text string. You can edit this text string. For information on openAnalytic, see the Linking from goal, universe query and metric-based analytics section in the documentation.

7. Select an error band width:

• Select Liberal to have the error band computed as +1.96 times L2 to

-1.96 times L2.

• Select Conservative to have the error band computed as +3 L2 to -3

L2 where L2 is root mean squared error.

The error band displays the margin of error allowed.

8. Select a sampling data set to divide a population into specific parts to use

in the model generation process.

• Select Complete to use three quarters of the data for estimation and

one quarter of the data for validation.

• Select Optimized to use all of the data for estimation and the last two

thirds of the data for validation.

The default mode, "Complete", works better for smaller data sets and metrics with trends. "Optimized" mode works better for metric with cycles.

Linking from goal, universe query and metric-based analytics

Linking to multiple documents from an analytic based on a goal, metric or universe query

You can create links to multiple documents from a goal or metric-based analytic. This is particularly useful for analytic drill down.

1. How you access the options for linking analytics to other documents,

depends on whether you are creating a new analytic or editing an existing analytic.

• If you are creating an analytic, do one of the following in InfoView:

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Metric Forecaster analytic based on metrics

Go to Open > Dashboard Builder > Create New Analytic.•

• Go to Document List > New > Analytic.

• If you are editing an analytic, select the analytic, then click Edit.

2. Expand Navigation.

3. The next step depends on whether you want:

• If you want to link the title of the analytic to multiple documents, click

Multiple Links below "Use Legend/Title as hyperlink to go to".

• If you want to provide data point navigation, so that users can click a

metric result and then view a filtered document that shows other results for that specific time period, click Multiple Links below "Use data point as hyperlink to go to".

The Edit Navigation Link dialog box appears.

4. Click Add, then click Browse to select the document or, if you want to

link to a web page, type the URL into the text box.

5. You can add several links to documents here.

6. Click Update, then click OK.

The link to the multiple documents appears.

For example:

Drill Down Group Level||openAnalytic.jsp?Document Name=Drill+Down+Group+Level&RepositoryType=C&Repository Name=Feature Examples&DocumentExt=rpt&DocumentId=AWbJO JEYvvVMnbtXEQ2An0s&sContentType=OnDemand&mode=full||Total Sales.rpt||openAnalytic.jsp?DocumentName=TotalSales.rpt&Repos itoryType=C&RepositoryName=Feature Samples&Docu mentExt=rpt&DocumentId=AdLEddSQNTlDhMiLiHoD7II&sContent Type=OnDemand&mode=full||Business Objects||http://www.busi nessobjects.com

In this example the multiple link is created to:

• Drill Down Group Level

• TotalSales report

• www.businessobjects.com

The syntax of the links is as follows:

name1||URL||name2||URL||name3||URL

7. To check the links, click OK.

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The analytic appears.

8. Place your cursor on a data point or the title.

If the links are defined correctly, a popup menu appears with list of destination documents.

9. Click the destination document of your choice.

Note:

When linking from goal and predictive analytics you must enter the link manually.

Related Topics

• OpenAnalytic variables used in goal, metric and universe query analytics

on page 60

• Using variables for dynamic links to documents on page 59

Linking from a goal, metric or universe query-based analytic to a document or analytic

Adding links to analytics allows end users navigate from an analytic to other analytics or documents that provide information for further analysis.

You can include prompt parameters in links. Adding prompt parameters means that the data displayed on the target analytic or document is dynamically generated according to the values displayed on the first analytic.

1. How you access the options for linking analytics to other documents

depends on whether you are creating an analytic or editing an existing analytic:

• If you are creating an analytic, do one of the following in InfoView:

• Go to Open > Dashboard Builder > Create New Analytic.

• Go to New > Analytic.

• If you are editing an analytic, select the analytic, then click Edit.

2. Expand Navigation.

3. The next step depends on whether you want to link the title or data points

to a destination document:

• If you want to link the title of the analytic to multiple documents, click

Browse below "Use Legend/Title as hyperlink to go to".

• If you want to provide data point navigation, so that users can click a

metric result to view a filtered document that shows other results for

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that specific time period, click the Browse below "Use data point as hyperlink to go to".

The "Edit Navigation Link Menu" dialog box appears.

4. Click Add, then Browse to select the document or, if you want to link to

a web page, type the URL into the text box.

Note:

When linking from goal and predictive analytics you must enter the link manually.

5. Click Update, then click OK.

The URL to the document appears in the URL box. The URL reads as follows:

openAnalytic.jsp?DocumentName=<FILE_NAME> &RepositoryType= C&RepositoryName=<REPOSITORY_NAME> &DocumentExt=<EXTN>& DocumentId=<DOCID> &sContentType=OnDemand&mode=full

6. To check the links, click OK.

The analytic appears.

7. Place your cursor on a data point or the title.

8. When your cursor turns into a hand sign, click the data point or title.

The destination document appears.

Related Topics

• OpenAnalytic variables used in goal, metric and universe query analytics

on page 60

• Linking to multiple documents from an analytic based on a goal, metric

or universe query on page 56

• Using variables for dynamic links to documents on page 59

Using variables for dynamic links to documents

You can include variables in the links to documents so that the destination document is filtered by the values for a specific metric, dimension, goal or set.

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Related Topics

• OpenAnalytic variables used in goal, metric and universe query analytics

on page 60

OpenAnalytic variables used in goal, metric and universe query analytics

The following table gives a description of each variable you can use when used in creating links between analytics.

Note:

A universe query analytic where a prompt is defined can use all of the OpenAnalytic variables.

DescriptionVariable

Metric ID$METRIC_ID$

Metric Name$METRIC_NAME$

Subject ID$SUBJECT_ID$

Subject name$SUBJECT_NAME$

Set ID$SET_ID$

Set Name$SET_NAME$

2nd Set ID (when metric based on two sets)$SET2_ID$

2nd Set Name (when metric based on two sets)$SET2_NAME$

Population ID$POPULATION_ID$

Population Name$POPULATION_NAME$

Dimension ID$DIM_ID$

Dimension Name$DIM_NAME$

2nd Dimension ID$DIM2_ID$

2nd Dimension Name$DIM2_NAME$

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DescriptionVariable

Name of goal associated with analytics$GOAL_NAME$

ID of goal associated with analytics$GOAL_ID$

Value of the slice (code)$SLICE_CODE$

Value of the slice (name)$SLICE_NAME$

Class Id from the source universe$CONDITION_ID$

Measure Name$MEASURE_NAME$

First slice of the dimension used for x-axis (QOU)$MIN_SLICE$

Last slice of the dimension used for x-axis (QOU)$MAX_SLICE$

$CURRENT_SLICE$

$CURRENT_DATE$

Current slice of the dimension used for x-axis (QOU)

Begin Date of the displayed period$BEGIN_DATE$

End Date of the displayed period$END_DATE$

Current period (corresponds to a data point in a trend)

Code for the current region displayed$MAP_CODE$

Name of current region displayed$MAP_NAME$

Long name of current region displayed$MAP_LONGNAME$

Model ID$MODEL_ID$

Influencer ID$INFLUENCER_ID$

Influencer Name$INFLUENCER_NAME$

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OpenAnalytic variables for Goal variance and Pareto analytics

Pareto analytic

You can only use the $METRIC_ID$ variable to link from a Pareto analytic.

Goal variance analytics

You can use the following variables to link from Goal Variance analytics:

• $CONDITION_ ID$

• $CONDITION_ NAME$

• $DIM_ID$

• $DIM_ NAME$

• $GOAL_ ID$

• $MEASURE_ NAME$

• $METRIC_ ID$

• $METRIC_ NAME$

• $POPULATION_ ID$

• $POPULATION_ NAME$

• $SET_ ID$

• $SET_ NAME$

• $SET2_ ID$

• $SET2_ NAME$

• $SLICE_ CODE$

• $SLICE_ NAME$

• $SUBJECT_ ID$

• $SUBJECT_ NAME$

• $USER$

Note:

A universe query analytic where a prompt is defined can use all of the OpenAnalytic variables.

Related Topics

• OpenAnalytic variables used in goal, metric and universe query analytics

on page 60

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OpenAnalytic variables for Map analytics

This table shows which variables can be used to link from Map analytics that use metrics or a universe query.

Note:

A universe query analytic where a prompt is defined can use all of the OpenAnalytic variables.

Variable

$SUBJECT_ NAME$

$POPULATION_ ID$

Map analytic using metrics

Title link

Map item link

Map analytic using a universe query

Map item linkTitle link

XXXX$METRIC_ ID$

XXXX$METRIC_ NAME$

XXXX$SUBJECT_ ID$

XXXX

XXXX$SET_ID$

XXXX$SET_ NAME$

XXXX$SET2_ID$

XXXX$SET2_ NAME$

XXXX

$POPULATION_ NAME$

XXXX

XXXX$DIM_ID$

XXXX$DIM_NAME$

X$GOAL_NAME$

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Variable

$CONDITION_ NAME$

$MEASURE_ NAME$

Map analytic using metrics

Title link

Map item link

Map analytic using a universe query

Map item linkTitle link

XX$GOAL_ID$

XXX$SLICE_CODE$

XXX$SLICE_NAME$

XXXX$CONDITION_ ID$

XXXX

XX$BEGIN_DATE$

XX$END_DATE$

XXX$MAP_CODE$

$MAP_ LONGNAME$

Related Topics

• OpenAnalytic variables used in goal, metric and universe query analytics

on page 60

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XXXX$MAP_NAME$

XXXX$USER$

Predictive analytics

Metric Forecaster analytic based on metrics

OpenAnalytic variables for Metric List and Strategy Map analytics

This table shows which variables can be used to link from Metric Lists and Strategy Maps analytics (only when based on goals, metrics or universe queries).

Strategy MapMetric List

Variable

Attached analyticTitle link

XX$METRIC_NAME$

XX$SUBJECT_ID$

XX$SUBJECT_ NAME$

XX$SET_ID$

XX$SET_NAME$

XX$SET2_ID$

XX$SET2_NAME$

XX$POPULATION_ ID$

XX$POPULATION_ NAME$

XX$DIM_ID$

XX$DIM_NAME$

XX$GOAL_NAME$

XX$SLICE_CODE$

XX$SLICE_NAME$

XX$CONDITION_ID$

XX$CONDITION_ NAME$

XX$MEASURE_ NAME$

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Variable

Note:

A universe query analytic where a prompt is defined can use all of the OpenAnalytic variables.

Related Topics

• OpenAnalytic variables used in goal, metric and universe query analytics

on page 60

OpenAnalytic variables for Metric Tree and Metrics Overview analytics

This table shows which variables can be used to link from Metric Trees and Metrics Overview analytics.

Note:

A universe query analytic where a prompt is defined can use all of the OpenAnalytic variables.

Strategy MapMetric List

Attached analyticTitle link

XX$USER$

Variable

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Attached Analytic

X$METRIC_ID$

Metrics OverviewMetric Tree

Set Name Link

Datapoint Link

XX$METRIC_NAME$

XX$SUBJECT_ ID$

XXX$SUBJECT_ NAME$

XXX$SET_ID$

XXX$SET_NAME$

Predictive analytics

Metric Forecaster analytic based on metrics

Metrics OverviewMetric Tree

Variable

Attached Analytic

X$GOAL_NAME$

X$GOAL_ID$

Set Name Link

Datapoint Link

XX$POPULATION _ID$

XX$POPULATION _NAME$

XX$DIM_ID$

XX$DIM_NAME$

XX$SLICE_ CODE$

XX$SLICE_ NAME$

XX$CONDITION _ID$

XX$CONDITION _NAME$

XX$MEASURE _NAME$

X$BEGIN_DATE$

X$END_DATE$

X$CURRENT _DATE$

XXX$USER$

Related Topics

• OpenAnalytic variables used in goal, metric and universe query analytics

on page 60

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OpenAnalytic variables for most volatile metrics and Set Membership analytics

This table shows which variables can be used to link from most volatile metrics and set membership analytics.

Variable

Set MembershipMost Volatile Metrics

DatapointDatapointAnalytic Link

XX$METRIC_ID$

XX$METRIC_NAME$

XXX$SUBJECT_ID$

XXX$SUBJECT_NAME$

XXX$SET_ID$

XXX$SET_NAME$

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XX$SET2_ID$

XX$SET2_NAME$

XX$POPULATION_ID$

XX$POPULATION_NAME$

XX$DIM_ID$

XX$DIM_NAME$

XX$SLICE_CODE$

XX$SLICE_NAME$

XX$CONDITION_ID$

XX$CONDITION_NAME$

XX$MEASURE_NAME$

Predictive analytics

Metric Forecaster analytic based on metrics

Set MembershipMost Volatile Metrics

Variable

DatapointDatapointAnalytic Link

XX$BEGIN_DATE$

XX$END_DATE$

XX$CURRENT_DATE$

XXX$USER$

Note:

A universe query analytic where a prompt is defined can use all of the OpenAnalytic variables.

Related Topics

• OpenAnalytic variables used in goal, metric and universe query analytics

on page 60

OpenAnalytic variables for Key Influencers analytics

The following variables can be used to link from Key Influencers analytics:

• $MODEL_ID$

• $INFLUENCER_ID$

• $INFLUENCER_NAME$

Note:

A universe query analytic where a prompt is defined can use all of the OpenAnalytic variables.

Related Topics

• OpenAnalytic variables used in goal, metric and universe query analytics

on page 60

OpenAnalytic variables for the Metric Forecaster analytic

The following variables can be used to link to Metric Forecaster analytic:

• $METRIC_ ID$

• $BEGIN_ DATE$

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• $END_ DATE$

OpenAnalytic variables for the Interactive Metric Trend analytic

This table shows the variables can be used to link from Interactive Metric Trends based on goals, metrics or universe queries.

DatapointLegendVariable

XX$METRIC_ID$

XX$METRIC_NAME$

XX$SUBJECT_ ID$

XX$SUBJECT_ NAME$

XX$SET_ID$

XX$SET_NAME$

XX$SET2_ID$

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XX$SET2_NAME$

XX$POPULATION_ ID$

XX$POPULATION_ NAME$

XX$DIM_ID$

XX$DIM_ NAME$

XX$SLICE_ CODE$

XX$SLICE_ NAME$

XX$CONDITION_ ID$

XX$CONDITION_ NAME$

XX$MEASURE_ NAME$

Predictive analytics

Metric Forecaster analytic based on metrics

DatapointLegendVariable

XX$MIN_ SLICE$

XX$MAX_ SLICE$

X$CURRENT_ SLICE$

XX$USER$

XX$BEGIN_DATE$

X$CURRENT_DATE$

XX$END_DATE$

Note:

A universe query analytic where a prompt is defined can use all of the OpenAnalytic variables.

Related Topics

• OpenAnalytic variables used in goal, metric and universe query analytics

on page 60

OpenAnalytic variables for a Migration analytic

The following variables can be used to link from a datapoint in the Migration analytic for set analysis:

• $SUBJECT_ID$

• $SUBJECT_ NAME$

• $SET_ID$

• $SET_NAME$

• $USER$

Note:

A universe query analytic where a prompt is defined can use all of the OpenAnalytic variables.

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Related Topics

• OpenAnalytic variables used in goal, metric and universe query analytics

on page 60

OpenAnalytic variables for Gauge analytics

This table shows which variables can be used to link from Gauges.

Universe queryMetricVariable

X$METRIC_ ID$

X$METRIC_ NAME$

X$SUBJECT_ ID$

X$SUBJECT_ NAME$

X$SET_ID$

X$SET_NAME$

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X$SET2_ID$

X$SET2_NAME$

X$POPULATION _ID$

X$POPULATION _NAME$

XX$DIM_ID$

XX$DIM_NAME$

X$DIM2_ID$

X$DIM2_NAME$

X$GOAL_NAME$

X$GOAL_ID$

X$SLICE_ CODE$

Predictive analytics

Metric Forecaster analytic based on metrics

Universe queryMetricVariable

XX$SLICE_ NAME$

X$CONDITION _ID$

X$CONDITION _NAME$

XX$MEASURE _NAME$

X$MIN_SLICE$

X$MAX_SLICE$

X$CURRENT _SLICE$

X$BEGIN_DATE$

X$END_DATE$

X$CURRENT _DATE$

X$USER$

OpenAnalytic variables for the Pie, Bubble and Radar Charts

The following table shows which variables can be used to link from the Pie, Bubble and Radar charts:

Radar ChartBubble ChartPie ChartVariable

XX$METRIC_ ID$

XX$METRIC_ NAME$

X$GOAL_ ID$

X$GOAL_ NAME$

XXX$BEGIN_ DATE$

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Radar ChartBubble ChartPie ChartVariable

XXX$END_ DATE$

$CURRENT_ DATE$

Note:

A universe query analytic where a prompt is defined can use all of the OpenAnalytic variables.

Related Topics

• OpenAnalytic variables used in goal, metric and universe query analytics

on page 60

XXX

OpenAnalytic variables for Data Exploration analytics

The following variables can be used to link from the Data Exploration analytics, which include Lag Plot, Frequency Histogram, Normal Probability Plot, Smoothing and Curve Fitting :

• $METRIC_ID$

• $METRIC_NAME$

• $BEGIN_DATE$

• $END_DATE$

• $CURRENT_DATE$

Note:

A universe query analytic where a prompt is defined can use all of the OpenAnalytic variables.

Related Topics

• OpenAnalytic variables used in goal, metric and universe query analytics

on page 60

nbPrompts

nbPrompts is a parameter used in customized links between analytics and documents stored on the Central Management Server in which prompts are allowed, including analytics, Crystal Reports, Desktop Intelligence and Web

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Intelligence documents. Using nbPrompts, you can fill the prompt(s) in the target document with openAnalytic variables.

nbPrompts syntax in links from goal and metric-based analytics

The following are examples of nbPrompts syntax:

openanalytic.jsp?...&mode=full&nbPrompts=1 &[analytic_prompt_name]=$METRIC_NAME$

openanalytic.jsp?...&mode=full&nbPrompts=3 &[analytic_prompt1]=$METRIC_NAME$&[analytic_prompt2]= $BEGIN_DATE$&[analytic_prompt3]=$END_DATE$

where openanalytic.jsp?...&mode=full is automatically included in the address when you select the target document, and you add the remaining text using the following syntax:

&nbPrompts=[#]&[analytic_prompt_name]= $METRIC_NAME$

where you replace [#] with the number of prompts, and for each prompt you add the string &[analytic_prompt_name]=$METRIC_NAME$, where [analytic_prompt_name] is replaced with the name of the prompt in the analytic or document.

Using openDocument in dashboards and analytics

The openDocument.jsp uses the "opendoc" context to open objects from a document or dashboard in InfoView.

Use the following syntax:

/OpenDocument/opendoc/ openDocument.jsp?iDocID=vscmMCXk &sIDType=CUID&sType=null&sInstance=Last

where vscmMCXk is the ID of the dashboard.

Note:

Use relative instead of absolute links to documents in InfoView, in the case where you plan to migrate servers.

To use openDocument to open a dashboard from a document or dashboard:

/OpenDocument/opendoc/openDocument.jsp? iDocID=vscmMCXk&sIDType=CUID&sType=null

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To use openDocument to open an analytic from a document or dashboard:

/OpenDocument/opendoc/openDocument.jsp? iDocID=vscmMCXk&sIDType=CUID&sType=null

Example: sInstance in openDocument syntax

Use the parameter sInstance=Last with the OpenDocument function to retrieve the last instance of a Web Intelligence or Desktop Intelligence report. You must specify the fully-qualified URL of the target document for the parameter to take effect.

Note:

sInstance=Last has no effect in the OpenAnalytic function.

URL Reporting using openDocument

URL reporting using openDocument provides URL access to multiple document types by passing a URL string to a BusinessObjects Enterprise server. openDocument provides commands to control how reports are generated and displayed.

You can use openDocument in BusinessObjects Enterprise to create cross-system links to and from the following document types:

• .wid: Web Intelligence documents

• .rep: Desktop Intelligence documents

• .rpt: Crystal reports

• .car: OLAP Intelligence reports

Structuring an openDocument URL

The next sections explain how to use the openDocument function, and how to construct the URL.

An openDocument URL is generally structured as follows:

http://<servername>:<port>/OpenDocument/opendoc/<platformSpe cific>?<parameter1>&<parameter2>&...&<parameterN>

The exact syntax of the <platformSpecific> parameter depends on your implementation:

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• For Java implementations, use openDocument.jsp in place of the

<platformSpecific> parameter.

• For .NET implementations, use opendocument.aspx in place of the

<platformSpecific> parameter.

The URL is constructed using the parameters listed in openDocument

parameter overview on page 79

Joining parameters

Join parameters with the ampersand (&). Do not place spaces around the ampersand. For example: sType=wid&sDocName=Sales2003

The ampersand is always required between parameters.

Spaces and special characters in parameter values

Because some browsers cannot interpret spaces, the parameters of the link cannot contain spaces or other special characters that require URL encoding. To avoid the misinterpretation of special characters, you can define a URLEncoded string in the source database to replace the special character with an escape sequence. This will allow the database to ignore the special character and correctly interpret the parameter value. Note that certain RDBMS have functions that allow you to replace one special character with another.

By creating an escape sequence for the plus sign (+), you can instruct the database to interpret the plus sign as a space. In this case, a document title Sales Report for 2003 would be specified in the DocName parameter as:

&sDocName=Sales+Report+for+2003&

This syntax prevents the database from misinterpreting the spaces in the title.

Trailing spaces in parameter values

Trim trailing spaces at the end of parameter values and prompt names. Do not replace them with a plus sign (+). The viewer may not know whether to interpret the plus sign (+) as part of the prompt name or as a space. For example, if the prompt name displays:

Select a City:_

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(where _ represents a space), enter the following text in the link:

lsSSelect+a+City:=Paris

where the spaces within the prompt name are replaced with the plus sign, and the trailing space is trimmed off.

For details on prompt parameters of the link, refer to openDocument

parameter overview on page 79 .

Capitalization

All of the openDocument parameters are case sensitive.

Link length limit

The encoded URL cannot exceed 2083 total characters.

Parameter values in links to sub-reports

You cannot pass parameter values to a sub-report of a target Crystal report.

Using the lsS parameter with OLAP Intelligence reports

If the target document is an OLAP Intelligence report (.car) you can use the IsS parameter to specify prompts. The parameters are passed in as a URL-encoded string using the unique name of the parameter set up in the OLAP Intelligence report.

Example: Opening a report to a specific page

If 23CAA3C1-8DBB-4CF3-BA%2CB8%2CD7%2CF0%2C68%2CEF%2C9C%2C6F is the URL-encoded unique name for the page parameter in the OLAP Intelligence report, you would use the following URL to open the OLAP Intelligence report to page 2:

http://<servername>:<port>/OpenDocument/<platformSpecif ic>?sType=car&sIDType=InfoObject&iDocID=440&lsS23CAA3C1-8DBB4CF3-BA%2CB8%2CD7%2CF0%2C68%2CEF%2C9C%2C6F=2

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Example: Opening a cube parameter

If 8401682C-9B1D-4850-8B%2C5E%2CD9%2C1F%2C20%2CF8%2C1%2C62 is the URL-encoded unique name for the cube parameter opening the warehouse cube in the catalogue FoodMart 2000 on MSAS, you would use the following URL to open this cube parameter:

http://<servername>:<port>/OpenDocument/<platformSpecif ic>?sType=car&sIDType=InfoObject&lsS8401682C-9B1D-48508B%2C5E%2CD9%2C1F%2C20%2CF8%2C1%2C62=CATALOG%3DFood Mart%202000,CUBE%3Dwarehouse&iDocID=616

Using the lsM parameter with OLAP Intelligence reports

If the target document is an OLAP Intelligence report (.car) you can use the IsM parameter to specify prompts. The parameters are passed in as a URL-encoded string using the unique name of the parameter set up in the OLAP Intelligence report.

As was the case for the lsS parameter, lsM parameters are also passed in as a URL-encoded string using the unique name of the parameter set up in the OLAP Intelligence report.

Example: Opening a report

http://<servername>:<port>/OpenDocument/<platformSpecif ic>?sType=car&sIDType=InfoObject&lsMADC216EA-D9A5-42B5AE%2C21%2C84%2CA9%2CF9%2C6E%2C31%2C7=[%5BCustomers%5D.%5BCoun try%5D.%26%5BMexico%5D],[%5BCustomers%5D.%5BCoun try%5D.%26%5BCanada%5D]&iDocID=544

This is a memberset parameter opening up a report with Customers > Country > Mexico and Customers > Country > Canada in the view.

openDocument parameter overview

This section provides a brief overview of openDocument and includes a list of available parameters. Details about the available parameters, their specific uses, and relevant examples are also provided.

Note:

The document containing the openDocument link is called the parent document, and it resides on the parent system. The document to which the link points is called the target document, and it resides on the target system.

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Table 6-11: Platform Parameters

DescriptionParameter

iDocID on page 83

sDocName on page 84

sIDType on page 84

sKind on page 85

sPath on page 85

sType on page 86

token on page 86

Table 6-12: Input Parameters

Document identifier.

Document name.

Crystal object type.

The file type of target Desktop Intelligence document.

The name of the folder and subfolder containing the target document.

The file type of target document or report.

A valid logon token for the current CMS session.

DescriptionParameter

Specifies a contextual prompt if there is an

lsC on page 88

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ambiguity during SQL generation (Business Objects and Web Intelligence documents only).

Predictive analytics

Metric Forecaster analytic based on metrics

DescriptionParameter

lsM[NAME] on page 89

lsR[NAME] on page 90

lsS[NAME] on page 91

sInstance on page 92

sPartContext on page 92

sRefresh on page 93

sReportMode on page 94

Specifies multiple values for a prompt, [NAME] is the text of the prompt

Specifies a range of values for a prompt,

[NAME] is the text of the prompt.

Specifies a value for a single prompt. [NAME] is the text of the prompt.

Indicates which specific instance of the target report to open.

In Crystal Reports, a report part is associated to a data context.

Indicates whether a refresh should be forced when the target document or report is opened.

For Crystal targets only, indicates whether the link should open the full target report or just the report part specified in sReportPart.

sReportName on page 94

sReportPart on page 95

Indicates which report to open if target document is multi-report.

Indicates which specific part of the target report to open.

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Table 6-13: Output Parameters

DescriptionParameter

NAII on page 96

sOutputFormat on page 97

sViewer on page 97

sWindow on page 98

Forces the display of the prompt selection page.

Indicates the format in which the target document is opened.

Indicates the selected report viewer (CR & CA only).

Indicates whether the target report will open in the current browser window or whether a new window will be launched.

openDocument platform parameters

This section details the openDocument platform parameters that are available and gives examples on how to use the commands.

An openDocument URL is generally structured as follows:

http://<servername>:<port>/OpenDocument/opendoc/<platformSpe cific>?<parameter1>&<parameter2>&...&<parameterN>

The exact syntax of the <platformSpecific> parameter depends on your implementation:

• For Java implementations, use openDocument.jsp in place of the

<platformSpecific> parameter.

• For .NET implementations, use opendocument.aspx in place of the

<platformSpecific> parameter.

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Note:

Variables are denoted with angle brackets. You must substitute the proper value for these variables. For example, you must use the name of your server in place of <servername> where it is contained in the code samples below, and you must use your port number in place of <port>.

iDocID

ValuesMandatory?DescriptionSyntax

iDocID

Yes*Document identifier.

Note:

*One of sDocName or iDocID is mandatory.

Example:

http://<servername>:<port>/OpenDocument/opendoc/<platformSpe cific>?sType=wid&sDocName=SalesReport&iDocID=2010

Note:

To obtain the document ID, navigate to the document within the Central Management Console (CMC). The properties page for the document contains the document ID and the CUID. Use this value for the iDocID parameter.

Document identifier (InfoObjectID).

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sDocName

Document name without extension

ValuesMandatory?DescriptionSyntax

sDocName

sIDType

If multiple documents have the same name, specify the correct document with iDocID.

Note:

*One of sDocName or iDocID is mandatory.

Example:

http://<servername>:<port>/OpenDocument/opendoc/<platformSpe cific>?sPath=[Sales+Reports]&sDocName=Sales+in+200

Document name.Yes*

sIDType

ValuesMandatory?DescriptionSyntax

• CUID

Central Management Server (CMS) object identifier type.

Yes*

• GUID

• RUID

• ParentID

• InfoObjectID (de-

fault)

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Note:

*Only mandatory if the target is a Crystal report or OLAP Intelligence report (sType=rpt or sType=car) in an Object Package. Otherwise, use sPath and

sDocName.

Example:

http://<servername>:<port>/OpenDocument/opendoc/<platformSpe cific>?iDocID=2010&sIDType=CUID

sKind

ValuesMandatory?DescriptionSyntax

sKind

sPath

The file type of target Desktop Intelligence document.

Note:

*Only mandatory if the target is a Desktop Intelligence document. Otherwise, use sType.

Example:

http://<servername>:<port>/OpenDocument/opendoc/<platformSpe cific>?sKind=FullClient

Yes*

• FullClient

sPath

ValuesMandatory?DescriptionSyntax

The name of the folder and subfolder containing the target document.

Yes*

Folder and/or subfolder:

[folder],[subfolder]

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Note:

*Only mandatory if a value is specified for sDocName and is not unique.

Example:

http://<servername>:<port>/OpenDocument/opendoc/<platformSpe cific>?sPath=[Sales+Reports]&sDocName=Sales+in+2005

sType

ValuesMandatory?DescriptionSyntax

•

wid

sType

token

Note:

The file type of target document or report.

Yes

•

rpt

•

car

This parameter is ignored for agnostic documents.

Example:

http://<servername>:<port>/OpenDocument/opendoc/<platformSpe cific>?sType=wid

token

ValuesMandatory?DescriptionSyntax

A valid logon token for the current CMS session.

The logon token for the current CMS session.

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Example:

http://<servername>:<port>/OpenDocument/opendoc/<platformSpe cific>?sType=wid&sDocName=Sales+in+2003&token=<logonToken>

openDocument input parameters

This section details the openDocument input parameters that are available and gives examples on how to use the commands.

An openDocument URL is generally structured as follows:

http://<servername>:<port>/OpenDocument/opendoc/<platformSpe cific>?<parameter1>&<parameter2>&...&<parameterN>

The exact syntax of the <platformSpecific> parameter depends on your implementation:

• For Java implementations, use openDocument.jsp in place of the

<platformSpecific> parameter.

• For .NET implementations, use opendocument.aspx in place of the

<platformSpecific> parameter.

Note:

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lsC

Specifies a contextual prompt if there is an ambiguity during SQL generation (Business Objects

lsC

Example:

http://<servername>:<port>/OpenDocument/opendoc/<platformSpe cific>?sType=wid&sDocName=SalesReport&iDocID=2010&lsC=Sales

and Web Intelligence documents only).

Note:

Not supported by OLAP Intelligence

ValuesMandatory?DescriptionSyntax

A prompt value that resolves the ambiguity in the SQL generation.

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lsM[NAME]

Specifies multiple values for a prompt. [NAME] is the text of the prompt.

Predictive analytics

Metric Forecaster analytic based on metrics

ValuesMandatory?DescriptionSyntax

• Multiple prompt val-

ues, separated by a comma. If the target is a Crystal report, each value must be enclosed in square brackets. If the target is a OLAP Intelligence report, use the MDX WITH clause (refer to

Using the lsS parameter with OLAP Intelligence reports on

page 78 and Using

the lsM parameter with OLAP Intelligence reports on

page 79).

• no_value (only for

optional parameters)

Note:

You can remove an optional parameter from the prompt by setting it to

no_value in the openDocument query string. If you leave an optional

parameter out of the openDocument query string, a default parameter value will be applied.

Example:

http://<servername>:<port>/OpenDocument/opendoc/<platformSpe cific>?sType=rpt&sDocName=SalesReport&lsMSe lect+Cities=[Paris],[London]

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lsR[NAME]

Specifies a range of values for a prompt. [NAME] is the text of the prompt.

lsR[NAME]

Note:

Not supported by OLAP Intelligence

ValuesMandatory?DescriptionSyntax

• A range of values for

the prompt, separated by a double period (..). If the target is a Crystal report, the range must be enclosed in square brackets and/or parentheses (use a square bracket next to a value to include it in the range, and parentheses to exclude it).

• no_value (only for

optional parameters)

Note:

You can remove an optional parameter from the prompt by setting it to

no_value in the openDocument query string. If you leave an optional

parameter out of the openDocument query string, a default parameter value will be applied.

Example:

http://<servername>:<port>/OpenDocument/opendoc/<platformSpe cific>?sType=rpt&sDocName=SalesReport&lsRTime+Peri od:=[2000..2004)

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lsS[NAME]

Specifies a value for a single prompt. [NAME] is the text of the prompt.

Predictive analytics

Metric Forecaster analytic based on metrics

ValuesMandatory?DescriptionSyntax

• A single prompt value

(refer to Using the lsS

parameter with OLAP Intelligence reports on

page 78 and Using

the lsM parameter with OLAP Intelligence reports on

page 79).

• no_value (only for

optional parameters)

Note:

You can remove an optional parameter from the prompt by setting it to

no_value in the openDocument query string. If you leave an optional

parameter out of the openDocument query string, a default parameter value will be applied.

Example:

http://<servername>:<port>/OpenDocument/opendoc/<platformSpe cific>?sType=wid&sDocName=SalesReport&iDocID=2010&lsSSe lect+a+City=Paris

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sInstance

Indicates which specific

sInstance

Note:

Use this parameter in combination with sDocName.

instance of the target report to open.

ValuesMandatory?DescriptionSyntax

•

User (Link to latest in-

stance owned by current user)

•

Last (Link to latest in-

stance for report)

•

Param (Link to latest

instance of report with matching parameter values)

Example:

http://<servername>:<port>/OpenDocument/opendoc/<platformSpe cific>?sPath=[Sales+Reports]&sDocName=Sales+in+2003&sReport Part=Part1&sInstance=User

sPartContext

In Crystal Reports, a re-

sPartContext

Note:

*Only mandatory if a value is specified for sReportPart.

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port part is associated to a data context.

Yes*

ValuesMandatory?DescriptionSyntax

Data context of the report part.

sRefresh

Predictive analytics

Metric Forecaster analytic based on metrics

Example:

http://<servername>:<port>/OpenDocument/opendoc/<platformSpe cific>?sPath=[Sales+Reports]&sDocName=Sales+in+2005&sReport Part=Part1&sPartContext=0-4-0

Note:

The sReportPart and sPartContext parameters are supported with the regular DHML viewer (sViewer=html). Instead of the first page of the report, the DHTML viewer displays the page and context containing the part with the part highlighted.

sRefresh

ValuesMandatory?DescriptionSyntax

•

Y (forces the docu-

Indicates whether a refresh should be forced when the target document or report is opened.

ment’s refresh)

•

N (note that the re-

fresh on open feature overrides this value)

Example:

http://<servername>:<port>/OpenDocument/opendoc/<platformSpe cific>?sType=wid&sDocName=SalesReport&iDocID=2010&sRefresh=Y

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sReportMode

For Crystal targets only, indicates whether the link

sReportMode

Note:

Defaults to Full if this parameter is not specified. Only applies if a value is specified for sReportPart.

Example:

http://<servername>:<port>/OpenDocument/opendoc/<platformSpe cific>?sPath=[Sales+Reports]&sDocName=Sales+in+2003&sReport Part=Part1&sReportMode=Part

should open the full target report or just the report part specified in sRe

portPart.

ValuesMandatory?DescriptionSyntax

•

Full

•

Part

sReportName

Indicates which report to

sReportName

Note:

Defaults to the first report if this parameter is not specified.

94 BusinessObjects Predictive Analysis User Guide

open if target document is multi-report.

ValuesMandatory?DescriptionSyntax

Report name for Web Intelligence documents, sub-report for Crystal Reports, pages for OLAP Intelligence reports.

sReportPart

Predictive analytics

Metric Forecaster analytic based on metrics

Example:

http://<servername>:<port>/OpenDocument/opendoc/<platformSpe cific>?sType=wid&sDocName=Sales+in+2003&sReportName=First+Re port+Tab

sReportPart

ValuesMandatory?DescriptionSyntax

Indicates which specific part of the target report to open.

Example:

http://<servername>:<port>/OpenDocument/opendoc/<platformSpe cific>?sPath=[Sales+Reports]&sDocName=Sales+in+2003&sReport Part=Part1

Name of the report part.No

Note:

openDocument output parameters

This section details the openDocument output parameters that are available and gives examples on how to use the commands.

An openDocument URL is generally structured as follows:

http://<servername>:<port>/OpenDocument/opendoc/<platformSpe cific>?<parameter1>&<parameter2>&...&<parameterN>

The exact syntax of the <platformSpecific> parameter depends on your implementation:

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• For Java implementations, use openDocument.jsp in place of the

<platformSpecific> parameter.

• For .NET implementations, use opendocument.aspx in place of the

<platformSpecific> parameter.

Note:

NAII

Forces the display of the prompt selection page.

NAII

Note:

Only supported by Web Intelligence documents.

ValuesMandatory?DescriptionSyntax

•

Y (all prompts whose values are passed with lsS, lsM or lsR are pre-selected)

•

N (displays only the prompts whose values passed with lsS, lsM or lsR)

Example:

http:/<servername>:<port>/OpenDocument/opendoc/<platformSpe cific>?sType=wid&sDocName=SalesReport&iDocID=2010&NAII=Y

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sOutputFormat

Predictive analytics

Metric Forecaster analytic based on metrics

ValuesMandatory?DescriptionSyntax

sOutputFormat

Note:

Defaults to HTML if this parameter is not specified.

Example:

http://<servername>:<port>/OpenDocument/opendoc/<platformSpe cific>?sPath=[Sales+Reports]&sDocName=Sales+in+2003&sOutput Format=E

sViewer

Indicates the format in which the target document is opened.

Indicates the viewer that is used to view the document.

• H (HTML)

• P (PDF)

• E (Excel)

• W (Word)

ValuesMandatory?DescriptionSyntax

• html

• part (Crystal reports

only)

• actx (Crystal reports

only)

• java (Crystal reports

only)

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Example:

http://<servername>:<port>/OpenDocument/opendoc/<platformSpe cific>?sPath=[Sales+Reports]&sDocName=Sales+in+2003&sView er=html

Example:

Note:

In order to use parameters in the URL with the ActiveX viewer, :connect must be appended to the URL, followed by the parameters.

http://<servername>:<port>/OpenDocument/opendoc/<platformSpe cific>?sPath=[Sales+Reports]&sDocName=Sales+in+2003&sViewer=ac tx:connect&IsMCountry=[Thailand],[Norway]

sWindow

ValuesMandatory?DescriptionSyntax

Indicates whether the target report will open in

sWindow

the current browser window or whether a new window will be launched.

Example:

http://<servername>:<port>/OpenDocument/opendoc/<platformSpe cific>?sType=wid&sDocName=SalesReport&iDocID=2010&sWindow=New

Contextual report linking

The openDocument feature allows you to create contextual links between Crystal reports, OLAP Intelligence reports and Web Intelligence documents. To do this, you construct a URL using the openDocument syntax and then insert the URL into a Crystal report, OLAP Intelligence report or Web Intelligence document.

98 BusinessObjects Predictive Analysis User Guide

•

Same (current browser

window)

•

New (new browser

window is launched)

Predictive analytics

Metric Forecaster analytic based on metrics

Contextual report linking allows report designers to specify associations for documents residing in either a Crystal Reports environment (unmanaged) or a BusinessObjects Enterprise environment (managed). Once these associations are created, users follow the resulting navigational paths embedded in the linked documents.

This feature enables you to invoke Business Objects and Web Intelligence documents from Crystal Reports and vice versa. This feature relies on functionality that allows the user to do the following:

• Link Web Intelligence or Business Objects documents within the document

domain.

• Link report objects in Crystal Reports.

Click the appropriate link to jump to that section:

•

To insert a link into a Crystal report on page 99

•

To create a link to another report or document from an OLAP Intelligence report on page 100

•

Creating links in Web Intelligence documents on page 101

To insert a link into a Crystal report

You can use openDocument to create hyperlinks in Crystal Reports. To create a link to another report or document, use the Hyperlink Tab of the field Format Editor.

1. Open the source report in Crystal Reports.

2. Right-click the field in which you want to insert the openDocument link

and select Format from the shortcut menu.

3. In the Format Editor, select the Hyperlink tab.

4. Select A website on the Internet.

5. In the “Hyperlink information? area, leave the Website Address field

empty and click the Format Formula Editor button.

6. Enter the openDocument link in the following format:

"http://[openDocument parameters]"+{Article_lookup.Fami ly_name}

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Where [openDocument parameters] are described in openDocument

parameter overview on page 79, and the {Article_lookup.Family_name}

enables the report to pass context-dependent data.

Note:

Test your link in a browser window before inserting it into a report or document.

7. Click Save and Close to leave the Formula Workshop.

8. Click OK in the Formula Editor to save the link.

To create a link to another report or document from an OLAP Intelligence report

You can use openDocument to create hyperlinks in OLAP Intelligence reports.

1. Open the source report in the OLAP Intelligence designer.

2. On the Tools menu, select Action Manager .

3. Click New to create a new action.

4. Enter an action name.

5. Select the area to which the action (the link) will apply.

6. Enter the openDocument link using the parameters and syntax described

in this document.

Tip:

Test your link in a browser window before inserting it into a report or document.

7. Click OK to save the link.

8. Close the Action Manager dialog box.

9. Create an Analysis Button on the source report.

10. Right-click the Analysis Button.

11. In the drop-down menu, select Properties and then Edit .

12. Select Launch an action .

13. Select the action that corresponds to the openDocument link created in

steps 3 through 6.

14. Click OK .

100 BusinessObjects Predictive Analysis User Guide

Business objects PREDICTIVE ANALYSIS 3.1 FOR WINDOWS User Manual

Specifications and Main Features

Frequently Asked Questions

User Manual

Contents

Predictive Analysis

Predictive Analysis populations

Adding a population in Predictive Analysis

Editing a population in Predictive Analysis

Removing a population in Predictive Analysis

The Populations list in Predictive Analysis

Derived Variables in Predictive Analysis

Derived variable as fact table aggregates

Creating derived variables in Predictive Analysis

Creating a binning in Predictive Analysis

Editing derived variables and binning in Predictive Analysis

Deleting derived variables and binning in Predictive Analysis

Predictive Analysis models

Adding a model in Predictive Analysis

Predictive Analysis model data types

Copying a model in Predictive Analysis

Editing a model in Predictive Analysis

Deleting a model in Predictive Analysis

Regenerating a model in Predictive Analysis

Regenerating statistics in Predictive Analysis

Predictive Analysis model-based metrics

Adding a model-based metric in Predictive Analysis

Aggregation functions for model-based metrics

Goal Level

Deleting model-based metrics in Predictive Analysis

Refreshing model-based metrics in Predictive Analysis

Purge a model-based metric in Predictive Analysis

Publishing model-based metrics in Predictive Analysis

Goal & Metric Publishing Wizard

The Strategy step of the Goal & Metric Publishing Wizard

The Who step of the Goal & Metric Publishing Wizard

The What panel of the Goal & Metric Publishing Wizard

The When panel of the Goal & Metric Publishing Wizard

The Actions step of the Goal & metric publishing wizard

The Summary panel of the Goal & metric publishing wizard

The model-based metrics list in Predictive Analysis

Predictive analytics

Selecting a predictive model to analyze in a Predictive analytic

Predictive analytic samples in the Analytics Catalog

Goal-based Influencer Detail analytic based on predictive models

Configuring the Goal-based Influencer Detail analytic

Setting the display options in Goal-based Influencer Detail and Influencer Detail analytics

Individual List based on predictive models

Influencer Detail analytic based on predictive models

Configuring the Individual List analytic

Configuring the Influencer Detail analytic

Setting the display options in Goal-based Influencer Detail and Influencer Detail analytics

Influencer Gains Chart analytic based on predictive models

Configuring the Influencer Gains Chart analytic

Key Influencers analytic based on predictive models

Metric Forecaster analytic based on metrics

Configuring the Key Influencers analytic

Configuring the Metric Forecaster analytic

Linking from goal, universe query and metric-based analytics

Linking to multiple documents from an analytic based on a goal, metric or universe query

Linking from a goal, metric or universe query-based analytic to a document or analytic

Using variables for dynamic links to documents

OpenAnalytic variables used in goal, metric and universe query analytics

OpenAnalytic variables for Goal variance and Pareto analytics

OpenAnalytic variables for Map analytics

OpenAnalytic variables for Metric List and Strategy Map analytics

OpenAnalytic variables for Metric Tree and Metrics Overview analytics

OpenAnalytic variables for most volatile metrics and Set Membership analytics

OpenAnalytic variables for Key Influencers analytics

OpenAnalytic variables for the Metric Forecaster analytic

OpenAnalytic variables for the Interactive Metric Trend analytic

OpenAnalytic variables for a Migration analytic

OpenAnalytic variables for Gauge analytics

OpenAnalytic variables for the Pie, Bubble and Radar Charts

OpenAnalytic variables for Data Exploration analytics

nbPrompts

nbPrompts syntax in links from goal and metric-based analytics

Using openDocument in dashboards and analytics

URL Reporting using openDocument

Structuring an openDocument URL