Oracle Bi Sample App Content Guide

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Oracle Business Intelligence Suite Enterprise Edition (BI EE)

“Samples Sales” Content Guide (V1.3)

Oracle Business Intelligence Product Management

Apr, 2009

Contents

1. Introduction

2. Dashboards and Reports Samples

A. Dashboards Overview …........……………….. Slide 5

B. Dashboard Details …………….………….. Slide 14• 01 History and Trends .…………….………………….. 12

• 02 Tiering and Toppers .…………….………………….. 23

• 03 Data Distribution .…………….………………….. 44

• 04 Dimensional Analysis .…………….………………….. 62

C. Answers features & Segment example …………….………….. Slide 79

3. Samples Repository Overview

A. Overview ………………….…….. Slide 83

B. “How to Demo” selected RPD features ………………………... Slide 91

C. Logical Aggregations details ……………….……….. Slide 109

4. Cloning Samples with CAF V1 Utility ………………….…….. Slide 150

5. Switching/Updating Datasets ………………….…….. Slide 169

Samples Sales Content Overview

• Web Presentation : Typical reports examples

Showcasing specific Answers features

Answers formula building,

Layouts

Presentation Variables,

Filtering,

Navigations.

• Repository : Examples of Metadata constructs

Intended to demonstrate design patterns and to showcase RPD modeling best practices

Numerous logical aggregations

Time series and Rolling X Months

Variations, Time Span Variations and Compounded Variations

Multi Physical Sourcing

Dimensional Snow flaking

Canonical Time Construct (Multi LTS Facts tables)

Data security, projects, segmentation metadata, etc…

A Sample Oracle BI application and data set called “Sample

Sales“ provided to illustrate functional capabilities of Oracle BI

EE and numerous best practices

OTN External Linkhttp://www.oracle.com/technology/products/bi/enterprise-edition.html

2.A Dashboards Overview

Overview of Samples Dashboards

Content

00 – Overview

01 – History and Trends

1 - History

2a - Seasonality

2b - Seasonality. Day

2c - Seasonality Qtr

3a - Trending Comparative

3b - Trending Day

3c - Trending Qtr

4 - Trend Lines

02 – Tiering and Toppers

5 - Eighty Twenty

6 - Tiering

7 - TopNs

8 - TopNs Heatmap

9 - TopNs History

10 - TopNs Frequency

11 - N-tiling

12 - Rank Changes

03 – Data Distribution

13 - Boxplot

14 - Distribution

15 - Comparative Distribution

16 - Scatter

17 - Variability

18 - Standard Deviation

19 – Statistical Process Control (SPC)

20 – Deviants

04 – Dimensional Analysis

21- Indexing

22- Waterfall

23 - Waterfall Multidimensional

24 – Benchmark

25 – Index to Average

26 – Index to Average Trended

27 – Profile

28 – Details

Sample Sales Dashboards List

Index page listing all available Dashboards, allows quick navigation to any dashboards

Overview Page

01 History and Trends Dashboard

Yearly, quarterly, and monthly

values and averages for a given

metric

Shows pattern of monthly values by

year, over multiple years. Leverages

daily granular data from DB

Comparative monthly trend charts

for several individuals on a single

metric.

Comparative monthly trend charts for

several individuals on a single metric.

Leverages daily granular data from DB.

Shows pattern of quarterly values

by year, over multiple yearsShows pattern of monthly values by

year, over multiple years.

Comparative quarterly trend

charts for several individuals on

a single metric.

Displays trend lines and variations in

the data by automatically grouping data

points into homogeneous clusters of

data individuals

http://laliv-lap:9704/analytics/saw.dll?Go&_scid=kg3dvLXXSnw&Path=/shared/Sample%20Sales/Help%20Content/Trending%20Month%20Help&Action=Prompt&Done=Close


02 Tiering and Toppers Dashboard

Contribution measure of upper tier

of a specific population for a

metric.

Shows historical amplitude of Top N

and Bottom N value layers and gives a

visual indication of how values for

Toppers and Bottomers evolve over

time comparatively.

Historical information on Top and

Bottom N individuals, frequency of

individuals qualifying for Tops and

Bottoms groups.

Combined set of views of Top

Individuals for two distinct

dimensions on a single metric.

Includes bi-dimensional toppers

matrix.

Tiers total of a metric in clusters

(tiers) of equal values, with

descending order of individuals.

Comparative analysis of dimension

individuals for several metrics, ordered

in descending way of values of a

specific metric. 'How are my top

revenue deciles contributing to profit ?'

Shows the ranking variations on a metric

for each individual in a dimension.

Measures how Top N individuals

contribute to total aggregation of

population, for two distinct metrics.

03 Data Distribution Dashboard

Comparative standard deviation

analysis on a metric for a selected

set of population, with a selected

grain for analysis.

Graphical summary of a set of

data. Displays measures of central

median, dispersion and skewness.

Simple statistical discrete

distribution of a population for

one metric Identifies and highlights top

sequences of consecutive deviant

data points that fall outside user

selected control limits

Comparative representations of

statistical distribution for a selected

population, over a dimension. 'How

do sales order size distribute every

year ?'

Comparative graphical summary

of a set of data. For each value in

a dimension, it shows measures of

central, average, dispersion and

skewness.

Displays Top, Mid and Bottom tiles

of a population, with extreme

ranking individuals, range, IQR and

basic descriptive measurements

Automatically groups data points into

user selected number of clusters and

highlights those data points that fall

outside the control limits


04 Dimensional Analysis Dashboard

Shows trended comparative

performance for individuals from a

dimension, benchmarked against

Average of individuals in this dimension

Relative performance of individuals

in a dimension, benchmarked

against a fixed individual.

Shows how an initial value is

increased and decreased by a

series of intermediate values.

Profiles selected population by

several attributes

Tabular list of detailed records for

selected population

Indexed representation of a metric

for dimension individuals.

Shows how an initial value is

increased and decreased by a

series of intermediate values,

breaking down details of

dimension individuals for each

intermediate value.

Performance of dimension individuals

indexed against average of all

individuals in report.


2.B Dashboard Details01 History and Trends

Analyze trended information,

history and seasonality

01 – History

This report shows the monthly, quarterly and yearly historical values and averages for a given metric. The metric to be analyzed can be

chosen from the list of options available in the Select Metric dropdown.

01 – History Help

1.1 Purpose:

This report presents the value of a metric over a

selected period for a time dimension. It displays

aggregated values for each period in this interval:

total yearly, quarterly, monthly and weekly, as well

as average quarterly, monthly and weekly.

The purpose of This report is to help understand the

behavior of a given measure over time.

The charts break down the information by Year, Quarter, Month, and Week and provide a

snapshot of long term trends in the value of the measurement. It also provides an

understanding of how a given period in particular relates to the total trend, as well as how

periodical averages are impacted.

1.2 Usage:

Select the metric to analyze from Select Metric drop down list of choices at top of page and

select Go. If necessary, enter a division factor, for example, 1000 in Divide Metric Value by

prompt at top of page and select Go.

Filter the scope of the report as required by using Page Filter Prompts at bottom of page.

Clicking on the blue colored numbers within tables will give you access to more detailed

navigation reports.

…

02 – Seasonality by Month (2a), Day(2b), Quarter(2c)

This report displays several views comparing how a specific metric value is evolving month to month over multiple years. It shows

pattern of monthly values by year, for selected history. Report 2b (by day) provides ability to select the time object to analyze on.

02 – Seasonality by Month Help

1.1 Purpose:

This report displays several views to compare how a

specific metric is evolving month to month over multiple

years. It shows pattern of monthly values by year, for

selected history.

The analysis helps to understand monthly seasonality

across years and how the values balance across periods

within a year. It computes month values as a percentage

of total year value in a way facilitating comparison

between years.

It also provides a cumulative representation of the full year, indicating the overall pace to

completion of the year total. The analysis is especially useful in forecast and performance

measurement processes.

1.2 Usage:

Select the metric to analyze from Select Metric drop down list of choices at top of page and

select Go .

If necessary, enter a division factor. For example, enter 1000 in Divide Metric Value by

prompt at top of the page and select Go button.


Clicking on the blue colored numbers within tables give you access to more detailed

navigation reports.

…

02 – Seasonality : Alternate Views

2b – Seasonality accessing Day Grain data :

provides the flexibility to select the time object to analyze on.

2b – Seasonality at Quarter level

03 – Trending comparative Month (3a), Day(3b), Qter(3c)

This reports display historical representations of the selected metric, presented individually in selected dimensions. It also provides a

quick visual comparison of year on year evolution of metric values by month or quarter for distinct individuals in a dimension. Report

3b (by day) provides ability to select the time object to analyze on.

03 – Trending Help

1.1 Purpose:

This report displays historical representations of the selected

metric, presented individually in selected dimensions. It also

provides a quick visual comparison of year on year evolution

of metric values by month for distinct individuals in a

dimension. This analysis helps understand the behavior of a

given metric over time for each year, in comparison to

individual dimensions. It gives insight on how individuals of a

dimension perform differently over time, for a single metric.

The chart displays details of business issues over time which may remain unnoticed if we look

only at aggregate time value or aggregate dimensional values.

1.2 Usage:

From drop down list of choices at top of page, select a metric to analyze from Select Metric

prompt and select a dimension from Select Dimension prompt and select Go. The dimension

values will each generate a series of three charts (a row of charts on the page) . Note: This report

generates a chart for every value in the dimension selected. Choosing a dimension with a large

number of values might lead to difficulties in generating the chart and the report not working

properly.

If necessary, enter a division factor , for example 1000, in Divide Metric Value by prompt at top of

the page and select Go.


To view tables with detailed data, select the option from Select information to display : drop down.

03 – Trending Comparative : Alternate Views

3b – Trending accessing Day Grain data :

provides the flexibility to select the time object to analyze on.

3b – Trending

Quarter

Detailed table view (in all trending reports)

Shows detailed chart values in multi blocks pivot tables

04 – Trend Lines

This report identifies and displays trending lines in metric values over time. This analysis automatically groups data points into user

selected number of clusters and then determines linear trends in metric values in each cluster.

04 – Trend Lines Help

1.1 Purpose:

This report identifies and displays trending lines in metric

values over time. This analysis automatically groups data

points into user selected number of clusters and then

calculates linear trending algorithm for each cluster. A

trend line is a momentum indicator, it measures the rate of

increase or decrease in the metric value over time.

Clusters breaking can alert to any acceleration or

deceleration of the trend.

1.2 Usage:

From drop down list of choices at the top of page, select a metric to analyze from "Select

Metric" prompt and select a dimension from "Select Dimension" prompt and select Go.

If required, user may also adjust Control zone bandwidth (expressed in # of standard

deviations). The number of sigmas expressed will increase the control band above and

beyond the average line.

User may adjust No of clusters : the number of clusters will define how many breaking

points will be inferred from the detailed data.

For more detailed analysis on any particular cluster, user may select corresponding down

arrow icon in second column on table beside the bubble chart

Filter the scope of the report as required by using "Page Filter Prompts" at bottom of page.

…

2.B Dashboard Details02 Tiering and Toppers

Analysis of top and bottom rankers,

tiers, deciles, eighty-twenty paretos

and historical variations of top layers

of individuals.

05 – Eighty Twenty

This report provides several dynamic views to help measure how the upper tier of a specific population set contributes in descending order of

value. Filters at the top of the page enable users to set that percent limit of value; the report then renders the corresponding percent of

population that makes up that value.

05 – Eighty Twenty Help

1.1 Purpose:

This report provides several dynamic views to help

measure how the upper tier of a specific population set

contributes in descending order of value. The filters at the

top enable users to set that percent limit of value; the

report then renders the corresponding percent of

population that makes up that value. For example, setting

the filter to "80" marks up the records in the entire

population that make up 80% of the value.

This report provides users insight on where to focus for a particular analysis. It answers the

question "What part of the population should I focus on to be sure to address the most

significant part of the value/ problem ?“

1.2 Usage:

From drop down list of choices at top of page, select a metric to analyze from "Select Metric"

prompt and select a dimension from "Select Dimension" prompt and select Go.

Set the '%' limit to desired value on the prompt at the top of the page and hit "Go" button.


To view tables with detailed data, select the option from "Select information to display :" drop

down

…

05 – Eighty Twenty : Alternate Views

Detailed table view

Shows detailed values by buckets, in multi blocks pivot tables

06 – Tiering

This report provides a visual distribution of the population after grouping the value of a metric into a given number of tiers. It ranks individuals

in descending order and then groups them in buckets of equal value (not equal counts). The report then displays the counts per tiers and how

other metrics distribute according to this tier split up. The number of tiers in the report is dynamically set by the user.

06 – Tiering Help

1.1 Purpose:

This report provides a visual distribution of the population

after grouping the value of a metric into a given number of

tiers. It ranks individuals in descending order and then

groups them in buckets of equal value (not equal counts).

The report then displays the counts per tiers and how other

metrics distribute according to this tier split up. The number

of tiers in the report is dynamically set by the user.

This report is very useful to gather a high level idea of how a

value distributes across a population.

For example, how many customer make up first third of my revenue, vs. how many in the

second third, vs. how many in the last third. Also, how is my profit for the population that

composes the first tier of my revenue? This report will visually display answers to these

questions and can also provide with details in tabular form.

1.2 Usage:

From drop down list of choices at top of page, select a metric to analyze from "Select Metric 1"

prompt and select a second metric from "Select Metric 2" prompt.

Select a dimension from "Select Dimension" prompt and select Go.

Set the number of tiers to desired value on the prompt at the top of the page and hit "Go"

button. Filter the scope of the report as required by using "Page Filter Prompts" at bottom of

page. To view tables with detailed data, select the option from "Select information to display :"

drop down. …

06 – Tiering : Alternate Views

Detailed table view

Shows detailed values by tier, in multi blocks formatted pivot table

07 – TopNs

This report provides views of top individuals in the selected dimension along with an aggregation of all the non Top N individuals, per

distinct metrics values for selected metrics. Filtering on the reports limits it to show those individuals that belong to the top N list for at least

one of the metrics considered.

07 – TopNs Help

1.1 Purpose:

This report provides views of top individuals in the selected

dimension along with an aggregation of all the non Top N

individuals, per distinct metrics values for selected metrics.

Filtering on the reports limits it to show those individuals

that belong to the top N list for at least one of the metrics

considered.

This report is useful to identify the top performers on a

given metric, and to aggregate their importance relative to

the total population.

This dashboard helps users to understand the impact of top performers in the context of the

whole business and may contribute towards business decisions and actions on the top

individuals.

1.2 Usage:

From drop down list of choices at top of page, select a metric to analyze from "Select Metric

1" prompt and select a second metric from "Select Metric 2" prompt.

Select a dimension from "Select Dimension" prompt and select Go.

Set the 'N' limit to desired value on the prompt at the top of the page and hit "Go" button.


…

08 – TopNs Heatmap

This report uses color to represent top individuals for selected dimensions in a two-dimensional table, showing the value of a selected

metric. The heat map matrix displays all individuals in selected dimensions and the overall rank of their crossing metric value. It is color

coded so as to quickly distinguish between leader, top 10s, 20s, 30s, 40s, 50s, 60s individuals and the rest.

08 – TopNs Heatmap Help

1.1 Purpose:

This report uses color to represent top individuals for selected

dimensions in a two-dimensional table, showing the value of a selected

metric. The heat map matrix displays all individuals in selected

dimensions and the overall rank of their crossing metric value. It is color

coded so as to quickly distinguish between leader, top 10s, 20s, 30s,

40s, 50s, 60s individuals and the rest.

Heatmaps give an edge in identifying critical issues, allocating resources, finding trends or relationships

and creating quicker insight on large data sets. Trends, anomalies, distributions and relationship can be

seen in context and at multiple levels at once. The report will help to quickly identify the top performers on

a given metric, in a cross dimensional context. and to aggregate their importance relative to the total

population. It This dashboard helps users to understand the impact of top performers in the context of the

whole business and may contribute towards business decisions and actions on the top individuals. For

example, what are my top selling region - products intersections ? How is the distribution of region /

product. top sellers spread on the population, any visible correlation ?

1.2 Usage:

From drop down list of choices at the top of page, select two dimensions from "Select Dimension 1" and

"Select Dimension 2" prompts. These dimensions are used respectively as columns and rows of the

heatmap. It is important that you select two different dimensions there in order for the heatmap to be

meaningful.

Set N limit to limit the number of toppers displayed and select Go, Select the metric to analyze from

"Select Metric 1" prompt and select Go. Filter the scope of the report as required by using Page Filter

Prompts at the bottom of page. …

09 – TopNs HistoryThis page displays historical amplitude of Top N and Bottom N layers and gives a visual indication of how values for Toppers and Bottomers

evolve over time comparatively. For each month, the report indicates how much of the total value was represented by Top and Bottom Ns,

what was the floor/limit to qualify for toppers, as well as how much in percentage each group represented to the total value.

09 – TopNs History Help

1.1 Purpose:

This page displays historical amplitude of Top N and

Bottom N value layers and gives a visual indication of how

values for Toppers and Bottomers evolve over time

comparatively. For each month in the range selected, this

report indicates how much of the total value was

represented by Top and Bottom Ns, what was the

floor/limit to qualify for toppers, as well as how much in

percentage each group represented to the total value.

1.2 Usage:

From drop down list of choices at top of page, select the metric to analyze from Select

Metric 1 prompt and select Go.

From the top of the page, select Dimension 1 and Dimension 2 and select Go . From the

top of the page, select the Top and Bottom N value in the Set N (Top & Bottom) and select

Go .This value sets the maximum Rank limit for individuals to qualify for Top N and

Bottom N ranges calculations.


…

10 – TopNs Frequency

This report displays historical information about Top N and Bottom N individuals. For each month in the range selected, this analysis will

indicate which individuals were the Top or Bottom N, and, for each, how many times each of them made it in the Top or Bottom N during the

range of time selected.

10 – TopNs Frequency Help

1.1 Purpose:

This report displays historical information about Top N and

Bottom N individuals. For each month in the range

selected, this analysis will indicate which individuals were

the Top or Bottom N, and, for each, how many times each

of them made it in the Top or Bottom N during the range of

time selected.

The information on this report is useful to understand who

are the individuals that regularly make it in top or bottom N

monthly podium, over a period of many months.

There can be lower business risks with a population where names on monthly top N positions

are regularly rotating, versus a situation where the list of monthly toppers over a long period of

time is very short. Similarly Bottomers that never move out of the bottom zone are indicative of

no relative business improvements and need consideration.

1.2 Usage:

From drop down list of choices at top of page, select the metric to analyze from Select Metric

prompt and select Go.

From the top of the page, select Dimension 1 and Dimension 2 and select Go . From the top of

the page, select the Top N value in the Set N(Top & Bottom) and select Go . The value entered

here is set as the rank limit for Top and Bottom N qualifiers in the report


…

10 – TopNs Frequency : Alternate Views

Detailed records view

Shows detailed TopNs records values by rank

11 – Ntiling

This report shows how the different tiles of individuals on metric 1 value contribute to metric 2 values. It arranges individuals in ascending order

of metric 1 value and groups them into user selected number of tiles. It then displays the exact same tiles and how they relate to the metric 2

values. A color code visually indicates if contribution of individuals in a tile is relatively higher or lesser toward metric 2 than towards metric 1.

11 – Ntiling Help

1.1 Purpose:

This report shows how the different tiles of individuals on

metric 1 value contribute to metric 2 values. It arranges the

individuals in ascending order of the metric 1 value and

groups them into user selected number of tiles. It then

displays the exact same tiles and how they relate to the

metric 2 values. A color code visually indicates if contribution

of individuals in a tile is relatively greater or lesser toward

metric 2 than towards metric 1.

This analysis is useful to understand how the same set of individuals' contributions to different

metrics vary. For example, you can get answers to the following questions using this dashboard:

Are my top revenue customers making up most of my profits ? How do small customers

contribute to my costs ? Are my top costing plants making the most of my revenue ? ... This

analysis can lead to interesting conclusions such as the customers in the highest revenue decile

may not be the top most profitable customers.

1.2 Usage:

From drop down list of choices at top of page, select two metrics to analyze from "Select Metric

1" and "Select Metric 2" prompts; select the dimension and select Go . The metric 1 is the one

used for the tiling of the dimension individuals.

At the top of the page, set number of tiles (for example, 10) and select Go . This determines how

many bars display in the charts.


To view tables with detailed data, select the option from "Select information to display :" drop

down …

11 – Ntiling : Alternate Views

Detailed Table view

Shows detailed individual values by Ntiles, organized in multi-blocks pivot table

12 – Rank Changes

This report displays information on Top individuals in a dimension with a condition upon amplitude of variations in their ranking on a

measurement, from one month to another one. The filtering at the top of the report provides users with the flexibility to reduce the scope

of the analysis to only top items with a minimum variation differential over time.

12 – Rank Changes Help

1.1 Purpose:

This report displays information on Top individuals in a

dimension with a condition upon amplitude of variations in

their ranking on a measurement, from one month to another

one. The LV filtering on the report provides users with the

flexibility to reduce the scope of the analysis to only top

items with a minimum variation differential over time.

1.2 Usage:

From drop down list of choices at top of page, select metrics to analyze from Select Metric ,

Select Period Ago Metric 1 and Select Period Ago Metric 2 prompts and select Go.

From the top of the page, Select Dimension and select Go . From the top of the page, set the

Top N Limit value in the Set Top N Limit drop down and select Go .This allows users to fix the

limit of top positions they elect for displaying in the report. This filter applies to each period in

the report set. For example a limit set to 'top 5' means to show any individual that made it at

least once in the top 5, either in current month, the month before or quarter ago month. Again,

the month ago and quarter ago can be replaced by any period ago metric. The report will then

display the rank variations for this population.

From the top of the page, set the Rank Variation Limit value in the Set Rk Var Limit drop down

and select Go .This allows the user to fix the minimum absolute value for rank variations he

wants to see in the report. For example, setting this value to 3 results in items that have

increased or decreased their rank by at least 3 positions between current and last month, or

between current and quarter ago month. Any lower rank variations will not show. This limit

filtering is applied in addition to the filter rule set in the 'Set Top N Limit' top page dashboard

prompt block. …

2.B Dashboard Details03 Data Distribution

Various data distribution

representations and simple

statistical analysis

13 – Boxplot

This report displays a boxplot whisker diagram comparing the spread of detailed data point values between individuals of a dimension. It

depicts a set of values for each dimension individual through seven number summaries: smallest observation (Bottom), lower decile (10%

mark), lower quartile and upper quartile (IQR), Median and Average, upper decile (90% mark), and largest observation (Top).

13 – Boxplot Help

1.1 Purpose:

This report displays a boxplot whisker diagram comparing

the spread of detailed data point values between individuals

of a dimension. It depicts a set of values for each dimension

individual through seven number summaries: smallest

observation (Bottom), lower decile (10% mark), lower

quartile and upper quartile (IQR), Median and Average,

upper decile (90% mark), and largest observation (Top).

Boxplots are useful to display differences between populations datasets without any

assumptions of the underlying statistical distribution. The spacing between the different parts

of the box indicate the degree of dispersion (spread) and skewness in the data, and identify

outliers. A boxplot report provides users with immediate visual insight on where to focus for a

particular individual of a dimension

1.2 Usage:

From drop down list of choices at top of page, select two dimensions to do the comparative

analysis on. Dimension 1 defines the comparative individuals (X axis values on boxplot

charts). Dimension 2 defines the grain for each dispersion analysis. Use an aggregated

column for dimension 1, so not to have too many values on X axis for the charts, and a

granular column for dimension 2 (dispersion analysis requires a dataset with multiple

datapoints to render meaningful charts).

From drop down list of choices at top of page, select the values to run Boxplot analysis on,

select Go. If necessary, enter a division factor (For example, 1000) in Divide Metric Value by

prompt at top of the page and select Go. …

14 – Distribution

This report describes basic statistical discrete distribution views of a selected population. It lets the user dynamically define the number of

buckets to use for statistical distribution, as well as the grain in the population, and provides several dynamic representations of the results.

14 – Distribution Help

1.1 Purpose:

This report describes basic statistical discrete distribution

views of a selected population. It lets the user dynamically

define the number of buckets to use for statistical

distribution, as well as the grain in the population, and

provides several dynamic representations in the results.

This report is useful to understand how the individuals of a

population are distributed between the minimum and the

maximum values, and to suggest the probabilities of where

an individual may fall in a specific bucket.

The report applies to numerous business situations: distribution of order values, distribution

of call times, distribution of salaries, and so on. It allows visualizing skewness of a given

population versus typical distribution.

1.2 Usage:

From drop down list of choices at the top of page, select a metric to analyze from "Select


Set the number of bins to desired value on the prompt at the top of the page and hit "Go"

button.

If necessary, enter a division factor (For example, 1000) in "Divide Metric Value by" prompt

at top of the page and select Go. Filter the scope of the report as required by using "Page

Filter Prompts" at bottom of page. To view tables with detailed data, select the option from

"Select information to display :" drop down.…

14 – Distribution : Alternate Views

Detailed Table view

Shows drillable detailed individual values by bin

15 – Comparative Distribution

This report provides a comparative representation of simple statistical distributions, by individuals, for a selected population. It allows user to

see how a metric comparatively distributes between different categories. The report lets the user dynamically define the number of buckets

to use for statistical distribution, as well as the grain of the population.

15 – Comparative Distribution Help

1.1 Purpose:

This page provides a comparative representation of simple

statistical distributions, by individuals, for a selected

population. It allows you to see how a metric comparatively

distributes between different categories. The report lets the

user dynamically define the number of buckets to use for

statistical distribution, as well as the grain of the population.

This report highlights how the spread of metric values

distribution changes from one value of a dimension to

another, for example, from one year to another.

This is done by displaying the structural distribution changes in the population of events. For

example, the evolution of mix of order values from large to small, change from one region to

another for the distribution of call durations, salaries, and so on.

1.2 Usage:

From drop down list of choices at top of page, select two dimensions for a comparative analysis.

Dimension 1 defines the comparative individuals, how many rows of charts in the page, and

dimension 2 defines the grain for each distribution. Use an aggregated column for dimension 1 to

avoid having too many charts on the page and a granular column for dimension 2. Distribution

analysis requires a dataset with multiple data points to render meaningful charts. From drop

down list of choices at top of page, select the to run distribution analysis on, select Go.

From Set # of Bins drop down, enter the number of buckets to include in distribution charts. This

defines the grain of the intervals in the distribution analysis. A value of 10 means that the chart is

splitting individuals into 10 buckets of equal range between minimum and maximum value of the

population (# of columns in the bar chart). Select Go. …

16 – Scatter

This report provides a graphical summary of a set of data. Individual values are represented by the position of the point in the chart space.

It displays measures of central median, dispersion and skewness. It also identifies top, bottom values and interquartile range (IQR).

16 – Scatter Help

1.1 Purpose:

This report provides a graphical summary of a set of data.

Individual values are represented by the position of the

point in the chart space. It displays measures of central

median, dispersion and skewness. It also identifies top,

bottom values and interquartile range (IQR).

Scatter charts are typically used to compare distinct

values across categories and visualize metric values for

individuals in a selected dimension. It also provides an

understanding of how dispersed those values are and

where extreme values compare with the rest of the

population.

1.2 Usage:

From drop down list of choices at top of page, select a metric to analyze from "Select



…

17 – Variability

This report shows how an initial value is increased and decreased by a series of intermediate values and, details of dimension

individuals for each intermediate value. It displays the top and bottom Ntiles of the selected dimension individuals by selected metric.

17 – Variability Help

1.1 Purpose:

This report shows how an initial value is increased and

decreased by a series of intermediate values and,

details of dimension individuals for each intermediate

value. It displays the top and bottom Ntiles of the

selected dimension individuals by selected metric.

This analysis is helpful in understanding the distribution

of metric value for individuals in selected dimension.

1.2 Usage:

From drop down list of choices at top of page, select a metric to analyze from "Select


Set the number of Percentiles to display and hit "Go" button.


…

18 – Standard DeviationThis report provides a comparative analysis of standard deviation on a metric, for a selected set of dimensions. For each individual of

dimension 1, reports displays standard deviation of metric values along dimension 2 (grain). It also identifies individuals with highest variability

and highest standard deviation.

18 – Standard Deviation Help

1.1 Purpose:

This report provides a comparative analysis of standard

deviation on a metric, for a selected set of dimensions. For each

individual of dimension 1, measurement of standard deviation of

metric values along dimension 2 (grain) will be displayed. In

addition, the report also presents comparison of average and

median values, and identifies the dimension 1 individual with

highest variability and highest standard deviation.

This analysis is useful in identifying extent of variation in metric values in dimension 2 individuals

and to compare that between different dimension 1 individuals. For example, what is the

variability in customer revenue for brand X versus brand Y ? You can gain an understanding of

overall volatility of business metrics, and identify which individuals have the highest variability

(volatility) in their businesses. Variability can correlate with a level of risk in the business, or

indicate potential area for processes optimization. For example, the report can help understand

which individual has the highest diversity in the size of its orders, which accounts have the

highest variation in their balances over months... and help identify candidates for improvement

Variability indicator is a percentage of standard deviation over average value for the population.

It's expressed as a percentage. Variability = 100% means that Std Dev = Avg value of

population.

1.2 Usage:

From drop down list of choices at top of page, select two dimensions from "Select Dimension 1"

and "Select Dimension 2" prompts. Dimension 1 is represented in X axis for the charts,

Dimension 2 is the grain detail upon which Standard Deviation is calculated for each of the X

axis individuals. Select the metric to analyze from "Select Metric 1" prompt and select Go.

19 – Statistical Process Control (SPC)

This report displays a configurable Control Chart analysis (also known as the Shewhart chart or process-behavior chart). This report is used to

determine whether a business process is in a state of statistical control or not. The chart identifies most homogenous periods of data over time

and indicates if the process is within control (between Lower and upper control limit) or not. If the chart indicates that the process is not in

control, the pattern it reveals can help determine the source of variation to be eliminated to bring the process back into control.

19 – SPC Help

1.1 Purpose:

This report displays a configurable template version of a

Control Chart. The control chart, also known as the Shewhart

chart or process-behavior chart, in statistical process control is

a tool used to determine whether a manufacturing or business

process is in a state of statistical control or not. If the chart

indicates that the process is currently under control then it can

be used with confidence to predict the future performance of

the process.

If the chart indicates that the process being monitored is not in control, the pattern it reveals can

help determine the source of variation to be eliminated to bring the process back into control. On

a practical level the control chart can be seen as part of an objective disciplined approach that

facilitates the decision as to whether process performance warrants attention or not. The control

chart is one of the seven basic tools of quality control.

This analysis automatically groups data points into user selected number of clusters and then

highlights those data points that fall outside the control limits. Control Limits are determined by

user selected value for control band (no of sigmas). For each cluster, a control zone marked

with their UCL(Upper Control Limit), Average, and Lower Control Limit (LCL) (Lower Control

Limit). The boundaries of clusters are also shown by break points.

1.2 Usage:

From drop down list of choices at the top of page, select a metric to analyze from "Select Metric"



deviations). The number of sigmas expressed will increase the control band above and beyond

the average line. User may adjust No of clusters : the number of clusters will define how many

breaking points will be inferred from the detailed data. …

20 – Deviants

This report displays how metric values vary over time and highlights top sequences of consecutive deviant data points. Consecutive deviants

are two or more consecutive data points which are above and below control zone (where metric value is outside control limits of a Control

Chart). The control chart is a tool used to determine whether a manufacturing or business process is in a state of statistical control or not.

20 – Deviants Help

1.1 Purpose:

This report displays how metric values vary over time and

highlights top sequences of consecutive deviant data points.

Consecutive deviants are two or more consecutive data

points which are above and below control zone (where metric

value is outside control limits of a Control Chart). The control

chart, also known as the Shewhart chart or process-behavior

chart, in statistical process control is a tool used to determine

whether a manufacturing or business process is in a state of

statistical control or not.

Consecutive deviants in a control chart indicates that the process being monitored is not in

control, the pattern it reveals can help determine the source of variation to be eliminated to bring

the process back into control. On a practical level the control chart and deviants can be seen as

part of an objective disciplined approach that facilitates the decision as to whether process

performance warrants attention or not. The control chart is one of the seven basic tools of

quality control.

1.2 Usage:

From drop down list of choices at the top of page, select a metric to analyze from "Select Metric"


If required, user may also adjust # of top sequences by setting the prompts at the top of the

page. Increasing # of top sequences value will let the chart identify more of the top sequences. #

of top sequences set to 1 means that only the longest sequence of deviants (the one with the

most individuals) will be marked. # of top sequences set to 3 means that only the top 3 longest

sequences of deviants (the ones with the top 3 most individuals) will be marked (that could

result in more than 3 sequences).


deviations). The number of sigmas expressed will increase the control band above and beyond

the average line.

For more detailed analysis on any particular set of consecutive deviants, user may select

corresponding down arrow icon in third column on table beside the bubble chart


…

2.B Dashboard Details04 Dimensional Analysis

Dimensional analysis layouts and

techniques, and other detailed

reports

21 – Indexing

This report provides a comparison of several dimension values over a time period using indexed line charts, as opposed to absolute value line

charts. The analysis turns absolute values into indexes and makes comparison between trended values intuitive. It allows users to select a

value from the X axis of "Actual Values" chart, for example, use Month, as the index basis point. Indexed information allows users to compare

the pattern of evolution of values in a more insightful manner than when using absolute values. Regardless of how far apart the absolute

values may be from one another, indexes allow them to be represented in a framed format with comparisons making more visual sense.

21 – Indexing Help

1.1 Purpose:

This report provides a comparison of several dimension values

over a time period using indexed line charts, as opposed to

absolute value line charts,

This analysis turns absolute values into indexes and makes

comparison between trended values intuitive. It allows users to

select a value from the X axis of "Actual Values" chart, for

example, use Month, as the index basis point.

Indexed information allows users to compare the pattern of evolution of values in a more

insightful manner than when using absolute values. Regardless of how far apart the absolute

values may be from one another, indexes allow them to be represented in a framed format with

comparisons making more visual sense.

1.2 Usage:


prompt and select a dimension from Select Dimension prompt and select Go. This dimension is

displayed as lines of different colors on the chart.

From the top of the page, select Dimension 2 which is to be used for Indexed charting. The

values from this dimension form the distinct values of X axis for the charts. Select Go

From the top of the page, select a value for index base and select Go . The selected value

defines the starting base for the index (value 100).


Use the drop down in the report to switch tabular display between indexed metrics and actual

values …

22 – Waterfall

This dashboard shows how individuals in a selected dimension contribute to total value of a selected metric, cumulatively. The report uses a

waterfall chart model to show individual's metric values in the form of floating columns leading up to total value of the metric. Height of the

column is proportional to value of metric for that individual.

22 – Waterfall Help

1.1 Purpose:

This report shows how individuals in a selected dimension

contribute to total value of a selected metric, cumulatively.

The report uses a waterfall chart model to show individual's

metric values in the form of floating columns leading up to

total value of the metric. Height of the column is proportional

to value of metric for that individual.

This analysis can be used to visually compare different

individual's contribution towards the total value of metric and

immediately appreciate the gaps between contributions.

It can also be used to visually explain what happened or in the case of a forecast - what may

happen. For example, to view how the revenue fluctuated over the months and which month

contributed the most to the total revenue.

1.2 Usage:


prompt and select a dimension from Select Dimension prompt and select Go. The individuals of

the dimension selected represent the different bars in the bar chart. It is better usage to select

aggregate dimensions in this chart, as opposed to very granular dimensions.

If necessary, enter a division factor (For example, 1000) in Divide Metric Value by prompt at top

of the page and select Go.


Click on the dimension individuals in the table to navigate to more detailed report. …

23 – Waterfall Multidim

This dashboard shows how individuals of two selected dimensions contribute to total value of a selected metric, cumulatively. The report uses

a waterfall chart model showing metric values in the form of floating columns leading up to total value of the metric. X axis indicates individuals

from one dimension, while height of the bars in each column is proportional to value of metric for individuals in from the other dimension.

23 – Waterfall Multidim Help

1.1 Purpose:

This report shows how individuals of two selected

dimensions contribute to total value of a selected metric,

cumulatively. The report uses a waterfall chart model

showing metric values in the form of floating columns

leading up to total value of the metric. X axis indicates

individuals from one dimension, while height of the bars in

each column is proportional to value of metric for individuals

in from the other dimension.

This analysis can be used to visually compare different individuals contribution towards the

total value of metric and immediately appreciate the gaps between contributions. It can also be

used to visually explain what happened or in the case of a forecast - what may happen. For

example, to view how the regional revenue fluctuated over the months and which month/region

contributed the most to the total revenue.

1.2 Usage:


prompt and select the dimensions from Select Dimension 1 and Select Dimension 2 prompt

and select Go. The individuals of the Dimension 2 selected represent the different bars in the

bar chart. Within each bar, the distribution by individuals in Dimension 1 is shown by different

colors. It is better usage to select aggregate dimensions in this chart, as opposed to very

granular dimensions.


…

24 – Benchmark

This dashboard shows comparative performance of two selected metrics for individuals on a dimension, benchmarked dynamically against a

user selected individual from the dimension. The report allows users to compare performance on metrics between individuals of a dimension.

It presents intuitive view of how individuals perform relative to each other, and relative to a selected individual elected as "benchmark" value.

The chart easily points out inconsistent performance gaps.

24 – Benchmark Help

1.1 Purpose:

This report shows comparative performance of two selected

metrics for individuals on a dimension, benchmarked

dynamically against a user selected individual from the

dimension.

The report allows users to quickly compare performance on

metrics between individuals of a dimension. It presents clear

and intuitive view of how individuals perform relative to each

other, and relative to a selected individual elected as

"benchmark" value.

The chart easily points out inconsistent performance gaps. For example, setting the bench on

region A, we can quickly visualize which regions are performing above A both for revenue

and margin, or which one is better than A in revenue but worse in margin, and so on.

1.2 Usage:

From drop down list of choices at top of page, select two metrics to analyze from Select

Metric 1 and Select Metric 2 prompts and select Go.

From the top of the page, select Dimension 1 and select Go .

From the top of the page, select the bench value in the Set Bench Value drop down and

select Go . The value selected in this drop down is the one set as bench (base 100 for

indexes), to which other individuals are indexed and compared.

Filter the scope of the report as required by using Page Filter Prompts at bottom of page …

25 – Index to Avg

This dashboard shows comparative performance for individuals on a dimension, for several metrics, indexed against the average

performance of all the individuals in the report. The report compares performance on several metrics. Each individual of a dimension and the

average of the group. It provides a visual view of how individuals perform and to highlight inconsistent performance gaps.

25 – Index to Avg Help

1.1 Purpose:

This report shows comparative performance for individuals on

a dimension, for several metrics, indexed against the average

performance of all the individuals in the report.

The report compares performance on several metrics. Each

individual of a dimension and the average of the group. It

provides a visual view of how individuals perform and to

highlight inconsistent performance gaps: you can quickly

visualize which regions are performing above average both

for revenue and margin, or which one is better than average

in revenue but worse in margin, and so on.

1.2 Usage:

From drop down list of choices at top of page, select a dimension and two metrics to

analyze from., then select Go. The Dimension object defines the bars of the bar charts, and

the metrics are used to calculate the indexing positions on.


Clicking on the blue colored numbers within tables grants you access to more detailed

navigation reports.. .

26 – Index to Avg Trended

This dashboard shows trended comparative performance for individuals from a dimension, benchmarked against Average of individuals in this

dimension. The report compares each individual to average for two distinct metrics, over time. It displays relative performance to a monthly

average base index (100 every month), as well as complementary presentation that factors in evolution of average value over time.

26 – Index to Avg Trended Help

1.1 Purpose:

This report shows trended comparative performance for

individuals from a dimension, benchmarked against

Average of individuals in this dimension. The report

compares each individual to average for two distinct

metrics, over time. It displays relative performance to a

monthly average base index (100 every month), as well as

complementary presentation that factors in evolution of

average value over time.

1.2 Usage:



From the top of the page, select Dimension 1 and Dimension 2 and select Go . Filter the

scope of the report as required by using Page Filter Prompts at bottom of page.

26 – Index to Avg : Alternate Views

Detailed Table view

Shows detailed view of monthly values by dimension individuals

27 – Profile

This dashboard profiles the selected population by the selected attributes. It provides a breakdown of user selected metric 1 and metric 2 for

the individuals within selected dimensions. The line bar chart provides a visual comparison of metric 2 values versus corresponding metric 1

value for each dimension individual.

27 – Profile Help

1.1 Purpose:

This report profiles the selected population by the selected

attributes. It provides a breakdown of user selected metric 1

and metric 2 for the individuals within selected dimensions.

The line bar chart provides a visual comparison of metric 2

values versus corresponding metric 1 value for each

dimension individual.

This analysis is very useful in comparing and understanding

how the metric values performed for different business

attributes. For example, how are revenue and quantity shipped

spread across different brands, geographic locations, customer

categories and markets?

1.2 Usage:



Within the report, select Slice by dimensions from the drop down provided.

If necessary, enter a division factor (For example, 1000) in Divide Metric 1 by and/or Divide

Metric 2 by prompts at top of the page and select Go.


28 – Details

This report shows detailed attributes for a selected population.

2.C Answers features &

Segment example

Overview of Answers features used

with Sample Sales

Selected Sample Sales Answers Features 1/2

Multi Navigation : click on hyperlinked

figures and select which detail to

navigate to. Repeat process and further

navigate deeper into details

Web Variables leveraging : Change

values in top pages pink boxes, and

see how reports queries change

accordingly

Page help content :

click on help

hyperlinks to see

contextual functional

help on dashboard

you are looking at

Segmentation metadata setup

examples (from detailed reports

(navigation targets), leverage table

dynamic sorting and direct

segment/list creation link.

Pivot table level calculations to extend

aggregations levels on top of answers

columns calculations

Dynamic selection of Metric and

dimensions in the dashboards.

A single report offers multiple

functional analysis combinations.

Selected Sample Sales Answers Features 2/2

Cascading Presentation variables

dependencies, leveraging strings

datatypes

Answers level Aggregations : visit

definition of answers based metrics

with SQL based Aggregations

formulas on top of existing RPD

objects

Union clause based answers report and

charts, that leverage capability of

bringing together results of several

distinct queries

Leverage of Filter Groups structure to

allow advanced filtering in reports, as

well as leverage of presaved prompted

filters

Conditional Chart series formatting

based on value of series, to allow

better visual rendering in charts.

Range drilling : navigate from a

range of values into a detail report

for this range of values.

Sample Customer Segment 1

Customer Waterfall Segment Sample :

• counts out all customers in specific criteria showcasing Segmentation capability,

• profile dashboards navigation setup enabled,

• list export format configured.

3.A Repository Overview

Overview of features and sample

techniques included in the Sample

repository

Physical Source Overview

• Set of 11 xml

independent files,

(total size 5 Megs)

• 3 Fact tables :– Main Fact : FactsRev,

Order detail, 5000 records

and two years data history

– Additional Facts :

Forecast : 229 records,

Inventory : 7200 records

• 6 Dimension tables– Orders : 5000 records

– Customers : 240 records

(with snowflake attribute for

Segments)

– Employees : 15 records

– Products : 15 records

– Market : 15 records

– Time : Day details and

month details, <1000

records. Time dimension

dynamically sourced from

two tables depending on

context of analysis.

Facts

Metadata : Physical Layer Overview

• Two distinct unrelated

physical sources logically

joined in business model (no cross database joins in physical

layer)

• 19 aliased tables including

specific constructs :

– Canonical Time (A11, A13)

– Rolling time construct

(A22, A24, A20, A21, A31)

• Fully Aliased sources best

practice

Facts Source 1

Rolling Months Aliased Sources

Source

2

Dimensions

Metadata : Logical Layer Overview

Facts

Logical Derived Facts

Facts

• Single logical layer federating

the two distinct physical

sources

• Several logical constructs to

showcase aggregation and

calculations capabilities :

– Time series, Rolling months,

– Canonical time construct and

Time Facts table

– Logical derived fact tables

• Numbered objects best

practice architecture including

metadata descriptions fully

populated

F1, Base Aggregations :

Basic aggregations (Sum, Avg, Counts).

F2, Time Series Measures

Aggregations on Time Dimension. Variants of Period Ago and

Period-To-Date metrics like MonthAgo, QuarterAgo, YearAgo,

Month-To-Date, Quarter-To-Date, Year-To-Date etc.

F3, Rolling Time Series Measures

Rolling Aggregations on Time. Contains Rolling 3 Months and

Rolling 6 Months measures with different aggregations like Sum,

Daily Avg, Monthly Avg etc. This LTS is physically mapped to the

Revenue and Inventory fact tables, but the fact tables here join to

the time dimension table using a complex join

F4, Time Variations calculations : variance with period ago values like

Variance to Month Ago, Variance to Year Ago, Quarter-To-Date to

Quarter Ago's Quarter-To-Date variance etc.. All metrics in this table

are logical calculations based on physically mapped metrics from

Base Measures logical tables

F5, Level Based Measures: aggregations that always return value at

a particular level within a dimension. E.g.:- Revenue at Month level,

Billed Quarterly at Year level, Booked Amt for all Products etc.

F6, Other Calculations : specific logical aggregations such as

Runrates and Seasonality metrics, grain fragmentation measures.

Metrics Aggregations

–Numerous logical RPD aggregations examples• Time series, Rolling Months, Variations, Run rates, Percent of Periods, Grain Fragmentation ….

Metadata : Presentation Layer Overview

• Presentation Layer organizing all objects into Root Folders and Subfolders

• Fully fledged version of presentation layer with all logical objects, and example of reduced set presentation layer with selected renamed objects

• User objects permission restriction : these two users (demo / demo2) have distinct accesses to presentation folders

Metrics Aggregation List

Other Selected Model Best Practices

Multiple Physical Sourcing (without cross db joins)

Dimensional Snow flaking

Degenerated Facts Attributes

Dimension based aggregations

Canonical Time Construct (Multi LTS Facts tables) and Time Facts folder

Objects Numbering and Derived objects fact tables

Multiple Hierarchy paths and Hierarchy drill chaining

Segmentation Metadata

Physical Layer Aliasing, Pres Layer Layout

User level Data Constrain

Projects

Metadata dictionary

…

3.B How To Demo

Selected Repository FeaturesHow to show and visit some of the

RPD features illustrated in Sample

Sales

Selected “How to Demo” scenarios:

Selected Repository Features

Multiple Physical

Sourcing

Dimensional Snow

Flaking

Hierarchy drill Chaining

Segmentation MetadataUser Level Data ConstraintMetadata Dictionary

Dimensions Based

Aggregations

Runrate Logical

Aggregations

How to Demo : Multiple Physical Sourcing 1/2

SELECT "D0 Time"."T02 Per Name Month", "F1 Revenue"."1-

01 Revenue (Sum All)", "14 Other Measures"."6-01 Revenue

Fcst (Sum All)" FROM "Sample Sales" WHERE "D0

Time"."T02 Per Name Month" BETWEEN '2008 / 01' AND

'2008 / 12'

2. Notice that results are

showing aligned data for

revenue and forecast

information

1. Define answers query as shown to the right :

Or, alternatively, paste following SQL into “SQL

Issued” under “Advanced” tab and press

3. Show that, in RPD, both revenue and

forecast data are sourced from distinct

physical tables that have no

relationships to each other

4. Explain that, in Logical Model, both

logical facts table are defined with same

“grain” (content level tab), and logical

dimension tables are each sourced from

both physical models (Forecast Time and

Actual Time)

How to Demo : Multiple Physical Sourcing 2/2

How to Demo : Dimensional Snow flaking

SELECT "D1 Customer"."C5 Segment”, "F1 Revenue"."1-01 Revenue (Sum All)" FROM "Sample Sales"

2. Results are showing

revenue broken down

by customer segments

3. Show customer

segment physical table

joined to Customer

dimension as snowflake

structure

4. Open Logical Table

Source for Customer logical

dimension and show that is

tied to both Customer and

Segment physical sources


Or, alternatively, paste following SQL into “SQL Issued”

under “Advanced” tab and press

SELECT "D4 Product"."P01 Product", "F1 Revenue"."1-01 Revenue (Sum All)" FROM "Sample Sales"

2. Results show

revenue broken

down by Products

3. Click on any product and see jump from

Product level in product hierarchy, to “Region

Level” in Market dimensional hierarchy…

4. Then, drilling down

the whole Market

hierarchy to jump to

Manager level in

Employee dimension

5. Then from employee hierarchy, jumping to Customer, then orders details

How to Demo : Hierarchy drill chaining 1/3


Or, alternatively, paste following SQL into “SQL Issued”

under “Advanced” tab and press

6. Open RPD and visit properties of

Detailed level in Product Hierarchy. See

setup in tab “Preferred Drill Path”, where

“Total level” for “Market hierarchy” is

defined as next drill.


7. Similarly, visit properties of Detailed

level in Market Hierarchy. See setup in tab

“Preferred Drill Path” : “Total level” for

“Employee hierarchy” is defined as next

drill….

8. Full detail of how all dimensions are tied in Sample Sales applications content.


How to Demo : User level Data Constrain 1/2

1. Login as demo/demo, open “Sample Sales Reduced” subject area

in answer. Notice you have access to multiple detail folders, including

“Customer” and “Fact Others” folders.

SELECT "Other Dimensions".Region, "Other Dimensions".District, "Other Dimensions".Area, "Other Dimensions".Market, "Facts

Revenue".Revenue FROM "Sample Sales Reduced"

3. See results showing data

for several of Market records 4. Save the report (or copy above SQL

in clipboard), logoff and login back as

demo2/demo2 user. Open “Sample

Sales Reduced” subject area. Notice

that you have access to less folders

than when logged in as demo/demo




How to Demo : User level Data Constrain 2/2

SELECT "Other Dimensions".Region, "Other Dimensions".District,

"Other Dimensions".Area, "Other Dimensions".Market, "Facts

Revenue".Revenue FROM "Sample Sales Reduced"

6. See results showing much more

restricted visibility into Market records

than with user demo/demo.

7. Open RPD, visit Security Manager

(>Manage>Security) and click on demo2 user.

Then click on button and visit tab

“Filters”. There see definition of visibility

restriction clauses. These clauses can be

composed of dynamically set session variables

and will apply to all queries for this user.




How to Demo : Segmentation Metadata 1/2

1. Click on “More Products” hyperlink at top of screen and select

“Marketing” from drop down menu. Then click on “Create

a Segment” and select Customers Target Level

2. Use answers like object on left pane

to build criterias as shown on left :

-Customers with high revenue (>1000),

- who had a sharp mth to mth decrease

in revenue (>50% drop),

- who do not own product 1,2,or 3,

3. Set Counts to be

“All Counts”, then

hit button “Update

Counts” to see

results of your

counting for distinct

customers.

4. Click on the total #

of customers at the

bottom (hyperlink) to

show direct

navigation / filtering

of dashboards with

this list

How to Demo : Segmentation Metadata 2/2

5. Open RPD, visit Marketing Manager (>Manage>Marketing). See Target Levels objects

defined. Click on Customers Target Level to visit Segmentation catalog defined, and Qualified

list item (i.e. what system is counting) mapped to Customer Key object from Customer

dimension.

How to Demo : Metadata Dictionary

1. Open RPD, navigate to

>Tools>Utilities and select

option “Generate Metadata

Dictionary”2. Copy resulting folder structure in a

new folder :

metadata_dictionary\Samplesales

under existing path :

…\OracleBI\oc4j_bi\j2ee\home\applic

ations\analytics\analytics\

3. Update Instanceconfig.xml (located in …\OracleBIData\web\config\)

and extend with following tags before the end of the file :<SubjectAreaMetadata>

<DictionaryURLPrefix>/analytics/metadata_dictionary/</DictionaryURLPrefix>

</SubjectAreaMetadata>

4. Restart OBI Web server and OC4J, the Metadata dictionary button should

now show up in answers with descriptions of all metdata RPD lineage

5. Repository object descriptions and full mapping paths available on click from answers interface

How to Demo : Metadata Dictionary

How to Demo : Runrate logical aggregations 1/2

2. Filter query to a custom time range spanning over a few weeks, as shown

SELECT "F1 Revenue"."1-01 Revenue (Sum All)" , "D02 Time Facts"."T62 # of Days", "D02 Time Facts"."T63 # of

Weeks", "D02 Time Facts"."T64 # of Months", "14 Other Measures"."1-81 Revenue (Wkly RunRate)", "14 Other

Measures"."1-82 Revenue (Mthly RunRate)", "14 Other Measures"."1-83 Revenue (Qtrly RunRate)", "14 Other

Measures"."1-84 Revenue (Yrly RunRate)" FROM "Sample Sales" WHERE "D0 Time"."T00 Calendar Date" BETWEEN

date '2008-01-01' AND date '2008-02-19'

1. Create folllowing

answers query :

(paste following SQL

into “SQL Issued”

under “Advanced” tab

and press

How to Demo : Runrate logical aggregations 2/2

3. Understand the single line results :

Sum of

Revenue for the

period selected

Calculation of number of days,

weeks, months in the period

selected. These calculations are

decimals, not tied to period selection

be over a full calendar month.

Note that these objects are shown in

the query for explanation, they are

not needed in queries using runrates

aggregations.

Average revenue amount

per week over the period

selected : = Σ(revenue) /

(number of weeks)

Average revenue amount per

month over the period selected : =

Σ(revenue) / (number of months)

Average revenue amount per quarter over

the period selected : = Σ(revenue) /

(number of Quarters)

Average revenue amount per year over

the period selected : = Σ(revenue) /

(number of Years)

How to Demo : Dimensions Based aggregations 1/2

2. Visit Results : notice value of column 1-43 and T60 columns, to understand what this

value is, drill on a specific week for instance

SELECT "D0 Time"."T01 Per Name Week”, "F1 Revenue"."1-01 Revenue (Sum All)", "12 Variations"."1-43 Revenue (Fst

oT)", "D02 Time Facts"."T60 First Time Day Dt" FROM "Sample Sales" WHERE "D0 Time"."T01 Per Name Week" IN ('2007

Week 10', '2007 Week 11')

1. Create folllowing

answers query :

(paste following SQL

into “SQL Issued”

under “Advanced” tab

and press

For a given time object

present in the query, 1-43

aggregation returns the

FIRST value of the

metricin this time object. If

the object is a Week, it

returns the value for the

first day in the week only.

How to Demo : Dimensions Based aggregations 2/2

3. To show the setup for

this, open RPD, and

visit properties of

object 1-43, tab

„Aggregation‟

4. The Aggregation there

is setup as dependant

on what dimension is

invoked. When time

dimension is in the

query, the aggregation

against it will be First,

while aggregation with

any other dimensions

will be sum.

3.C Repository

Aggregations Detail

Functional details of RPD aggregationsDetail of all logical aggregations

included in Sample repository

Metrics Aggregation List

Metrics Aggregation Details : Base & Agos

01 Sum All Returns sum of data over any selected dimensions

03Count

DistinctCounts the number of distinct occurrences of a value

04Month Ago

(Mago)

Returns data for same period as of previous month vs. month of date selected in the query.

Example, Mago of revenue metric as of May 19th returns revenue value for Apr 19th of

same year.

052 Months

Ago (2Mago)

Returns data for same period as of two months before date selected in the query.

Example : 2Mago of revenue metric as of May 19th returns revenue value for Mar 19th of

same year.

063 Months

Ago (3Mago)

Returns data for same period as of three months before date selected in the query. Example

: 3Mago of revenue metric as of May 19th returns revenue value for Feb 19th of same year.

Note : aggregations 04,05 and 06 will return at least month level detail. Ie, if no time object in the report, or if only

objects higher than month, then the query will force return one row per month. Incase there are time objects in the

reports at month, week or day level, then it will break down the results to this level of detail

07Quarter Ago

(Qago)

Returns data for same period as of previous quarter vs. quarter of date selected in the

query.

Example, Qago of revenue metric as of May 19th returns revenue value for Feb 19th of

same year.

08Year Ago

(Yago)

Returns data for same period as of previous Year vs. year of date selected in the query.

Example, Yago of revenue metric as of May 19th of year N returns revenue value for May

19th of year N-1

09Week Ago

(Wago)

Returns data for same day as of previous week vs. week of date selected in the query.

Example, Wago of revenue metric as of May 19th returns revenue value for May 12th

Note : aggregations 07,08 and 08 will return at least respectively Qtr,Year or Week level month level detail. Ie, if no

time object in the report, or if time objects higher than these (respectively) month, then the query will force return one

row per each level respectively.

10 Month To Date Sum Sums all the values from the first day of the month to the selected day in the query

11Month To Date

Daily Avg

Sum of all the values from the first day of the month to the selected day in the query

divided by number of days from the start of the month to the current day

12Month Ago Month

To Date

Month Ago Month-To-Date Value for the same day in past month. For the day in the

previous month as the day selected in the query, it returns the sum of all the values

from the first day of the month to that period.

13Month Ago Year To

Date Sum

Month Ago Year-To-Date value for the same day in past month. For the same day in

the previous month as the day selected in the query, it returns the sum of all the

values from the first day of the current year to that day in the previous month.

14Quarter To Date

SumSums all the values from the first day of the quarter to the day selected in the query

15Quarter To Date

Monthly Avg

Sum of all the values from the first day of the quarter to the day selected in the query

divided by number of months (decimal) from the start of the year to the current period

16Quarter To Date

Daily Avg

Sum of all the values from the first day of the quarter to the current period divided by

number of days from the start of the quarter to the current period

17Quarter To Date

Weekly Avg

Sum of all the values from the first day of the quarter to the current period divided by

number of weeks from the start of the quarter to the current period

18

Quarter Ago

Quarter To Date

Sum

Quarter Ago Quarter-To-Date Value for the same period. For the same period in the

previous quarter, it returns the sum of all the values from the first day of the quarter to

that period.

19 Year To Date Sum Sums all the values from the first day of the year to the current period

20Year To Date

Monthly Avg

Sums of all the values from the first day of the year to the current period divided by

number of months from the start of the year to the current period

21Year Ago Year To

Date Sum

Year Ago Year-To-Date Value for the same period. For the same period in the

previous year, it returns the sum of all the values from the first day of the year to that

period.

Metrics Aggregation Details : To Dates

10. Month to Date Sum (MTD)

Definition :Sums all the values from the first day of the month to the selected day in the query.

Exp : MTD Sum of revenue for May 19th of Year N returns sum of revenue for May 1st to May 19th included

Use When :Use this object when, for a given date, you need to see the sum of the metric since the beginning of the month that this datebelongs to. This aggregation is useful when performance measurement cycles are at month level to compare metrics from one period to another one. I.e., how were we doing last month or last year at the same day in month, compared to this period.

Logical Formula :TODATE(“Base Metric”, "Sample Sales"."H0 Time"."Month“)

Limitations :This aggregation will return at least month level detail. I.e., if

no time object in the report, or if only objects higher than

month, then the query will force return one row per month.

Incase there are time objects in the reports at month, week or

day level, then it will break down the results to this level of

detail.

Year N - 1 Year NQ1 Q3 Q4 Q1 Q3 Q4

Jan Feb Mar July Aug Sep Oct Nov Dec Jan Feb Mar July Aug Sep Oct Nov Dec

Sum

Q2Q2

Apr May Jun May JunApr

11. Month To Date Daily Avg

Definition :Sum of all the values from the first day of the month to the selected day in the query divided by number of days from the start of the month to the current day

Exp : MTD Avg of revenue for May 19th of Year N returns daily Avg of revenue from May 1st to May 19th included

Use When :Use this object to view how average daily value for the whole month is stabilizing as month completes. At any given date in the month, the daily average of all past days in month will be returned. This aggregation is useful when performance measurement cycles are at month level to compare metrics from one period to another one. Ie, how were we doing last month or last year at the same day in month, compared to this period.

Logical Formula :“10 MTD Sum" /"FT1 Time Facts"."T72 MTD Distinct Days“

T72 is a time fact object that calculates the number of distinct

days since the beginning of the month for any date.

Limitations :This aggregation will return at least month level detail. Ie, if




day level, then it will break down results to this level of detail



Avg

Q2Q2


Definition :For the same period in the previous month, it returns the sum of all the values from the first day of the month to that period to the same day that is selected in the query, but for last month

Exp : Mago MTD of revenue for May 19th of Year N returns Sum of revenue for Apr 1st to Apr 19th

Use When :Use this aggregation to compare line to line what previous month value was with what current month value is, at any given day in the month. This time series allows to have both values in the same query table, as distinct columns, and hence allows to derive logical calculations between these two aggregated values (either in answers or in RPD)

Logical Formula :TODATE(“Month Ago Base Metric”, "Sample Sales"."H0

Time"."Month“)

Limitations :This aggregation will return at least month level detail. Ie, if




day level, then it will break down results to this level of detail

12. Month Ago Month To Date (Mago MTD)



Sum

Q2Q2


DefinitionMonth Ago Year-To-Date value for the same period. For the same period in the previous month, it returns the sum of all the values from the first day of the year to that period.

Exp : Mago YTD of revenue for May 19th of Year N returns Sum of revenue for Apr 1st to Apr 19th

Use When :Comparing current month YTD to Mago YTD

Logical FormulaCASE

WHEN Calendar Month is January 1 THEN AGO(“Base Metric", "Sample Sales"."H0 Time"."Month", 1)

ELSE “19. Base Metric YTD” - “Base Metric“

Limitations :

Level Base = Month on time dimension.

This aggregation will Always return data detail at month level detail, no matter what time object are included in the

report.

13. Month Ago Year To Date Sum (Mago YTD)



Sum

Q2Q2


14. Quarter to Date Sum (QTD)

Definition :Sums all the values from the first day of the quarter to the day selected in the query

Exp : QTD Sum of revenue for May 19th of Year N returns sum of revenue from Apr 1st to May 19th included

Use When :Use this aggregation when, for a given date, you need to see the sum of the metric since the beginning of the quarter that this date belongs to. This aggregation is useful to track performance when reporting cycles are at quarter level, to compare from current quarter to another past quarter one, or to compare entities during current quarter. I.e., how were we doing last quarterat the same day in quarter, compared to this quarter.

Logical Formula :TODATE(“Base Metric”, "Sample Sales"."H0 Time".“Quarter“)

Limitations :This aggregation will return at least Quarter level detail. Ie, if


month, then the query will force return one row per quarter.

Incase there are time objects in the reports at quarter, month,

week or day level, then it will break down the results to this

level of detail.



Sum

Q2Q2


15. Quarter To Date Monthly Avg

Definition :Sum of all the values from the first day of the quarter to the day selected in the query divided by number of months (decimal) from the start of the year to the current period

Exp : QTD Monthly Avg of revenue for May 19th of Year N returns Monthly Avg of revenue from Apr 1st to May 19th included

Use When :Use this object to view how average Monthly value for the whole quarter is stabilizing as quarter completes. At any given date in the quarter, the monthly average of all past days in quarter will be returned. This aggregation is useful when performancemeasurement cycles are at quarter level to compare metrics from one period to another one. I.e., how were we doing last quarter or last year at the same day in quarter, compared to this period.

Logical Formula :“14 QTD Sum" /"FT1 Time Facts"."T74 QTD Distinct Months“

T74 is a time fact object that calculates the number of distinct

months since the beginning of the Quarter for any date.

Limitations :This aggregation will return at least Quarter level detail. Ie, if


month, then the query will force return one row per quarter.


week or day level, then it will break down results to this level

of detail.



Avg

Q2Q2


16. Quarter To Date Daily Avg : Sum of all the values from the first day of the quarter to the selected day

divided by number of days from the start of the quarter to the selected Day

Exp : QTD daily Avg of revenue for May 19th of Year N returns daily Avg of revenue from Apr 1st to May 19th included

Similar to : 15. Quarter To Date Monthly Avg, but with day denominator instead of month. The use

case and formulas structures similar to the ones for 15. Quarter To Date Monthly Avg.

17. Quarter To Date Weekly Avg : Sum of all the values from the first day of the quarter to the selected day

divided by number of weeks from the start of the quarter to the selected day

Exp : QTD weekly Avg of revenue for May 19th of Year N returns weekly Avg of revenue from Apr 1st to May 19th

included

Similar to : 15. Quarter To Date Monthly Avg, but with week denominator instead of month. The

use case and formulas structures similar to the ones for 15. Quarter To Date Monthly Avg

16. Quarter To Date Daily Avg

17. Quarter To Date Weekly Avg


Jan Feb Mar July Aug Sep Oct Nov Jan Mar July Aug Sep Oct Nov Dec

Daily Avg

JunAprDec

Q2

Feb May

Q2

Apr May Jun



Weekly Avg

JunAprDec

Q2

Feb May

Q2

Apr May Jun

Definition :For the same period in the previous quarter, this aggregation returns the sum of all the values from the first day of the quarter to the same day in previous quarter as the selected day in the query.

Exp : Qago QTD of revenue for May 19th of Year N returns Sum of revenue from Jan 1st to Feb 19th

Use When :Use this aggregation to compare line to line what previous quarter value was with what current quarter value is, at any givenday in the quarter. This time series allows to have both values in the same query table, as distinct columns, and hence allows to derive logical calculations between these two aggregated values (either in answers or in RPD)

Logical Formula :TODATE(“Quarter Ago Base Metric”, "Sample Sales"."H0

Time"."quarter“)

Limitations :This aggregation will return at least quarter level detail. Ie, if


quarter, then the query will force return one row per quarter.


week or day level, then it will break down results to this level

of detail

18. Quarter Ago Quarter To Date Sum (Qago QTD)


Jan Feb Mar July Aug Sep Oct Nov Dec Jan Mar July Aug Sep Oct Nov Dec

Q2Q2

Apr May Jun Feb May Jun

Sum

Apr

19. Year to date to Date Sum (YTD)

Definition :Sums all the values from the first day of the year to the selected day in the query.

Exp : YTD Sum of revenue for May 19th of Year N returns sum of revenue from Jan 1st of year N to May 19th included

Use When :Use this object when, for a given date, you need to see the sum of the metric since the beginning of the year that this date belongs to. This aggregation is useful to track how cumulative value of the metric is doing for the whole year. The cumulative view attenuates the impacts of weekly/monthly adjustments, and allows sharper business comparison to targets or budgets

Logical Formula :TODATE(“Base Metric”, "Sample Sales"."H0

Time".“Year“)



Sum

JunAprDec

Q2

Feb May

Q2

Apr May Jun

20. Year To Date Monthly Avg : Sums of all the values from the first day of the year to the current period divided by number of months from the start of the year to the current period

Exp : YTD Monthly Avg of revenue for May 19th of Year N returns Monthly Avg of revenue from Jan 1st to May 19th

included of year N

Similar to : 15. Quarter to Date Monthly Avg, but with scope of year for numerator instead of Quarter. The use case and formulas structures are similar.

21. Year Ago Year To Date Sum (Yago YTD) : For the same period in the previous year, it returns the sum of all the values from the first day of the year to that period.

Exp : Yago YTD Sum of revenue for May 19th of Year N returns Sum of revenue from Jan 1st to May 19th included of year N-1

Similar to : 18. Quarter Ago Quarter To Date Sum (Qago QTD), but with scope of year for numerator instead of Quarter.

20. Year To Date Monthly Avg

21. Year Ago Year To Date Sum



Monthly Avg

JunAprDec

Q2

Feb May

Q2

Apr May Jun



Sum

JunAprDec

Q2

Feb May

Q2

Apr May Jun

22Rolling 3 Months

Sum

Rolling 3 months sum as of selected month : sum of current

month plus previous two months value. For January, it gives the

value of January plus December and November of the previous

year

23Rolling 3 Months

Daily Avg

Daily average over Rolling 3 months period : Rolling 3 months

sum as of selected month divided by number of days in Rolling 3

Months period.

24Rolling 3 Months

Monthly Avg

Monthly average over Rolling 3 months period : Rolling 3

months sum as of selected month divided by 3

25Rolling 6 Months

Sum

Rolling 6 months sum as of selected month : sum of current

month plus previous two months value. For January, it gives the

value of January plus December and November of the previous

year

26Rolling 6 Months

Daily vg

Daily average over Rolling 6 months period : Rolling 6 months

sum as of selected month divided by number of days in Rolling 3

Months period.

27Rolling 6 Months

Monthly Avg

Monthly average over Rolling 6 months period : Rolling 6

months sum as of selected month divided by 6

Metrics Aggregation Details : Rolling Time

Definition :Rolling X months sum as of selected month : sum of current month plus previous X-1 months value. For January, it gives the value of January plus December and November of the previous year

Exp : Rolling 6 Months Sum of revenue for Apr of year N returns sum of revenue from Dec of year N-1 to Apr of year N

Use When :Use this aggregation to track how a metric is structurally trending over time, with limited impact of week to week variations oradjustments. The rolling X months aggregation will give the trailing sum of a metric at any month in time

Logical Formula :FILTER(“All Hist Base Metric” USING “# of months between

selected month and fact month” < X)

“All Hist Base Metric” is an logical aggregation that sums up the

values for all the month available in db history

“# of months between selected month and fact month” is the

calculation of # of months from the selected value in the time

dimension, and the month id in the fact table

Limitations :This aggregation requires that time object “month” be present in

the query, and that no day/week grain object be in the query.

Ie, you have to both : use month AND not use day/week with this

object.

22. Rolling 3 Months Sum

25. Rolling 6 Months Sum



Sum

JunAprDec

Q2Q2

Apr May Jun Feb May

Definition :Rolling X months monthly average as of selected month : sum of current month plus previous X-1 months value, all divided by X. For January, it gives the value of January plus December and November of the previous year

Exp : Rolling 6 Months Monthly Avg of revenue for Apr of year N returns Monthly Avg of revenue from Dec of year N-1 to Apr of year N

Use When :Use this aggregation to track how a metric is structurally trending over time, limiting impacts of week to week variations orconjectural adjustments. The rolling X months monthly average will give a trailing average aggregation of a metric at any month in time

Logical Formula :“22 or 25 RXM Base Metric” / X


the query, and that no day/week grain object be in the query.

Ie, you have to both : use month AND not use day/week with this

object.

24. Rolling 3 Months Monthly Avg

27. Rolling 6 Months Monthly Avg



Monthly Avg

Q2

Apr May Jun JunAprDec

Q2

Feb May

Definition :Rolling X months daily average as of selected month : sum of current month plus previous X-1 months value, all divided by average number of days within X months.

Exp : Rolling 6 Months Daily Avg of revenue for Apr of year N returns Daily Avg of revenue from Dec of year N-1 to Apr of year N

Use When :Use this aggregation to track how a metric is structurally trending over time, limiting impacts of week to week variations orconjectural adjustments. The rolling X months daily average will give a trailing average aggregation of a metric at any monthin time

Logical Formula :“22 or 25 RXM Base Metric” / (X x 30.4)


the query, and that no day/week grain object be in the query. Ie,

you have to both : use month AND not use day/week with this

object.

The Daily averaging simply results from using fixed denominator of

average 30.4 days per month. Therefore it does not take into

account differences in the proper number of days for each month.

It is possible to enhance the formula using time facts objects to

address that limitation

23. Rolling 3 Months Daily Avg

26. Rolling 6 Months Daily Avg



Sum of metric / Avg number of days

Q2


Q2

Feb May

30 Value Variation to Month Ago Value change to same period in previous month

31 Percent Change to Month AgoChange to same period in previous month, expressed as percent growth of

previous month value

32Month to Month Ago

Compounded Yearly Growth

Variation to last month expressed as a compounded yearly percent growth

rate. Example : 2% growth month to Month Ago = 26.82% compounded

yearly growth rate.

33

Month To Date to Month Ago

Month To Date Percent

Change

Change to same Month To Date period in previous month, expressed as

percent growth of previous month to date value

34Value Variation to Quarter

AgoValue change to same period in previous Quarter

35Percent Change to Quarter

Ago

Change to same period in previous quarter, expressed as percent growth

of previous quarter value

36Month to Quarter Ago

Compounded Yearly Growth

Variation to same period last Quarter expressed as a compounded yearly

percent growth rate.

37

Quarter To Date to Quarter

Ago Quarter To Date Percent

Change

Change to same Quarter To Date period in previous quarter, expressed as

percent growth of previous quarter to date value

38 Value Variation to Year AgoDifference of the value of the metric in the current period with the value of

the metric in the same period the previous year

39 Percent Change to Year AgoChange to same period in previous Year, expressed as percent growth of

previous Year value

40Year To Date to Year Ago Year

To Date Percent Change

Variation to same Year To Date period last Year expressed as a percent

growth rate.

41 Value Variation to Week AgoDifference of the value of the metric in the current period with the value of

the metric in the same period the previous Week

42 Percent Change to Week AgoChange to same period in previous Week, expressed as percent growth of

previous Week value

Metrics Aggregation Details : Time Variations

Definition :Value change to same period in previous month

Exp : Value Variation to Month Ago of revenue for May 19th of year N returns the difference between revenue on May 19 th

and revenue of Apr 19th of year N

Use When :Use this aggregation to directly display month to month variation measures in a query

Logical Formula :“Base Metric” - “04. Mago Base Metric”

Limitations :This aggregation will return at least Month level

detail. Ie, if no time object in the report, or if only

objects higher than month, then the query will force

return one row per month. Incase there are time

objects in the reports at quarter, month, week or day

level, then it will break down the results to this level

of detail

30. Value Variation to Month Ago (Value Var to Mago)



Sum Y - X

Metric Value on same day in previous month = X Metric Value on day selected in query = Y

JunAprDec

Q2

Feb May

Q2

Apr May Jun

Definition :Change to same period in previous month, expressed as percent growth of previous month value

Exp : Value Variation to Month Ago of revenue for May 19th of year N returns the difference between revenue on May 19 th

and revenue of Apr 19th of year N

Use When :Use this aggregation to directly display month to month percent variation measures in every row of a query.

Logical Formula :[ (“Base Metric” - “04. Mago Base Metric”) / “04. Mago

Base Metric” - 1 ] * 100

Limitations :This aggregation will return at least Month level detail.

Ie, if no time object in the report, or if only objects

higher than month, then the query will force return one

row per month. Incase there are time objects in the

reports at quarter, month, week or day level, then it will

break down the results to this level of detail

31. Percent Change to Month Ago (Pct Chg to Mago)



Sum [ ( (Y - X) / X ) - 1 ] *100


JunAprDec

Q2

Feb May

Q2

Apr May Jun

Definition :Variation to last month expressed as a compounded yearly percent growth rate. Example : 2% growth month to Month Ago = 26.82% compounded yearly growth rate.

Exp : Month to Month Ago Compounded Yearly Growth on revenue for May 19th of year N” returns compounded yearly extrapolation of growth pace between periods of Apr 19th and May 19th of year N.

Use When :Use this aggregation to rapidly convert month to month growth into a compounded yearly percent growth rate. For example, express variation of cash balance over past month into a flat full year percent growth figure.

Logical Formula :( POWER(“Base Metric” / “04. Mago Base Metric”, 12) - 1) * 100

Limitations :This aggregation will return at least Month level detail. Ie, if no time object in the report, or if only objects higher

than month, then the query will force return one row per month. Incase there are time objects in the reports at

quarter, month, week or day level, then it will break down the results to this level of detail

32. Month to Month Ago Compounded Yearly Growth



Sum Compounded yearly % growth of (X to Y)


JunAprDec

Q2

Feb May

Q2

Apr May Jun

Definition :Change of month to date current value to last month month to date value expressed as percent change to initial period

Exp : MTD to Mago MTD Pct Chg of revenue for May 19th of year N returns the percent growth of revenue between periods from Apr 1st to Apr 19th compared with May 1st to May 19th of year N

Use When :Use this aggregation to directly display month to date performance compared to previous month for each record on a query.

Logical Formula :[ (“MTD Base Metric” / “Mago MTD Base Metric”) -1 ]

x 100

Limitations :This aggregation will return at least Month level detail.

Ie, if no time object in the report, or if only objects

higher than month, then the query will force return

one row per month. Incase there are time objects in

the reports at quarter, month, week or day level, then

it will break down the results to this level of detail

33. Month To Date to Month Ago Month To Date Percent Change

(MTD to Mago MTD Pct Chg)



Mago MTD = X MTD = Y Value returned : [ (Y/X) -1 ] x 100

Q2


Q2

Feb May

34. Value Variation to Quarter Ago

35. Percent Change to Quarter Ago

34. Value Variation to Quarter Ago (Value Var to Qago) :

Value change to same period in previous Quarter

Exp : Value Var to Qago revenue for May 19th of Year N returns difference between value of metric as of May 19th and value as of Feb 19th of year N

Similar to : 30. Value Variation to Month Ago, but with scope of Quarter instead of Month. The use case and formulas structures are similar.

35. Percent Change to Quarter Ago (Pct Chg to Qago): Change to same period in previous quarter, expressed as percent growth of previous quarter value

Exp : Pct Chg to Qago revenue for May 19th of Year N returns percent growth between value of metric as of May 19th

and value as of Feb 19th of year N

Similar to : 31. Percent Change to Month Ago, but with scope of Quarter instead of Month



Qago Value = X Current Value = Y Value returned : Y - X

JunAprDec

Q2

Feb May

Q2

Apr May Jun



Qago Value = X Current Value = Y Value returned : [ (Y - X)-1 ] /100

JunAprDec

Q2

Feb May

Q2

Apr May Jun

Definition :Variation to same period last Quarter expressed as a compounded yearly percent growth rate.

Exp : Month to Quarter Ago Compounded Yearly Growth on revenue for May 19th of year N” returns compounded yearly extrapolation of growth pace between periods of Feb 19th and May 19th of year N.

Use When :Use this aggregation to rapidly convert Quarter to quarter growth into a compounded yearly percent growth rate. For example, express variation of cash balance over past quarter into a flat full year percent growth figure.

Logical Formula :( POWER(“Base Metric” / “07. Qago Base Metric”, 4) - 1) * 100

Limitations :This aggregation will return at least Quarter level detail. Ie, if no time object in the report, or if only objects higher

than Quarter, then the query will force return one row per Quarter.

36. Month to Quarter Ago Compounded Yearly Growth



Compounded yearly % growth calculated betwee Y and X

Metric value Quarter ago = X Current Value = Y

JunAprDec

Q2

Feb May

Q2

Apr May Jun

Definition :Change of quarter to date current value to last quarter quarter to date value expressed as percent change to initial period

Exp : QTD to Qago QTD Pct Chg of revenue for May 19th of year N returns the percent growth of revenue between periods from Jan 1st to Feb 19th compared with March 1st to May 19th of year N

Use When :Use this aggregation to directly display quarter to date performance compared to previous quarter for each record on a query.

Logical Formula :[ (“14. QTD Base Metric” / “18. Qago QTD Base

Metric”) -1 ] x 100

Limitations :This aggregation will return at least Quarter level

detail. Ie, if no time object in the report, or if only

objects higher than Quarter, then the query will

force return one row per Quarter.

37. Quarter To Date to Quarter Ago Quarter To Date Percent

Change (QTD to Qago QTD Pct Chg)



Metric Year ago Year to date value = X Current Quarter to Date Value = Y

Value Returned : Compounded Percent growth calculated betwee Y and X

JunAprDec

Q2

Feb May

Q2

Apr May Jun

38. Value Variation to Year Ago

39. Percent Change to Year Ago

38. Value Variation to Year Ago (Value Var to Yago) :

Value change to same period in previous Year

Exp : Value Var to Yago revenue for May 19th of Year N returns difference between value of metric as of May 19th of year N and value as of May 19th of year N-1

Similar to : 30. Value Variation to Month Ago, but with scope of Year instead of Month.

39. Percent Change to Year Ago (Pct Chg to Yago): Change to same period in previous Year, expressed as percent growth of previous Year value.

Exp : Pct Chg to Yago revenue for May 19th of Year N returns percent growth between value of metric as of May 19th of year N and value as of May 19th of year N-1

Similar to : 31. Percent Change to Month Ago, but with scope of Year instead of Month



Qago Value = X Current Value = Y Value returned : Y - X

JunAprDec

Q2

Feb May

Q2

Apr May Jun



Qago Value = X Current Value = Y Value returned : [ (Y - X)-1 ] /100

JunAprDec

Q2

Feb May

Q2

Apr May Jun

Definition :Change of year to date current value to last year year to date value expressed as percent change to initial period

Exp : YTD to Yago YTD Pct Chg of revenue for May 19th of year N returns the percent growth of revenue between periods from May 1st to May 19th of year N-1 compared with May 1st to May 19th of year N

Use When :Use this aggregation to directly display quarter to date performance compared to previous quarter for each record on a query.

Logical Formula :[ (”19. YTD Base Metric” / “21. Yago YTD

Base Metric”) -1 ] x 100

40. Year To Date to Year Ago Year To Date Percent Change (YTD

to Yago YTD Pct Chg)



Metric Year ago Year to date value = X Percent growth calculated betwee Y and X Current Year to Date Value = Y

JunAprDec

Q2

Feb May

Q2

Apr May Jun

41. Value Variation to Week Ago

42. Percent Change to Week Ago

41. Value Variation to Week Ago (Value Var to Wago) :

Value change to same period in previous Week

Exp : Value Var to Week ago revenue for May 19th of Year N returns difference between value of metric as of May 19th

and value as of May 12th of year N

Similar to : 30. Value Variation to Month Ago, but with scope of Week instead of Month.

42. Percent Change to Week Ago (Pct Chg to Wago): Change to same period in previous Week, expressed as percent growth of previous Week value.

Exp : Pct Chg to Yago revenue for May 19th of Year N returns percent growth between value of metric as of May 19th

and value as of May 12th of year N

Similar to : 31. Percent Change to Month Ago, but with scope of Week instead of Month



Week ago Value = X Current Value = Y Value returned : [ (Y - X)-1 ] /100

Q2


Q2

Feb May



Week ago Value = X Current Value = Y Value returned : Y - X

Q2


Q2

Feb May

43First on Time

spanFirst oT returns the value of the first day in the period selected for the query.

44Last on Time

spanLast oT returns the value of the last day in the period selected for the query.

45 Value Variation

For any time interval specified in the query row, this aggregation returns the difference

between value at beginning of the interval and value at end of the interval. The values

at beginning and end date that make up the variation can be displayed by adding

aggregation objects Fst oT and Last oT. Info about beginning date and end date for

each row are available with time facts objects : First Time Day Dt and Last Time Day

Dt. For example, if a query is filtered for a quarter, this will show the Value Variation

between first and last day of the quarter. If the query objects are at week level, This

aggregation will returns for each row (week level) the value of the last day of each

week minus value of the first day in that week.

46 Percent Change

For any time interval specified in the query row, this aggregation returns the growth

between value at beginning of the interval and value at end of the interval expressed as

a % to initial value. The values at beginning and end date that make up the variation

can be displayed by adding aggregation objects First oT and Last oT. Info about

beginning date and end date for each row are available with time facts objects : First

Time Day Dt and Last Time Day Dt.

47Average Monthly

Value Variation

This aggregation divides the Value Variation aggregation metric by number of months

in the time interval considered. Note that number of months is decimal value (not

integer), and only dependent on number of days in the time interval. Example : time

interval between Jan 13th and Mar 27 = 2.48 months

48Average Monthly

Percent Change

This aggregation converts the Avg Monthly Value Variation aggregation metric into a

simple arithmetic monthly average percentage variation. Note, this is value is not a

compounded % variation.

49Average Yearly

Value Variation

This aggregation divides the Value Variation aggregation metric by number of years in

the time interval considered. Note that number of years is decimal value (not integer),

and only dependent on number of days in the time interval.

50

Compounded

Yearly Percent

Change

This returns the compounded yearly variation rate for the time interval considered. It

projects the growth rate in the interval to a basis of 1 year. Exp 1 : 50% growth over 24

months = 22.47% compounded yearly growth rate. Exp 2 : 2% growth over 1 month =

26.82% compounded yearly growth rate.

Metrics Aggregation Details : Time Span Variations

Definition :First oT returns the metric value of the first day where there is data in the period selected for the query.

Last oT returns the value of the last day where there is data in the period selected for the query.

Exp : Fst oT and Lst oT revenue for query filtered as “between Apr 01 of year N and May 19th of year N returns two figures : daily value for Apr 1st and daily value for May 19th

Use When :Use these aggregations when analyzing variations over a time span, and are interested in showing starting and ending values for the time span. Fst oT and Lst oT will adapt to any time grain in the query and will break down results by each time grain.

Hint :To see what are the actual dates for Fst oT and Lst oT, use

time facts objects “T60 First Time Day Dt” and “T61 Last

Time Day Dt”

Logical Formula :“Base Metric” with aggregation rule First (or Last) on Time

dimension, Sum on all other dimensions

43. First on Time Span

44. Last on Time Span



Sum [[ (Y - X) / X ] -1 ] x 100

Metric Value on First day of time selected = X Metric Value on Last day of time selected = Y

JunAprDec

Q2

Feb May

Q2

Apr May Jun

Definition :For any time interval specified in the query row, this aggregation returns the difference between value at beginning of the interval and value at end of the interval. The values at beginning and end date that make up the variation can be displayed by adding aggregation objects Fst oT and Last oT. Info about beginning date and end date for each row are available with time facts objects : First Time Day Dt and Last Time Day Dt.

Exp : Value Variation aggregation on revenue for query filtered as “between Apr 01 of year N and May 19th of year N” returns value as of May 19th minus value as of Apr 1st

Use When :Use that aggregation when analyzing variations over a custom time span, or variations for each row in a query. For any custom time period, or for any time grain rows in a query, “Value Variation” will show delta between starting and ending values for the time span. It will adapt to any time grain in the query and will break down results by each row.

Hint :To see what are the actual dates for Fst oT

and Lst oT, use time facts objects “T60 First

Time Day Dt” and “T61 Last Time Day Dt”

Logical Formula :“44 Last on Time - 43 First on Time”

45. Value Variation



Sum Y - X

Metric Value that day = X Metric Value that day = Y

JunAprDec

Q2

Feb May

Q2

Apr May Jun

Definition :For any time interval specified in the query row, this aggregation returns the growth between value at beginning of the intervaland value at end of the interval expressed as a % to initial value. The values at beginning and end date that make up the variation can be displayed by adding aggregation objects Fst oT and Last oT. Info about beginning date and end date for each row are available with time facts objects : First Time Day Dt and Last Time Day Dt.

Exp : Percent Change aggregation on revenue for query filtered as “between Apr 01 of year N and May 19th of year N” returns the percent growth of revenue between Apr 1st and May 19th

Use When :Use “Percent Change” aggregations when analyzing percent growth over a custom time span, or growth for each row in a query. For any custom time period, or for any time grain rows in a query, “Percent Change” will show delta between starting and ending values for the time span. It will adapt to any time grain in the query and will break down results by each row.

Hint :To see what are the actual dates for Fst

oT and Lst oT, use time facts objects “T60

First Time Day Dt” and “T61 Last Time

Day Dt”

Logical Formula :[ (“44 Last on Time” – “43 First on Time”) /

“43 First on Time” – 1 ] * 100

46. Percent Change



Sum [[ (Y - X) / X ] -1 ] x 100

Metric Value that day = X Metric Value that day = Y

JunAprDec

Q2

Feb May

Q2

Apr May Jun

Definition :This aggregation divides the Value Variation aggregation metric by exact number of months included in the custom time interval considered. Note that number of months is decimal value (not integer), and only dependent on number of days in the time interval. Example : time interval between Jan 13th and Mar 27 = 2.48 months

Exp : Avg Monthly variation on revenue for query filtered as “between Apr 01 of year N and May 19th of year N” extrapolates growth pace of Apr 1st and May 19th over a full single month.

Use When :Use this aggregation to rapidly convert growth over any time period into a monthly change figure. For example, express variation of cash balance over past 10 days or past quarter into a comparable monthly growth figure. This aggregation will extrapolate the values and calculate the average monthly change for each.

Hint :To see what are the actual dates for Fst oT and Lst oT, use time facts objects “T60 First Time Day Dt” and “T61 Last Time Day Dt”. To see the number of months considered in the calculation for each period, use time facts object “T64 # of Months“

Logical Formula :“45. Value Variation” / “T64 # of Months“

47. Average Monthly Value Variation



Sum (Y - X) / number of months (decimal) between last day and first day


JunAprDec

Q2

Feb May

Q2

Apr May Jun

Definition :This aggregation converts the Avg Monthly Value Variation aggregation metric into an average growth expressed as a percent of initial value. Note, this is value is not a compounded % variation

Exp : Average Monthly Percent Change of revenue for custom time period Apr 1st to May 19th of Year N returns % value that represents Avg monthly growth of revenue between Apr 1st and May 19th of year N

Use When :Use this aggregation to rapidly convert growth over any time period into a monthly percentage change figure. For example, express variation of cash balance over past 10 days or past quarter into a comparable monthly percent growth figure. This aggregation will extrapolate the values and calculate the average monthly percent change for each.

Hint :To see what are the actual dates for Fst oT and Lst oT, use time facts objects “T60 First Time Day Dt” and “T61 Last Time Day Dt”. To see the number of months considered in the calculation for each period, use time facts object “T64 # of Months“

Logical Formula :“46. Percent Change” / “T64 # of Months“

48. Average Monthly Percent Change



Sum Avg Monhtly % growth of metric value between First Day and Last Day

Metric Value on First day of time selected Metric Value on Last day of time selected

JunAprDec

Q2

Feb May

Q2

Apr May Jun

Definition :This aggregation divides the Value Variation aggregation metric by exact number of years in the time interval considered. Thenumber of years is decimal value (not integer) and only calculated based on number of days in the time interval.

Exp : Avg Yearly variation on revenue for query filtered as “between Apr 01 of year N and May 19th of year N” returns the value growth of revenue for the whole year, extrapolating growth pace of Apr 1st and May 19th over the whole year.

Use When :Use this aggregation to rapidly convert growth over any time period into a yearly value change figure. For example, express variation of cash balance over past 3 months or past 5 quarters into a comparable yearly growth figure. This aggregation willextrapolate the values and calculate the average yearly change for each.

Hint :To see what are the actual dates for Fst oT and Lst oT, use time facts objects “T60 First Time Day Dt” and “T61 Last Time Day Dt”. To see the number of months considered in the calculation for each period, use time facts object “T66 # of Years“

Logical Formula :“45. Value Variation” / “T64 # of Months“

49. Average Yearly Value Variation



Sum (Y - X) / number of years (decimal) between last day and first day


JunAprDec

Q2

Feb May

Q2

Apr May Jun

Definition :This returns the compounded yearly variation rate for the custom time interval considered. It projects the growth rate in theinterval to a full basis of 1 year. Example 1, a 50% growth rate over 24 months is equivalent to a 22.47% compounded yearly growth rate. Example 2, a 2% growth over 1 month is equivalent 26.82% compounded yearly growth rate. The formula for compounded growth rate leverage the power arithmetic function.

Exp : Compounded Yearly Percent Change on revenue for query filtered as “between Apr 01 of year N and May 19 th of year N” returns the full year percent growth of revenue, using a compounded extrapolation of actual growth pace of Apr 1st and May 19th.

Use When :Use this aggregation to rapidly convert growth over any time period into a compounded yearly percent growth rate. For example, express variation of cash balance over past 3 months or past 5 quarters into a flat full year percent growth figure.

Hint :To see what are the actual dates for Fst oT and Lst oT, use time facts objects “T60 First Time Day Dt” and “T61 Last Time Day Dt”. To see the number of months considered in the calculation for each period, use time facts object “T66 # of Years“

Logical Formula :[ POWER ( (1 + "46 Percent Variation" / 100) , (1 / "T66 # of Years“) ) – 1 ]* 100

50. Compounded Yearly Percent Change



Sum (Y - X) / number of years (decimal) between last day and first day


JunAprDec

Q2

Feb May

Q2

Apr May Jun

51Full Month

Sum

Sum of the metric value for the full month. This aggregation is always calculated at the

month level.. For example, when viewed against Week level, it shows the same total

month's value for all the weeks in that month.

52Month Ago Full

Month Sum

Full month's value of the metric for the previous month. This aggregation is always

calculated at the month level. For example, when viewed against Week level, it shows

the same total month's value for all the weeks in that month.

53Year Ago Full

Month Sum

Full month's value of the metric for the same month in the previous year. This

aggregation is always calculated at the year level. For example, when viewed against

week level, it shows the same total month's value for all the weeks in that month.

53

12 Months Ago

Full Month

Sum

Full month's value of the metric for 12 months prior to the current month. This

aggregation is always calculated at the month level. For example, when viewed against

week level, it shows the same total month's value for all the weeks in that month.

54Full Quarter

Sum

Sum of the metric value for the full quarter. This aggregation is always calculated at the

quarter level. When viewed against Month or Week level, it shows the quarter's value

for all the weeks and months in that quarter.

55 Full Week Sum

Sum of the metric value for the full week. This aggregation is always calculated at the

week level. When viewed against day level, it shows the week's value for all the days

in that week.

56 Full Year Sum

Sum of the metric value for the full year. This aggregation is always calculated at the

year level. When viewed against quarter, month or week level, it shows the year's value

for all the quarters, month, and weeks in that year.

57Year Ago Full

Year Sum

Full year's value of the metric for the previous year. This aggregation is always

calculated at the year level

58

Quarter Ago

Full Quarter

Sum

Full quarter's value of the metric for the previous quarter. This aggregation is always

calculated at the quarter level

Metrics Aggregation Details : Level Based (1/2)

61Sum for All

Customers

Sum of the metric for all the Customers. It always returns the value at Total level

irrespective of the level within the Customer dimension that is used in the query

62Sum for All

Markets

Sum of the metric for all the Markets. It always returns the value at Total level

irrespective of the level within the Market dimension that is used in the query

63Sum for All

Products

Sum of the metric for all the Products. It always returns the value at Total level

irrespective of the level within the Product dimension that is used in the query

64Sum for All

Employees

Sum of the metric for all the Employees. It always returns the value at Total level

irrespective of the level within the Employee dimension that is used in the query

65Sum for All Dims

But Time

Sum of the metric for all the dimensions except Time. It always the value at the

Total level when viewed against all the dimensions except time. When viewed

against Time, it returns the value at that level.

66Sum for All

Orders

Sum of the metric for all the Orders. It always returns the value at Total level

irrespective of the level within the Orders dimension that is used in the query

67 Sum by Orders

Sum of the metric for by orders. This aggregation always returns the value at the

detail order level irrespective of the level within the dimension that is used in the

query

68Sum by

Customers

Sum of the metric for by Customers. This aggregation always returns the value at

the detail order level irrespective of the level within the dimension that is used in

the query

69Sum By

Employees

Sum of the metric for by Employees. This aggregation always returns the value at

the detail order level irrespective of the level within the dimension that is used in

the query

Metrics Aggregation Details : Level Based (2/2)

76

Fragmented

Year Ago

Pattern

This aggregation will fragment physical data to a lower granularity then it is in the database, using the pattern

of last year spread. Example : Forecast data is available by month in db, this metric will take the 'by month'

information, and break it down at day level using the same spread as how last year's actual daily values

were spread over the same month. This aggregation will apply similar break down on the following

dimensions : Time, Customer, Market. Ie, forecast data available only by month by market level is

fragmented further down to be "by customer, by product, by day" according to how that pattern was the

previous year.

77

Fragmented

Quarter

Ago Pattern

This aggregation will fragment physical data to a lower granularity then it is in the database, using the pattern

of last quarter spread. Example : Forecast data is available by month in db, this metric will take the 'by

month' information, and break it down at day level using the same spread as how last quarter's actual daily

values were spread over the same month. This aggregation will apply similar break down rule on the

following dimensions : Time, Customer, Market. Ie, forecast data available only by month by market level is

fragmented further down to be "by customer, by product, by day" according to how that pattern was the

previous quarter.

78Fragmented

Flat Pattern

This aggregation will fragment physical data to a lower granularity then it is in the database, using linear rule

spread. Example : Forecast data is available by month in db, this metric will take the 'by month' information,

and break it down at day level simply by dividing by number of days available in each month. This

aggregation will apply similar break down on the following dimensions : Time, Customer, Market. Ie, forecast

data available only by month by market level is linearly fragmented further down to be "by customer, by

product, by day".

80Daily

RunRate

For any time interval specified in the query row, this aggregation returns the sum of the metric value over this

interval divided by the number of days in that period. To visualize beginning and end dates, and number of

days used in the calculation, use time facts objects : First Time Day Dt Last Time Day Dt and # of Days. For

example, if a query is filtered for a quarter, this will show the average daily value for this quarter.

81Weekly

RunRate

For any time interval specified in the query row, this aggregation returns the sum of the metric value over this

interval divided by the number of weeks in that period. To visualize beginning and end dates, and number of

weeks used in the calculation, use time facts objects : First Time week Dt Last Time week Dt and # of

weeks. Note that number of weeks is decimal value (not integer), and only dependent on number of days in

the time interval.

For example, if a query is filtered for a quarter, this will show the average weekly value for this quarter.

Metrics Aggregation Details : Other Aggs (1/2)

82Monthly

RunRate

For any time interval specified in the query row, this aggregation returns the sum of the metric value over this interval divided by the

number of months in that period. To visualize beginning and end dates, and number of months used in the calculation, use time facts

objects : First Time month Dt Last Time month Dt and # of months. Note that number of months is decimal value (not integer), and

only dependent on number of days in the time interval. Example : time interval between Jan 13th and Mar 27 = 2.48 months. For

example, if a query is filtered for a quarter, this will show the average monthly value for this quarter.

83Quarterly

RunRate


number of quarters in that period. To visualize beginning and end dates, and number of quarters used in the calculation, use time

facts objects : First Time quarter Dt Last Time quarter Dt and # of quarters. Note that number of quarters is decimal value (not

integer), and only dependent on number of days in the time interval.

For example, if a query is filtered for a quarter, this will show the average quarterly value for this quarter.

84Yearly

RunRate


number of years in that period. To visualize beginning and end dates, and number of years used in the calculation, use time facts

objects : First Time year Dt Last Time year Dt and # of years. Note that number of years is decimal value (not integer), and only

dependent on number of days in the time interval.

For example, if a query is filtered for a year, this will show the average yearly value for this year.

85

Seasonal

Percent of

Total

Month

This aggregation retrieves the percentage that a time value represents to the total of the current month. For any grain of the query

that is below the month level detail, this will return how much the value is for the total month it belongs to. If the query grain is higher

than month, this aggregation will return results at month level (a row for each month, with 100% as the value).

Note that this metric will consider the amount of data in the db, and extrapolate the result in case data is not available for full month in

the db (in order to show consistent percentage values where data is available for full month)

86

Seasonal

Percent of

Total

Quarter

This aggregation retrieves the percentage that a time value represents to the total of the current quarter. For any grain of the query

that is below the quarter level detail, this will return how much the value is for the total quarter it belongs to. If the query grain is

higher than quarter, this aggregation will return results at quarter level (a row for each quarter, with 100% as the value).

Note that this metric will consider the amount of data in the db, and extrapolate the result in case data is not available for full quarter

in the db (in order to show consistent percentage values where data is available for full quarter)

87

Seasonal

Percent of

Total Year

This aggregation retrieves the percentage that a time value represents to the total of the current year. For any grain of the query that

is below the year level detail, this will return how much the value is for the total year it belongs to. If the query grain is higher than

year, this aggregation will return results at year level (a row for each year, with 100% as the value).

Note that this metric will consider the amount of data in the db, and extrapolate the result in case data is not available for full year in

the db (in order to show consistent percentage values where data is available for full year).

Metrics Aggregation Details : Other Aggs (2/2)

4. Cloning Samples content on other environments with :

CAF V1.0Content Accelerator Framework

Cloner Utility

CAF V1 UtilityWhat it is

• CAF V1 is a user friendly utility that allows to clone any existing OBI EE

calculations, report and dashboards setup into any target OBI EE environment.

• After one dashboard is completed with its appropriate reports, calculations,

variables, RPD objects, views layout and formatting, why not apply its structure

as a template to other functional cases ?

• CAF V1.0 utility makes this task very straightforward and simple.

• Samples Sales content can easily be used as source content for CAF V1 in

order to duplicate logical constructs or webcat layouts to any target

environment.

CAF V1.0 Value offer

Adopts selected content from functional BI Value library for his

domain

OBI Functional Value Library (Samples or other)

Customer Functional Domain Owner

Cu

sto

mer

Ag

en

t

Mark

eti

ng

Man

ag

er

Cu

sto

mer

Gain & Refine

Customer

Insight

Design

Customer

Segments

Develop

Retention &

Growth

Campaigns

Request

Service

Provide

Personalized

Customer

Service

Generate &

Execute Lead

Real-Time

Offer

Allocation

Respond

to Offer

Respond

to Offer

Measure

Performance

Increased processes efficiency Increased OBI EE ROI

Customer business

Customer IT

Library Configuration Customer Environment

Deploys selected content setup,

• Simple & Easy

•Fast

•Secure

•optimized …

How to replicate content

from any existing

environment (Sample Sales

in this example)…

… into any different

target environment

(Paint in this

example)…

… with only

a few clicks,

a few seconds,

and zero editing

work

CAF V1.0 CloningPractical Overview

Step 1 : Launch Cloning utility for selected reports (or dashboards)

Step 2 : Declare source and target environments (Pick Subject Area from choices)

Step 3 : Provide Mappings for Required Objects (declarative mapping)

Step 4 : Provide name / path for cloned reports

Step 5 (Optional) : rename RPD objects created

CAF V1.0 Cloning Overview4 intuitive steps to complete cloning of any content

CAF V1.0 Cloning OverviewStep 1 : Launch Cloning for selected reports (or dashboards)

CAF V1.0 Cloning OverviewStep 2 : Declare source and target environments…

CAF V1.0 Cloning Overview Step 2 : Select Target Subject Area

CAF V1.0 Cloning Overview Step 3 : Declare Mappings for Required Objects

• Cloner module identifies minimum object required to build the selected reports

• Cloner module will create necessary logical objects in target RPD if/where needed based on minimum mappings provided

• Cloner module will create all webcat reports in target environment using mapping information and any new rpd object created

CAF V1.0 Cloning Overview Step 4 : Provide name / path for cloned reports

V1 : Use Case

1. Operational user :

Chooses report from list of templates

Expresses functional context for the target report

Benefits :

Learn by seeing what OBI EE can functionally deliver

Deliver better expression of needs

Reach deeper BI requirement faster

2. IT Developer / IT :

Run CAF on selected reports

Select base columns to map

Run Synchronizer to adapt object names

Benefits :

Fast and automated development process

Reduced risk in design development

Better leverage of OBI EE capabilities

Content Accelerator Framework Enablement Technology for all OBI EE stakeholders

Targets

• Any OBIEE

Environment

• No Specific

OBIEE Skills

required

CAF

Templates Library

RPD Web

-cat

ClonerSynchro

nizer

Rapid Time to Value, Speed up Implementation & adoption : fast deploy, fast learn

Increase ROI : broader & deeper functional leverage of OBI EE

Adopt Best Practices :

- Functional : all what OBI EE can really accomplish

-Technical : fully utilize OBI EE architecture & features

Reduce Dev Risk : Automated high Quality designs

Drives better & Deeper usage of our platform

Help POC Quality

Help drive platform enhancements

Secure dev process : reduce time and risk (less QA)

Stakeholders

• QA

• Support

• …

• Customers

• Consultants

• Partners

• PMs

• Field

• Engineering

Content Accelerator Framework Some Use Cases examples

1. Customer sees example of dashboards / reports that would apply

to his functional area, and wants to implement on his own

environment quickly,

2. Customer is interested in leveraging some logical calculations /

RPD features in in his environment, but not sure how to build it,

3. Customer creates reports or dashboards template formats that he

needs to use across several functional areas

4. Customer renames existing objects in RPD, and needs to upgrade

webcat with these

5. …

CAF V1 & Value Layers of OBI EE Content

Dashboards Structure & Page layouts

Field & Variable Prompts

Reports Views Layout

Columns Syntax & Formulas

Filtering

Query Structure

Webcat Foldering Structure

Presentation layer objects

Logical Layer : Logical calculations

Logical Layer : Root Logical Model

Physical Layer

RPD Variables, Security, Marketing

Dashboards

Reports

RPD

Database, OLAPs, ETL,

Aggregations, Physical Model

ETL logics

CAF V1.0 Coverage

CAF V1 Demos : overall scenario context Overall on site POC scenario context

Webcat

Target RPD

Online

Offline

• Source Library environment :

Source : Sample Sales

RPD

Online or Offline

Offline

• Requirement : showcase some of OBI EE capabilities by cloning many Sample Sales reports, dashboards and logical metrics in customer environment.

• Timeline : 15 min

• Release : 10.1.3.4

• CAF V1 Utility installed

CAF V1

• Customer environment (for training purposes)

Webcat

Target : Paint (vanilla)

Source Webcat

Source RPD

Target Webcat

Target RPD

Online or Offline

Offline

Cloner

1. Parses XML content of selected reports

2. Parses Source RPD with XUDML

3. Identifies Logical calculations in selected reports

8. Creates resulting report XML files in

Target Webcat

5. Parses XUDML Target RPD

6. Recognizes existing Logical calculations

7. Modifies Target RPD

Online or Offline

Offline

4. Identifies minimal mapping info needed from user

CAF V1 Demos : Cloner ModuleHow it works

CAF V1 UtilityWhat it does

1. Clones Reports, Dashboards, RPD logical formulas

2. Allows Saving of mappings info

3. Allows formula overriding

4. Introduces Optional / required mapping

5. Handles level mapping

6. Traceable Logs & Backups

7. Sources from any existing content

8. Synchs up RPD + Webcat names

CAF V1 Where to get it on OTN ? http://www.oracle.com/technology/products/bi/enterprise-edition.html

CAF V1 = free

utility,

Not a Licensed

product, not

maintained

INSTALL

Download 8 megs install batch file from OTN,

with simple install directions

Requires 10.1.3.4 & JDK 1.6

Installs by pasting 7 files in any existing

10.1.3.4 environment

Comprehensive functional user Guide

5. Switching/Updating

Alternative Datasets

How to switch to industry specific datasets, or

update dataset dimensions to custom values

Switching to Alternative prebuilt datasets

• For product dimension, a choice of 6 industry flavors is provided (Communications, Energy,

Food, Media, Medical, Banking) with 6 additional distinct files.

• For Market dimension, a file for EMEA one for North America and one for APAC are provided

as well.

To switch from one data set to another one :

• Stop BI Server

• Rename original product.xml file to product_Generic.xml

• Rename file of your choice from Product_Ind_XXX_xml to product.xml

• Start BI server.

Same procedure applies to switching to alternate Market data

Product and Market dimension values are stored in two distinct Xml files : Product.xml and Market.xml

Alternative sets of product and market data are provided with the 1.1 install of sample sales application.

Using generic

- Market.xml

- Product.xml

files

Using files :

- Market_Emea.xml

- Product_Ind_

Comm.xml

Switching to Alternative prebuilt datasets

Updating Dataset to custom values 1/2

• Import the file (click ok to window suggesting table structure and data import)

2. Update content of the table within Access Db.

You can also simply update data from XML datasources using MS Access software for example :

1. Import XML tables that need to be changed in a blank answers db

• Access menu : >File>Get External Data>Import

• Use filter for Files of type „XML‟ and browse to the location where you have the source XML files

3. Once data is updated, then Export table back

under XML format :

• select table to save and click menu >File>Export.

• Use save as type „XML‟ and browse to the location

where you have the source XML files

• Save by overriding the original file name (you may

want to keep a backup copy of original file)

4. Correct tablename Tag of each changed files :

• Using windows explorer, navigate to the directory where you have saved updated XMLs

• Open updated xml files with notepad

• Scroll to line # 2 and replace the text

from : <dataroot xmlns:od="urn:schemas-microsoft-com:officedata" ….>

to : <Table Name="Product"> or <Table Name=“Market"> or other depending on the

file you are changing (use the name of the file)

• Scroll down to last line of the file, and replace string from </dataroot> to </Table>

5. Stop and Start OBI EE server. You should now see your updated data in reports

Updating Dataset to custom values 2/2

Documents

Oracle Bi Sample App Content Guide