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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
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
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.
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.
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 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.
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.
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.
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
…
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.
Filter the scope of the report as required by using "Page Filter Prompts" at bottom of page.
…
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.
Filter the scope of the report as required by using Page Filter Prompts at bottom of page.
…
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
Filter the scope of the report as required by using Page Filter Prompts at bottom of page.
…
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.
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 …
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
Metric" prompt and select a dimension from "Select Dimension" prompt and select Go.
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
Metric" prompt and select a dimension from "Select Dimension" prompt and select Go.
Filter the scope of the report as required by using Page Filter Prompts at bottom of page.
…
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
Metric" prompt and select a dimension from "Select Dimension" prompt and select Go.
Set the number of Percentiles to display and hit "Go" button.
Filter the scope of the report as required by using "Page Filter Prompts" at bottom of page.
…
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"
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. …
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"
prompt and select a dimension from "Select Dimension" prompt and select Go.
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).
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.
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
Filter the scope of the report as required by using "Page Filter Prompts" at bottom of page.
…
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:
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. 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).
Filter the scope of the report as required by using Page Filter Prompts at bottom of page.
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:
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 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.
Filter the scope of the report as required by using Page Filter Prompts at bottom of page.
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:
From drop down list of choices at top of page, select a metric to analyze from Select Metric
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.
Filter the scope of the report as required by using Page Filter Prompts at bottom of page.
…
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.
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 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 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 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:
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.
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.
Filter the scope of the report as required by using Page Filter Prompts at bottom of page.
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
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
1. Define answers query as shown to the right :
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
1. Define answers query as shown to the right :
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.
How to Demo : Hierarchy drill chaining 2/3
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 : Hierarchy drill chaining 3/3
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
2. Define answers query as shown to the right :
Or, alternatively, paste following SQL into “SQL
Issued” under “Advanced” tab and press
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.
1. Define answers query as shown to the right :
Or, alternatively, paste following SQL into “SQL
Issued” under “Advanced” tab and press
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 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
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 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
Avg
Q2Q2
Apr May Jun May JunApr
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
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 results to this level of detail
12. Month Ago Month To Date (Mago MTD)
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
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)
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
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
no time object in the report, or if only objects higher than
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.
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
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
no time object in the report, or if only objects higher than
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 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
Avg
Q2Q2
Apr May Jun May JunApr
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
Year N - 1 Year NQ1 Q3 Q4 Q1 Q3 Q4
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
Year N - 1 Year NQ1 Q3 Q4 Q1 Q3 Q4
Jan Feb Mar July Aug Sep Oct Nov Jan Mar July Aug Sep Oct Nov Dec
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
no time object in the report, or if only objects higher than
quarter, 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 results to this level
of detail
18. Quarter Ago Quarter To Date Sum (Qago QTD)
Year N - 1 Year NQ1 Q3 Q4 Q1 Q3 Q4
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“)
Year N - 1 Year NQ1 Q3 Q4 Q1 Q3 Q4
Jan Feb Mar July Aug Sep Oct Nov Jan Mar July Aug Sep Oct Nov Dec
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
Year N - 1 Year NQ1 Q3 Q4 Q1 Q3 Q4
Jan Feb Mar July Aug Sep Oct Nov Jan Mar July Aug Sep Oct Nov Dec
Monthly Avg
JunAprDec
Q2
Feb May
Q2
Apr May Jun
Year N - 1 Year NQ1 Q3 Q4 Q1 Q3 Q4
Jan Feb Mar July Aug Sep Oct Nov Jan Mar July Aug Sep Oct Nov Dec
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
Year N - 1 Year NQ1 Q3 Q4 Q1 Q3 Q4
Jan Feb Mar July Aug Sep Oct Nov Jan Mar July Aug Sep Oct Nov Dec
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
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.
24. Rolling 3 Months Monthly Avg
27. Rolling 6 Months Monthly Avg
Year N - 1 Year NQ1 Q3 Q4 Q1 Q3 Q4
Jan Feb Mar July Aug Sep Oct Nov Jan Mar July Aug Sep Oct Nov Dec
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)
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.
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
Year N - 1 Year NQ1 Q3 Q4 Q1 Q3 Q4
Jan Feb Mar July Aug Sep Oct Nov Jan Mar July Aug Sep Oct Nov Dec
Sum of metric / Avg number of days
Q2
Apr May Jun JunAprDec
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)
Year N - 1 Year NQ1 Q3 Q4 Q1 Q3 Q4
Jan Feb Mar July Aug Sep Oct Nov Jan Mar July Aug Sep Oct Nov Dec
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)
Year N - 1 Year NQ1 Q3 Q4 Q1 Q3 Q4
Jan Feb Mar July Aug Sep Oct Nov Jan Mar July Aug Sep Oct Nov Dec
Sum [ ( (Y - X) / X ) - 1 ] *100
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 :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
Year N - 1 Year NQ1 Q3 Q4 Q1 Q3 Q4
Jan Feb Mar July Aug Sep Oct Nov Jan Mar July Aug Sep Oct Nov Dec
Sum Compounded yearly % growth of (X to Y)
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 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)
Year N - 1 Year NQ1 Q3 Q4 Q1 Q3 Q4
Jan Feb Mar July Aug Sep Oct Nov Jan Mar July Aug Sep Oct Nov Dec
Mago MTD = X MTD = Y Value returned : [ (Y/X) -1 ] x 100
Q2
Apr May Jun JunAprDec
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
Year N - 1 Year NQ1 Q3 Q4 Q1 Q3 Q4
Jan Feb Mar July Aug Sep Oct Nov Jan Mar July Aug Sep Oct Nov Dec
Qago Value = X Current Value = Y Value returned : Y - X
JunAprDec
Q2
Feb May
Q2
Apr May Jun
Year N - 1 Year NQ1 Q3 Q4 Q1 Q3 Q4
Jan Feb Mar July Aug Sep Oct Nov Jan Mar July Aug Sep Oct Nov Dec
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
Year N - 1 Year NQ1 Q3 Q4 Q1 Q3 Q4
Jan Feb Mar July Aug Sep Oct Nov Jan Mar July Aug Sep Oct Nov Dec
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)
Year N - 1 Year NQ1 Q3 Q4 Q1 Q3 Q4
Jan Feb Mar July Aug Sep Oct Nov Jan Mar July Aug Sep Oct Nov Dec
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
Year N - 1 Year NQ1 Q3 Q4 Q1 Q3 Q4
Jan Feb Mar July Aug Sep Oct Nov Jan Mar July Aug Sep Oct Nov Dec
Qago Value = X Current Value = Y Value returned : Y - X
JunAprDec
Q2
Feb May
Q2
Apr May Jun
Year N - 1 Year NQ1 Q3 Q4 Q1 Q3 Q4
Jan Feb Mar July Aug Sep Oct Nov Jan Mar July Aug Sep Oct Nov Dec
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)
Year N - 1 Year NQ1 Q3 Q4 Q1 Q3 Q4
Jan Feb Mar July Aug Sep Oct Nov Jan Mar July Aug Sep Oct Nov Dec
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
Year N - 1 Year NQ1 Q3 Q4 Q1 Q3 Q4
Jan Feb Mar July Aug Sep Oct Nov Jan Mar July Aug Sep Oct Nov Dec
Week ago Value = X Current Value = Y Value returned : [ (Y - X)-1 ] /100
Q2
Apr May Jun JunAprDec
Q2
Feb May
Year N - 1 Year NQ1 Q3 Q4 Q1 Q3 Q4
Jan Feb Mar July Aug Sep Oct Nov Jan Mar July Aug Sep Oct Nov Dec
Week ago Value = X Current Value = Y Value returned : Y - X
Q2
Apr May Jun JunAprDec
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
Year N - 1 Year NQ1 Q3 Q4 Q1 Q3 Q4
Jan Feb Mar July Aug Sep Oct Nov Jan Mar July Aug Sep Oct Nov Dec
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
Year N - 1 Year NQ1 Q3 Q4 Q1 Q3 Q4
Jan Feb Mar July Aug Sep Oct Nov Jan Mar July Aug Sep Oct Nov Dec
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
Year N - 1 Year NQ1 Q3 Q4 Q1 Q3 Q4
Jan Feb Mar July Aug Sep Oct Nov Jan Mar July Aug Sep Oct Nov Dec
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
Year N - 1 Year NQ1 Q3 Q4 Q1 Q3 Q4
Jan Feb Mar July Aug Sep Oct Nov Jan Mar July Aug Sep Oct Nov Dec
Sum (Y - X) / number of months (decimal) between last day and first day
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 :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
Year N - 1 Year NQ1 Q3 Q4 Q1 Q3 Q4
Jan Feb Mar July Aug Sep Oct Nov Jan Mar July Aug Sep Oct Nov Dec
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
Year N - 1 Year NQ1 Q3 Q4 Q1 Q3 Q4
Jan Feb Mar July Aug Sep Oct Nov Jan Mar July Aug Sep Oct Nov Dec
Sum (Y - X) / number of years (decimal) between last day and first day
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 :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
Year N - 1 Year NQ1 Q3 Q4 Q1 Q3 Q4
Jan Feb Mar July Aug Sep Oct Nov Jan Mar July Aug Sep Oct Nov Dec
Sum (Y - X) / number of years (decimal) between last day and first day
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
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
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 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
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 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 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
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