This workshop will enable users on the IBM Rational Insight solution and how to customize it to achieve their specific business needs. The first segment will provide an overview of the Insight offering. The next segment will provide an understanding of framework data modeling and a walkthrough of the customization process. The next segment will be a series of hands-on labs that will allow users to assume the role of an Insight developer and report author. Users will experience and end to end scenario in creating a custom metric and report based on an IBM Rational Team Concert custom attribute, however the process is applicable to any data source.

1. © 2014 IBM Corporation
WRK-1951
Unleashing Metrics Outside the
Box: Customizing Your Rational
Insight Deployment
Marc J. Nehme
Reporting & Analytics Engineer, Certified IT Specialist
IBM Rational
Paulo Lacerda Neto
IT Specialist
IBM Rational

2. Accelerating Product and Service Innovation
• Marc J. Nehme
– Twitter: marc_nehme
– LinkedIn
– Blog: marcnehme.com/blog
– mjnehme@us.ibm.com
• Paulo Cezar Lacerda Neto
– Twitter: pclacerda
– LinkedIn
– placerda@br.ibm.com
IBM Presenter Contact Information
2

3. Accelerating Product and Service Innovation
Performance is based on measurements and projections using standard IBM benchmarks in a controlled environment. The
actual throughput or performance that any user will experience will vary depending upon many factors, including
considerations such as the amount of multiprogramming in the user’s job stream, the I/O configuration, the storage
configuration, and the workload processed. Therefore, no assurance can be given that an individual user will achieve results
similar to those stated here.
Please note
IBM’s statements regarding its plans, directions, and intent are subject to change
or withdrawal without notice at IBM’s sole discretion.
Information regarding potential future products is intended to outline our general
product direction and it should not be relied on in making a purchasing decision.
The information mentioned regarding potential future products is not a commitment,
promise, or legal obligation to deliver any material, code or functionality.
Information about potential future products may not be incorporated into any
contract. The development, release, and timing of any future features or
functionality described for our products remains at our sole discretion.

4. Accelerating Product and Service Innovation
• Introduction
• Reporting Challenges
• Rational Insight
• How Data Flows in Insight
• Insight Components
• Hands-on Labs
• Conclusion
Agenda
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5. Accelerating Product and Service Innovation
• Organizational process
– Different departments, different processes, not unified
– Various disparate data sources
– Roll-up of data doesn't compare well
– Lack of visibility into organization metrics
• Manual process
– High Level of effort / Time consuming = $
– Inaccurate / unreliable data
– Outdated metrics
• Individual product reporting
– Lack of functionality/customization
– Training & Resources = $
Enterprise Reporting Challenges
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6. Accelerating Product and Service Innovation
• IBM Rational’s Performance Measurement Offering
• An Enterprise Reporting solution that gets consistent metrics from across an
organization to objectively measure status and progress
• Measure program, project, and team-level progress against pre-defined business
objectives through multiple views
• Identify high-priority items and Become notified of high-severity items
• Based on Cognos 10 BI platform, industry proven technology
• Report & Dashboard elements viewable via
– Web browser
– iPads
– Mobile phones
– MS Office, PDF
– Any tool that is JSR168 compliant
What is Rational Insight?
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Insight versus RRDI versus Cognos
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Rational Insight High-Level Architecture
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• Bridges XML and the relational representation of data
– REST service/XML used by Jazz tools to publish data
– Relational: efficient for querying by ETL jobs
What does the XDC (XML Data Configuration) tool do?
10
Design Time
XML Data configuration
Maps XML data source
structure to relational format
Run time
XML ODBC Driver
Exposes XML Data as
relational result sets

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ETL: Anatomy of a fact build in Data Manager
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ETL: Anatomy of a fact build in Data Manager
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Extract Transform Load

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What is the Data Warehouse?
13
• The Data warehouse is a database that consists of several tables and views that stores a high
volume of data from source systems. This consists of operational data and trend data
(summarized and aggregated data over time) to be used by report designers to create the
reports and dashboards.
• The tables are organized in the following schemas:
• RIODS: An Operational Data Store that contains data extracted from multiple sources
structured is integrated in a relational model.
• RIDW: Contains the star schema metrics, dimension tables and historical data. The RIDW
schema contains aggregated data, making certain types of reports more efficient. The
RIDW schema is built by an ETL operation sourced with data in the RIODS schema.
• RICALM: Consists of tables and views that are designed to stage and integrate the data
collected from the Collaborative Lifecycle Management applications (RTC, RQM, RRC)
into the Rational Insight ETL.
• RIASSET: Consists of tables used for the asset management operational data store.
• RIBA: Consists of tables for the Business Analytics area of the database.
• RISCHK: Consists of tables for the MCIF self-check operational data store.

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Reporting metadata
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Query Studio lets users
with little or no training
quickly design, create and
save basic reports to meet
reporting needs not covered
by the professional reports
designed in Report Studio.
Report Studio lets report
authors create, edit, and
distribute a wide range of
professional reports.
Framework Manager lets
users create metadata to
shield the report designer
from the data warehouse
structure allowing for better
alignment to business needs.

15. Accelerating Product and Service Innovation
• Dimensions are the core components or
categories of a business. This is
anything that decision makers want to
analyze in reports.
• Example: a software development
organization may categorize its defect
data by project, defect severity and
time.
• This example organization has three
dimensions: Project, Defect Severity
and Date. The measure, such as
number of open defects, lie at the
intersection of these dimensions.
– Illustration shows these
dimensions as the axis of a three-
dimensional space. The cube at
the center of this space
represents 100 severity 1 defects
in Project ABC in November.
Dimensional Modeling : Aggregating Data Along Dimensions
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Severity 1 Defects
found in Project ABC
in November
Time
Project ABC
Project
Defect Severity
November
Severity 1

16. Accelerating Product and Service Innovation
• Designing the Star Schema
– http://ciobriefings.com/Publications/WhitePapers/DesigningtheStarSchemaDat
abase/tabid/101/Default.aspx
• Insight Data Model
– http://pic.dhe.ibm.com/infocenter/rentrpt/v1r1m1/topic/com.ibm.rational.raer.mo
dels.doc/topics/c_data_models_ovr.html
• CLM Data Dictionaries
– CCM: https://jazz.net/help-
dev/clm/topic/com.ibm.jazz.reports.doc/topics/ccm.html
– QM: https://jazz.net/help-dev/clm/topic/com.ibm.jazz.reports.doc/topics/qm.html
– RM: https://jazz.net/help-dev/clm/topic/com.ibm.jazz.reports.doc/topics/rm.html
References
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17. 17
Unleashing Metrics Outside the
Box: Customizing Your Rational
Insight Deployment
Hands-on Labs

18. Accelerating Product and Service Innovation
To configure Rational Insight to create a custom trend report to show the
number of Defects submitted over time by Root Cause (a custom attribute that
resides in Rational Team Concert).
Objective
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• Lab 1: Create a Trend Report using Report Studio
• Lab 2: Modify the Insight ETL using Data Manager
• Lab 3: Modify the Insight Data Model using Framework Manager
• Lab 4: Modify an existing Trend Report using Report Studio
Lab Exercises
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• XDC Tool
– Understand RTC Reportable REST API Data mapping in XDC Tool
• Data Manager
– Understand tables in ODS and fact builds that load Data from RTC
into ODS
– Add a fact build to load the new dimension data in ODS
– Understand dimension and fact tables in DW
– Add dimension build to load dimension data in RIDW
– Change current fact build to include the new dimension when
aggregating the metric
• Framework Manager
– Understand FM model
– Add new dimension in FM Model and relate it to existing Star
Schema definition
• Report Studio
– Create a report using the new dimension
Adding a dimension (custom RTC attribute) to an
existing fact
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Developer
Report
Author

21. Lab 1
Create a Trend Report
using Report Studio
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22. Accelerating Product and Service Innovation
IBM Cognos Report Studio
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23. 23
Lab 2
Modify the Insight ETL using
Data Manager

24. Accelerating Product and Service Innovation
Lab sections:
2.1 Prepare the data warehouse
2.2 Create a reference dimension in Data Manager
2.3 Create a dimension build in Data Manager
2.4 Add the new dimension build to the existing job stream
2.5 Create a lookup for the new dimension
2.6 Add the "Root Cause" dimension to the existing fact build
Modify the Insight ETL using Data Manager
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25. Accelerating Product and Service Innovation
2.1 Prepare the Data Warehouse
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Create dimension table and add default values
CREATE TABLE RIDW.D_ROOT_CAUSE (ROOT_CAUSE_ID INTEGER NOT
NULL,NAME VARCHAR(255), PRIMARY KEY(ROOT_CAUSE_ID)) IN
VSTR_32K INDEX IN VSTR_IDX;
INSERT INTO RIDW.D_ROOT_CAUSE (ROOT_CAUSE_ID, NAME) VALUES
(-1, 'Info not available');
Add dimension relationship in fact table
ALTER TABLE RIDW.F_REQUEST_CREATION_DT_METRICS ADD
ROOT_CAUSE_ID INTEGER NOT NULL DEFAULT -1;
ALTER TABLE RIDW.F_REQUEST_CREATION_DT_METRICS ADD
FOREIGN KEY (ROOT_CAUSE_ID) REFERENCES
RIDW.D_ROOT_CAUSE(ROOT_CAUSE_ID);

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2.2 Create a Reference Dimension
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A Reference Dimension is a structure that represents a business dimension and contains hierarchies and
lookups.

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2.3 Create a Dimension Build in Data Manager
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A Dimension Build is an ETL element that loads dimensional data (eg Root Cause, Priority) into the Data
Warehouse.

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2.4 Add the new Dimension Build to the Existing Job Stream
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A Job Stream is an ETL element that groups fact and dimension builds in an organized sequence

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2.5 Create a Lookup for the new Dimension
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A Lookup is a simple, single-level reference structure commonly used to retrieve specific values from a
reference structure based on a lookup key.

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2.6 Add the "Root Cause" Dimension to the Fact Build
30
Lookup_Root_Cause

31. Lab 3
Modify the Insight Data
Model using Framework
Manager
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32. Accelerating Product and Service Innovation
3.1 Launching your Framework Manager project
3.2 Refresh existing fact table to realize ETL changes
3.3 Import the new dimension database table
3.4 Create a Query Subject for the new Dimension
3.5 Refresh existing Data Mart to realize ETL changes
3.6 Create the relationship between the Query Subjects
3.7 Convert the new Query Subject to a Regular Dimension
3.8 Convert the Data Mart Query Subject to a Measure Dimension
3.9 Publish Framework Manager Package to the Report Server
Modify the Insight Data Model using Framework Manager
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3.1 Launching Your Framework Manager Project
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3.2 Refresh existing fact table to realize ETL changes
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3.3 Import the new Dimension table
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3.4 Create a Query Subject for the new Dimension
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A Query Subject is a model element that represents a database table.

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3.5 Refresh existing Data Mart to realize ETL changes
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3.6 Create the relationship between the Query Subjects
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39
3.7 Convert the new Query Subject to a Regular Dimension

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40
3.8 Convert the Data Mart Query Subject to a Measure Dimension

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41
3.9 Publish the Framework Manager Package to the Report Server

42. Lab 4
Modify an Existing Trend
Report using Report Studio
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43. Accelerating Product and Service Innovation
Acknowledgements and Disclaimers
© Copyright IBM Corporation 2014. All rights reserved.
– U.S. Government Users Restricted Rights - Use, duplication or disclosure restricted by GSA ADP Schedule Contract
with IBM Corp.
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44. Accelerating Product and Service Innovation
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