Innovate 2014 - Customizing Your Rational Insight Deployment (workshop)

© 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

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

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.

• Introduction
• Reporting Challenges
• Rational Insight
• How Data Flows in Insight
• Insight Components
• Hands-on Labs
• Conclusion
Agenda
4

• 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– Outdated metrics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
5

• 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?
6

Insight versus RRDI versus Cognos
7

Rational Insight High-Level Architecture
8

• 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

ETL: Anatomy of a fact build in Data Manager
11

ETL: Anatomy of a fact build in Data Manager
12
Extract Transform Load

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.

Reporting metadata
14
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.

• 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
15
Severity 1 Defects
found in Project ABC
in November
Time
Project ABC
Project
Defect Severity
November
Severity 1

• 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
16

17
Unleashing Metrics Outside the
Box: Customizing Your Rational
Insight Deployment
Hands-on Labs

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
18

• 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
19

• 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
20
Developer
Report
Author

Lab 1
Create a Trend Report
using Report Studio
21

IBM Cognos Report Studio
22

23
Lab 2
Modify the Insight ETL using
Data Manager

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
24

2.1 Prepare the Data Warehouse
25
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);

2.2 Create a Reference Dimension
26
A Reference Dimension is a structure that represents a business dimension and contains hierarchies and
lookups.

2.3 Create a Dimension Build in Data Manager
27
A Dimension Build is an ETL element that loads dimensional data (eg Root Cause, Priority) into the Data
Warehouse.

2.4 Add the new Dimension Build to the Existing Job Stream
28
A Job Stream is an ETL element that groups fact and dimension builds in an organized sequence

2.5 Create a Lookup for the new Dimension
29
A Lookup is a simple, single-level reference structure commonly used to retrieve specific values from a
reference structure based on a lookup key.

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

Lab 3
Modify the Insight Data
Model using Framework
Manager
31

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
32

3.1 Launching Your Framework Manager Project
33

3.2 Refresh existing fact table to realize ETL changes
34

3.3 Import the new Dimension table
35

3.4 Create a Query Subject for the new Dimension
36
A Query Subject is a model element that represents a database table.

3.5 Refresh existing Data Mart to realize ETL changes
37

3.6 Create the relationship between the Query Subjects
38

39
3.7 Convert the new Query Subject to a Regular Dimension

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

41
3.9 Publish the Framework Manager Package to the Report Server

Lab 4
Modify an Existing Trend
Report using Report Studio
42

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.
IBM, the IBM logo, ibm.com, IBM Rational, IBM Rational Insight, IBM Cognos are trademarks or registered trademarks of International
Business Machines Corporation in the United States, other countries, or both. If these and other IBM trademarked terms are marked on
their first occurrence in this information with a trademark symbol (® or ™), these symbols indicate U.S. registered or common law
trademarks owned by IBM at the time this information was published. Such trademarks may also be registered or common law
trademarks in other countries. A current list of IBM trademarks is available on the Web at “Copyright and trademark information” at
www.ibm.com/legal/copytrade.shtml
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Availability. References in this presentation to IBM products, programs, or services do not imply that they will be available in all
countries in which IBM operates.
The workshops, sessions and materials have been prepared by IBM or the session speakers and reflect their own views. They are
provided for informational purposes only, and are neither intended to, nor shall have the effect of being, legal or other guidance or advice
to any participant. While efforts were made to verify the completeness and accuracy of the information contained in this presentation, it is
provided AS-IS without warranty of any kind, express or implied. IBM shall not be responsible for any damages arising out of the use of,
or otherwise related to, this presentation or any other materials. Nothing contained in this presentation is intended to, nor shall have the
effect of, creating any warranties or representations from IBM or its suppliers or licensors, or altering the terms and conditions of the
applicable license agreement governing the use of IBM software.
All customer examples described are presented as illustrations of how those customers have used IBM products and the results they may
have achieved. Actual environmental costs and performance characteristics may vary by customer. Nothing contained in these
materials is intended to, nor shall have the effect of, stating or implying that any activities undertaken by you will result in any specific
sales, revenue growth or other results.

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