Hands-On Lab: CA PPM Data Warehouse

ca Intellicenter
Hands-On Lab:
CA PPM Data Warehouse
Bryan Temple
Session Number ICX07L #CAWorld
CA PPM
CA Technologies

Hands On Lab: CA PPM Data Warehouse

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© 2014 CA. ALL RIGHTS RESERVED.
Abstract
Having the right data at your fingertips is critical for making decisions in the new world of software-driven business. Facilitating access to project and resource data is a key focus area for CA Project & Portfolio Management (CA PPM). Review the presentation from this hands-on lab to learn the details of the new CA PPM data warehouse.
Bryan Temple
CA Technologies
Sr. Engineering Services Architect

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Agenda
DATA WAREHOUSE OVERVIEW AND ARCHITECTURE
LOADING THE DATA WAREHOUSE
DATA WAREHOUSE DEMO
DATA WAREHOUSE STANDARDS
DATA WAREHOUSE DIMENSIONS AND FACTS
DATA WAREHOUSE ROADMAP
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3
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5
6
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DATA WAREHOUSE SETUP
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Subject Oriented
Modeled on the STAR schema and includes the following master objects: Investment (All Types), Resource, Portfolio and Timesheet
Integrated
Consistent naming conventions, formats and encoding structures
Non-Volatile
Separate schema optimized for business decision making and analytics
Time Variant
Predefined, yet configurable, time slices
–1 year back/forward for weekly
–3 years back/forward for monthly
Data Warehouse
Data Warehouse Overview

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Dimensions are the descriptive fields on an object (Examples: Investment ID, Investment Name, Investment Manager, etc.).
Facts are the metrics on an object (Examples: Total Cost, Actual Hours, etc.).
Star Schema is a type of database design. A simple Star would have a fact table with a few direct links to dimension tables.
A Snowflake is a dimension table that can be indirectly linked to a fact table.
Common Terms

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CA PPM Application
CA PPM Reporting Architecture
Load Data
Warehouse job
(embedded Pentaho Data Integration)
Data Warehouse
Jaspersoft Reports, Ad Hoc Views & Domains
CA PPM Database
oLightweight, drag and drop business user reporting capability
oOut of the box reports and domains for Investments, Resources, Financials and Timesheets.
oPentaho Data Integrator is embedded within CA PPM. The data transformation and load runs as a CA PPM job.
oThe Data Warehouse is modeled on a STAR schema, with Dimensions covering the major areas in CA PPM and their associated Facts.

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Reports and portletsrun against transactional data.
–The data warehouse schema resides on another database server taking the stress off the transactional CA PPM database.
Relational database makes queries very complex.
–The data warehouse carries keys and descriptive values in the dimension tables so fewer joins are required. Facts are combined into summary and period tables.
Finding the data with 1000+ tables
–With the exception of configuration and meta tables, the data warehouse tables are ‘user friendly’ to report against.
Table name inconsistencies
–Similar tables are grouped together by the table prefix and the names are very descriptive.
Addresses Reporting Challenges

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Time slice requests
–Specific time slice requests are set up to populate the data warehouse. Defaults are set but can be modified.
Column naming
–Columns are consistently named across tables.
Resource ID versus user ID
–In the CA PPM database, manager points to the user ID and resource points to the resource ID, or code, which makes it inconsistent. In the data warehouse, resource columns (manager_key, resource_key, etc.) are always the resource_key.
Date/time storage
–In the CA PPM database, the finish/end dates do not always match those displayed in CA PPM. Database functions in queries must be leveraged to determine the correct date. In the data warehouse, the finish/end dates always match CA PPM.

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Code versus ID
–In the CA PPM financial tables, codes are used instead of IDs. The data warehouse always uses the numeric key of the dynamic lookups.
Database tuning
–Since the data warehouse is separate from the CA PPM database, the database can be tuned differently for optimal performance.
Studio attributes are not available in Business Objects Universes without customization.
–The data warehouse is extendable without customization. A flag has been added to Studio objects and attributes that control whether the data warehouse load job automatically adds custom objects and attributes.

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Data Warehouse Setup
The CSA data warehouse properties allow you to configure the basic data warehouse credentials and settings. This database can be on the same physical server, a different instance on the same server, or on a different server. This depends on the size of the CA PPM database.
CSA Configuration

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Administration/General Settings/System Options:
–The languages selected determine which localizations are included in the data warehouse (more languages means more disk).
–The entity chosen determines which fiscal periods are used when aggregating data.
–The entity selected does not restrict the investment data included in the data warehouse to that entity.
System Options

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Time slices with the Data Warehouse flag checked determine the ranges for the facts in the data warehouse.
Defaults
–Months: 3 years back and forward
–Weeks:1 year back and forward
–Daily: 1 year back and forward
–Fiscal:3 years back and forward
Verify these ranges work for your company. If not, you can update the From Date and Periods in the time slice request.
All monthly time slices should have the same From Date and Number of Periods. (The same applies for Weekly, etc.).
Time Slice Requests

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Custom objects can be included in the data warehouse via Studio. Simply check the box for ‘Include in the Data Warehouse.’
The attributes of the object also need to be selected manually for inclusion in the data warehouse.
Custom Objects

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Custom attributes can be included in the data warehouse via Studio. Simply check the box for ‘Include in the Data Warehouse.’
Boolean, String, Number, Money, Date, Calculated, Formula, Lookup and Multi-Valued Lookup attributes are supported. Calendar and Fiscal TSVs are supported for relevant attributes.
Custom Attributes

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Loading the Data Warehouse
Two jobs exist in CA PPM for loading the data warehouse. These jobs are independent of one another.
Load the Data Warehouse Security Privileges: Loads the security for investments and resources. The table is truncated and rebuilt each time.
Load the Data Warehouse: This is the core job that analyzes the meta data, creates new objects and attributes (if needed), loads the dimensions, lookups and facts.
Parameter:
Data Warehouse Full Reload –If checked, this will truncate and rebuild the data warehouse. Otherwise, only incremental changes are processed.
ETL Jobs

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Loading the Data Warehouse
Reports and Jobs
Load the Data Warehouse Security Privileges.
–Loads the investment/resource security for the system users
–Separate job –the security job is not incremental, the table gets truncated and rebuilt.
Load the Data Warehouse.
–Loads the complete data warehouse
–ETL job steps:
Runs scripts the data warehouse is dependent upon: calendar population,WBS hierarchy,investment hierarchy
Builds the meta data that determines the data warehouse structure
Checks/corrects any data warehouse structure changes
Loads/updates the lookup tables
Loads/updates the dimension tables
Loads/updates the fact tables
ETL Jobs

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Data Warehouse Table Prefix Standards
DWH_CFG-Configuration tables used to supply the data warehouse log and audit information
DWH_CMN-Common database objects used across most areas
DWH_CMP-Company database objects
DWH_FIN-Financial management database objects
DWH_INV-Investment management database objects
DWH_LKP-Lookup database objects
DWH_META-Meta data tables that help determine the data warehouse structure
DWH_ODF-Custom database objects
DWH_PFM-Portfolio management database objects
DWH_RES-Resource management database objects
DWH_RIM-Risk, issue and change management database objects
DWH_TME-Time management database objects
DWH_X-Internal database objects used to help populate the fact tables

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Data Warehouse Static Lookup Standards
Static Lookups in CA PPM can be confusing because they are stored in one table and you need to qualify them by the lookup_type. In the data warehouse, each lookup is its own table. The lookup values are stored in the different languages chosen for the data warehouse. If, for example, the data warehouse is stored in English and Spanish, two records exist for each lookup value.
Column
Data Type
Description
[lookup_name]_key
Number or
Varchar(30)
The key value of the lookup. If the hiddenkey in CA PPM is lookup_enum, then the key in the data warehouse will be populated with the lookup_enum. Same for lookup_code.
Example: investment_status_key
language_code_key
Number
ID from the CA PPM languages table
language_code
Varchar(30)
Unique language code from the CA PPM languagestable
[lookup_name]
Varchar(255)
Descriptivename of the lookup: Example: investment_status
sort_order
Number
Sort order is used tospecify a specific order in which the user wants to see the values
is_active
Number
Is the current lookup value active
clarity_updated_date
Date
Last time the record was updated in CA PPM
dw_updated_date
Date
Last time the record was updated in the data warehouse

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Data Warehouse Dynamic Lookup Standards
Dynamic Lookups in CA PPM are determined by NSQL statements. In the data warehouse, a table exists for each dynamic lookup that is used. Each table structure can be different depending on the lookup. If the lookup is language dependent, then language_code_keyand language_codewill be stored. Otherwise, there will be one record per value.
Column
Data Type
Description
[lookup_name]_key
…
The key value of the dynamic lookup. Depends on the NSQL’s hidden value
language_code_key
Number
ID from the CA PPM languages table if applicable
language_code
Varchar(30)
Unique language code from the CA PPM languagestable if applicable
[lookup_name]
…
Descriptivename of the lookup: Example: investment_status
…
…
Miscellaneous columns specific to the lookup
clarity_updated_date
Date
Last time the record was updated in CA PPM
dw_updated_date
Date
Last time the record was updated in the data warehouse
Basic Dynamic Lookup Structure

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Data Warehouse Dimension Standards
Dimension Language Tables
–If the dimension has language dependent lookups, a table ending with ‘_ln’ carries the language dependent descriptions.
Below is a simple example using ‘Investment_status’. The key is carried in the investment table while the language dependent description is carried in the investment language table.

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Data Warehouse Fact Standards
•Fact table names end with ‘_facts’.
•Fact tables with ‘_period_’ in the name store facts by defined periods.
•Fact tables with ‘_summary_’ in the name store summarized facts.
•The fact table keys all have referential integrity.
•Calculated facts are stored in the tables to help with consistency.
•Summary rollups exist in the data warehouse.
•Assignments roll up to tasks, tasks roll up to investments.
•Data warehouse time slice requests aggregate the data into weekly, monthly and fiscal periods.
•Fiscal aggregation is new to the data warehouse.

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Fact Period Aggregation Tables
Fact Description
FactTable
Aggregation
Financial Transaction Facts
dwh_fin_transaction_facts
Daily
Time Entry Facts
dwh_tme_entry_facts
Daily
Financial Benefit Facts
dwh_fin_benefit_period_facts
Fiscal Period
Financial Plan Facts
dwh_fin_plan_period_facts
Fiscal Period
Task Assignment Facts
dwh_inv_assign_period_facts
Fiscal Period,Weekly, Monthly
Investment Task Facts
dwh_inv_task_period_facts
Investment Team Facts
dwh_inv_team_period_facts
Investment facts
dwh_inv_period_facts
Resource Facts
dwh_res_period_facts

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Fact Summary Tables and Internal Fact Tables
•Summary tables exist for many of the facts.
•If matching summary numbers to period facts, qualify the period facts by a period type.
•Internal Fact Tables start with a ‘dwh_x_’. These tables are used to populate the period and summary fact tables in the most efficient way. They are not for user consumption.
Fact Description
FactTable
Financial benefit facts
dwh_fin_benefit_summary_facts
Financial plan facts
dwh_fin_plan_summary_facts
Task assignment facts
dwh_inv_assign_summary_facts
Investment task facts
dwh_inv_task_summary_facts
Investment team facts
dwh_inv_team_summary_facts
Investment facts
dwh_inv_summary_facts

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Example: Investment Period Facts Table
•The Investment period facts table contains over 110 different facts.
•Investment_keyis a foreign key to the investment table.
•Period_keyis a foreign key to the periodic table.
•Dw_updated_dateis the last date this record was updated.

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Example: Investment Team Table
DWH_INV_TEAM
DWH_INV_TEAM_LN
Table contains language translations.
If the data warehouse is set up for English and Spanish, there would be two records for every one record in dwh_inv_team.

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Example: Old Team Query (CA PPM Database)
SELECT m.full_name investment_manager,
i.name investment_name,
r.full_name resource_name,
rr.full_name role_name,
tl.name booking_status,
t2.name request_status,
s1.slice_date period_start_date,
NVL(s1.slice,0) alloc_hours,
NVL(s2.slice,0) alloc_cost
FROM inv_investments i
INNER JOIN prTeam t ON i.id = t.prProjectID
LEFT OUTER JOIN srm_resources m ON i.manager_id = m.user_id
LEFT OUTER JOIN srm_resources r ON t.prResourceID = r.id
LEFT OUTER JOIN srm_resources rr ON t.prRoleID = rr.id
LEFT OUTER JOIN cmn_lookups_v tl ON t.prBooking = tl.lookup_enum
AND tl.lookup_type = 'BOOKING_STATUS_LIST'
AND tl.language_code = 'en'
LEFT OUTER JOIN cmn_lookups_v t2 ON t.prBooking = t2.lookup_enum
AND t2.lookup_type = 'REQUEST_STATUS_LIST'
AND t2.language_code = 'en'
LEFT OUTER JOIN prj_blb_slices s1 ON t.prID = s1.prj_object_id
AND s1.slice_request_id IN (SELECT id
FROM prj_blb_slicerequests
WHERE request_name = 'MONTHLYRESOURCEALLOCCURVE')
LEFT OUTER JOIN prj_blb_slices s2 ON t.prID = s1.prj_object_id
AND s1.slice_request_id IN (SELECT id
FROM prj_blb_slicerequests
WHERE request_name = 'team::alloccost_curve::dwh_month')
AND s1.slice_date = s2.slice_date
WHERE s1.slice_date BETWEEN TO_DATE('01/01/2014','MM/DD/YYYY') AND TO_DATE('12/31/2014','MM/DD/YYYY')
•Need to know lookup types
•Inconsistent joins between tables (resource_idor user_id)
•Inconsistent column names
•Multiple joins to the same table for different information
•Not intuitive

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Example: New Team Query (Data Warehouse)
•No need to join to lookup tables
•Consistent joins between tables (always resource_id)
•Consistent column names
•Intuitive
SELECT i.investment_manager,
i.investment_name,
t.resource_name,
t.role_name,
tl.booking_status,
tl.request_status,
p.period_start_date,
tf.alloc_hours,
tf.alloc_cost
FROM dwh_inv_team t
INNER JOIN dwh_inv_team_ln tl ON t.team_key = tl.team_key
INNER JOIN dwh_inv_investment i ON t.investment_key = i.investment_key
INNER JOIN dwh_inv_team_period_facts tf ON t.team_key = tf.team_key
INNER JOIN dwh_cmn_period p ON tf.period_key = p.period_key
WHERE SYSDATE BETWEEN p.year_start_date AND p.year_end_date
AND p.period_type_key = 'MONTHLY'
AND tl.language_code = 'en'

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Financial Plan Facts
•Combines the periodic plan facts
•Calculates forecast facts
•Numerous slices used to produce these facts
•Summarizes the periodic plan facts
•Calculates forecast facts

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Investment Team Facts
•Combines the team facts together by period
•Calculates costs
•Summarizes the periodic team facts

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Task Assignment Facts
•Combines the assignment facts by period
•Calculates costs
•Summarizes the periodic assignment facts

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Investment Task Facts
•Summarizes assignment facts to the task by period
•Formulas calculated for consistency
•Summarizes task facts
•Contains earned value information

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Investment Period Facts
•Summarizes investment period facts
•Comprehensive investment data

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Resource Period Facts
•Summarizes resource period facts
•Comprehensive resource data

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Data Warehouse Items Included
Change Request Management
Issue Management
WBS Structure
ExchangeRates
OBSHierarchy
WIP Financial Transactions
Financial Benefit Plans
Portfolio (High Level)
Facts by Weekly/Monthly/Fiscal Period
Financial Budget/Cost Plans
Resource Assignments
Summary Facts
Investment –Applications
Resources
All Associated Lookups
Investment –Assets
ResourceUser Security
CustomAttributes
Investment –Ideas
Risk Management
TSV Values
Investment –Other Work
Team Allocations
Summary InvestmentEarned Value Data
Investment–Products
Time Entry
Current Baseline Data
Investment -Projects
Time EntryNotes
PMO Accelerator
Investment –Services
Time Sheets
DBLINKfor Missing Data
Investment User Security
Time Sheet Notes
New Cost Slices –ETC, Allocations

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Data Warehouse Items Under Consideration
Additional Objects
–Baseline History
–Earned Value History
–Incidents
–Portfolio Management
–Resource Skills
–Scenarios
Snapshots for Trending
Slowly Changing Dimensions

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For More Information
To learn more about Management Cloud, please visit:
http://bit.ly/1wEnPhz
Insert appropriate screenshot and textoverlayfrom following“More Info Graphics” slide here; ensure it links to correct page
Management Cloud

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For Informational Purposes Only
© 2014 CA. All rights reserved. All trademarks referenced herein belong to their respective companies. The presentation provided at CA World 2014 is intended for information purposes only and does not form any type of warranty. Some of the specific slides withcustomer references relate to customer's specific use and experience of CA products and solutions so actual results may vary.
Certain information in this presentation may outline CA’s general product direction. This presentation shall not serve to (i) affectthe rights and/or obligations of CA or its licensees under any existing or future license agreement or services agreement relating to any CA software product; or (ii) amend any product documentation or specifications for any CA software product. This presentation is based oncurrent information and resource allocations as of November 9, 2014 and is subject to change or withdrawal by CA at any time withoutnotice. The development, release and timing of any features or functionality described in this presentation remain at CA’s sole discretion.
Notwithstanding anything in this presentation to the contrary, upon the general availability of any future CA product release referenced in this presentation, CA may make such release available to new licensees in the form of a regularly scheduled major product release. Such release may be made available to licensees of the product who are active subscribers to CA maintenance and support, on a whenand if- available basis. The information in this presentation is not deemed to be incorporated into any contract.
Terms of this Presentation

Hands-On Lab: CA PPM Data Warehouse

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