1. Business Data Transformation and Consolidation for a Global Energy
Services Company
John Perkins
Director, ChainSys Corporation
john.perkins@chain-sys.com
Introduction
Business and organizational changes have significant ramifications on data requirements for a global company.
Combine these changes with radical business process change and you have a functional and technical problem
with increased complexity. Migration of master, referential and transactional data to new business organizations
require precise planning, coordination and execution. We will review in detail a case study for a global energy
services company reviewing the process followed throughout the project.
Objective 1: To provide project and data strategies to dealing with multiple organizational consolidation.
Objective 2: To discuss both manual and automated tools that were used to create the new business entity.
Objective 3: To review the structured process and techniques followed by the project team.
Objective 4: To review lessons learned in dealing with a complex business process and data consolidation project.
Objective 5: To review processes to follow to mitigate business risks with data migration.
Intended Audiences:
(i) Individual contributor (ii) Project team member (iii) Project Manager
High Level Overview
Our Client “Avantha Power and Infrastructure (http://www.avanthagroup.com)” implemented Oracle E-Business
Suite R12.1.3 application for Projects, Enterprise Asset Management, Finance, Order to Cash and Procure to pay
functions. The source system was 10 years old Oracle E-Business Suite 11.5.7 application. This project involves
highest complications in Data transformations and consolidation of data from multiple Operating Units and
Inventory Organizations. We successfully used Oracle Business Accelerators (OBA) for creating the new setups in
Oracle E-Business Suite R12.1.3. We used the automated data migration tool “appLOAD” for migrating the huge
volume complex data from Oracle E-Business Suite 11.5.7 to Oracle E-Business Suite 12.1.3 with complicated
Validations, Translation Rules and Data Consolidations as well. The project was implemented under 6 month’s
timeframe.
Key Concepts
Information Life cycle phases are Plan, Obtain, Store and Share, Maintain, Apply and Dispose. All phases of the
Information life cycle have a cost. It is only when the resource is applied that the company receives value from it.
Data quality is affected by activities in all of the phases of the life cycle. The major difference between information
as a resource and other resources is that Information is reusable; it is not consumed when used. If the information
is wrong, it will be used again and again – with negative results.

2. Master Data: Master data describe the people, places and things that are involved in an organization’s business.
Eg: People (Customer, employees, vendors), places (locations, sales territories, organizations), and things
(accounts, products, assets, document sets).
Reference Data: Reference data are sets of values or classification schemas that are referred to by systems,
applications, data stores, processes and reports as well as by transactional and master records. Eg: Customer type
in Customer Master Data, Item type in Item master data.
Transactional Data: Transactional data describe an internal or external event or transaction that takes place as an
organization conducts its business. Eg: sales order, invoices, purchase orders, trips, deliveries, cash receipts,
payments, inventory transactions etc.
Metadata: Metadata literally means “data about data”. It shows all the characteristics of the tables and fields
within them such as: Field name, Constraints, Data Type etc.
Data Validation: To ensure data quality, data validations of the source data against the target setups and
reference data is very essential. Also validations are done against the source data itself for issues such as
Duplication, Data accuracy and Data Profiling issues. Generally condition based action logic is performed to check
for the data validations.
Data Extraction: In a Data Migration project the source data which need to be migrated need to be extracted in a
specific format needed for the target systems. You can use SQL, PL/SQL, Java approaches to extract data into
appropriate file formats.
Data Mart: In a data migration projects, the data extracted is loaded into a Data Mart so that data quality checks
and data cleansing can be performed in the data mart without disturbing the source application. Data mart is sets
of tables which are used for staging the data and used as a working area. These tables can be dropped from the
database once the data migration project is completed successfully.
Data Transformation: The Source data cannot be directly loaded into the target application. The source data
columns need to be translated to a different value based on certain rules. These translations can take place on the
Data extraction or against the data loaded in the data mart. This is a key functionality needed for any ERP data
migrations.
Data Consolidation: Data consolidation is a process to combine data from multiple operating units or inventory
organization into fewer OU’s and Inventory Organizations. Also this is applicable for situations where more than 1
application needs to be migrated into a single application.
The Current Challenges
Operational Challenges:
Multiple representation of financial transaction
No Standardized business processes across the organization
Cannot Capture the production costs and product costing for energy products
Could not take advantage of tax holidays for capacity expansions
Cannot Keep track of project expenditures and capital expenditures
Could not close the periods Independent by the SBUs
Category based valuation for coal and chemicals was not available

3. Compliance and Governance:
To Comply with updated Accounting Standards (IFRS)
To Comply with complex Tax rules
To Improve Group level consolidation and reporting
To Comply with multiple legislative, industry or geography requirement
Technology:
End of life application – Oracle E-Business Suite 11.5.7 application
Reporting platform was not user friendly
Inadequate MIS Reporting
Project Information
Avantha Power was using Oracle E-Business Suite 11.5.7 from Year 2002 onwards. Avantha used Oracle Financials,
Oracle Process Manufacturing and Oracle Receivables for its business. Asset management was handled outside the
EBS system. Project costing was handled using GL Chart of account mapping. Avantha Power was demerged from
BILT and wanted to implement Oracle E-Business Suite R12.1.3 for their global operations which includes 6 Power
Plants. The data from multiple Operating Units were consolidated into target Operating Units. The OPM Inventory
data was migrated into Oracle Discrete Inventory module without Lot/Sublot information for the EAM-MRO items.
The entire project duration was 6 months.
Modules Implemented
The following modules where implemented in Oracle E-Business Suite R12.1.3: Oracle General Ledger, Oracle
Payables, Oracle Receivables, Oracle Fixed Assets, Oracle Cash Management, Oracle Project Costing, Oracle Project
Billing, Oracle Project Management, Oracle Enterprise Asset Management Suite, Oracle Order Management,
Oracle Advanced Pricing, Oracle Purchasing, Oracle Inventory, Oracle Process Manufacturing – Process Execution,
Costing, Formulator etc.
Process Improvements
Oracle MAC to Oracle SLAM: The client never made use of MAC in Oracle EBS 11.5.7 and chargedall the WIP issues
to consumption. In Oracle EBS R12, using SLAM the Raw material, WIP and Finished good valuations and costing
needs where successfully mapped.
Lot/Sub-lot reorganization: Oracle OPM Inventory was migrated into Oracle Discrete Inventory module.
Implemented the new discrete functionalities along with OPM functions. Data transformation from lot/sub-lots to
no controls. Usage of sub-inventories and locators. Accurate costing was achieved.
New Quality Models: Quality collection plan for procurement, Lab samples, grading processes through Oracle
Quality module.
Project Costing: Client was capturing project costs against projects by having a separate segment in COA in legacy.
In EBS R12, through Oracle Project Costing module, project costs captured at tasks level and interfaced to Oracle
FA for capitalizations

4. Maintenance Cost: Maintenance costs were captured manually in Oracle EBS 11.5.7. In the new EBS R12, through
Enterprise Asset Management, preventive and breakdown Maintenance activities are handled to automate the
capturing of material and resource costs.
Improved Order Management: For energy products/services, manual Invoices were raised in Oracle EBS 11.5.7. In
the new EBS R12, by way of configuration of O2C process, Auto invoices were implemented.
Oracle Business Accelerator (OBA) and ChainSys appLOAD Tool
For this project we utilized the OBA templates for configuring the Setups and used appLOAD Suite for data
migration from Oracle EBS 11.5.7 to Oracle EBS R12.1.3. OBA is a great accelerator tool for creating Functional
Setups in Oracle EBS R12.1.3. AppLOAD helped us tremendously with Data Extraction, Data Transformation, Data
Validation, Data Consolidation, Data Cleansing and Data Migration. With both OBA and appLOAD we were able to
shrink the project implementation timeline close to 50% from 12 months to 6 months project.
Cleanse and Transform Data as Required
AppLOAD Suite provides a robust logical data transformation tool. It is capable of logically modifying existing data
or creating data logically in pre-defined fields. For example if a new segment is added to the general ledger chart
set the entry can be created using this feature. AppLOAD Suite includes a number of tools to support data
cleansing, for example data may be compared to locate duplicate records with alias key identifiers based on sound,
key words (Levinstein Distance Method) and custom configured logical relationships. All objects and attributes in
the data mart are available for edit. An audit trail is created for all changes.
Data Consolidation
We used appLOAD Suite to perform the Data consolidation and cross referencing. The data was extracted and
brought into the Data Mart. Data Transformation and Cleansing took place in the data mart using automated rules
engine and manual excel export/imports. The data consolidation happens in the data mart as well using techniques
such as Soundex, Logical Rules, NYSIIS, and Double Metaphone. The consolidated data will be standardized and
loaded into the target application with the cross reference information stored against the alias information.
Key Take Away
Technology: Choose the correct Oracle Business Accelerator template for configuring the new EBS R12 Instance.
Along with OBA, choose the correct accelerator tools for Data Migration and BI/Analytics purpose. Plan effectively
for the on-going data quality initiatives. Move as many reports into BI/Analytics platform. Plan for post
implementation initiatives and communicate effectively.
People: Create grass root support within the organization for the project work. Don’t address the people, address
the issue. Don’t shoot the messenger.
Process: Do not work on the solution during the requirement gathering. Clearly understand the reasoning for
performing as-is study. Prioritize the requirements and work. Keep the solution simple and easy. Work on the
change management continuously from the project start to end.