MDM106 - MDM106_Leading_with_Data___Governance_for_One_Finance

Data and governance for
financial transformation
Informatica World 2015
Speakers
Alistair Wallace — eBay Inc.
Sachin Khairnar — Deloitte Consulting LLP

‫‏‬Topics we will cover today
‫‏‬Program vision and approach
‫‏‬Functional and technical design
highlights
‫‏‬Information technology enablers
‫‏‬Leading practices and lessons
learned

2 Data and governance for financial transformation Informatica World 2015 Copyright © 2015 Deloitte Development LLC. All rights reserved.
One Finance delivering business goals through improved data, more
effective processes, and standard systems
Program vision: Propelling One Finance
Objectives
Common and consistent
processes
Standardized planning
and reporting
Accurate and timely
spend visibility
Enable future growth
Coordinate governance
$
Outcomes
Better insights and
controls
Increased stakeholder
experience
Improved functional
effectiveness

Data and governance program was kicked off to improve the quality of
information, drive operational efficiency and make key business decisions
Leading the vision with data
Data and
governance
• Profile, cleanse, and
rationalize data
• Co-exist with SAP
landscape
• Implement Master
Data Management,
Data Cleansing and
Monitoring solution
• Instill business ownership starting with design
• Engage business leadership and stakeholders
• Deploy data governance org
• Align anticipated
outcomes
• Establish common
data language
• Design data
governance
processes
• Develop solution
roadmap

The‫“‏‬One‫‏‬Finance”‫‏‬data‫‏‬language
What aspects of data were addressed? Which functions were impacted?
Master data: Deployed central Master
Data Management for Cost Center,
Profit Center and G/L Account
Reference data: Reference data is
centrally created with proactive
business and quality controls in
MDM/RDM before publishing to
financial transactions and reporting
Hierarchies: Hierarchy structures are
centrally maintained in MDM solution
and published to SAP ECC and BPC
X-ref mapping: X-ref. mappings
enable co-existence of legacy data
structures‫‏‬with‫‏‬new‫“‏‬data‫‏‬language”
• FP&A
• Accounting
• Controllership
• SAP CoE
• BU finance operations
• Procurement
• IT

Key design highlights — data governance processes
Data profiling and cleansing: Analyzed
finance data to identify business impact.
Cleansed and rationalized data prioritized data
Data management: Streamlined processes to
perform day to day management and control
ongoing changes in master data
Data monitoring: Deployed systematic
processes to actively detect compliance with
common data language and drive remediation
Data glossary change control: Process to
control changes in definition and list of values
for‫‏‬data‫‏‬elements‫‏‬that‫‏‬make‫‏‬up‫“‏‬common‫‏‬data‫‏‬
language”

Data management functionality was designed to initially manage SAP
financial master data, reference data, hierarchies, relationships, and then
scale to manage other data domains
Key design highlights — solution architecture
User experience and workflow
Business rules
Data quality
Data repository
Master data delivery services
ETLXML flat file Web services
Finance master
data
Reference data
Hierarchy X-reference
Data monitoringData standardization Data cleansing
Data validationsControl values
Data standards and
glossary
Request
Review/
assess
Approve
Maintain and
synch
Finance data management Consuming systems
SLC /SRM SAP BA
SAP BPC
Non-SAP
SAP ECC
BW
SAP validations
Workflow trigger
X-ref/mapping
Master data,
hierarchies

A segment and corporate co-owned organization was designed and
deployed to sustain the data quality accountabilities post go-live
Key design highlights — organizational
Regional issue
log
MDM request
log
DQ monitoring
reports
Executive report
Overall health
report
Status update
for DGO
Strategic
Steering Committee
Execution
DGO Working
Operations
End Users
Tactical
DGO Advisory Council
Data Domain Leadership
The executive report has a
comprehensive overview of the
overall health and condition of
the Data Governance program
Reports and logs from
the execution level
describes KPIs and
metrics
Status updates and health
reports analyzes the impact of
outstanding issues on
operations

Key design highlights — technical
Enabling Informatica tools
MDM: Master Data created in a central MDM
system with proactive business and quality
controls and publishes add/changes into
subsequent ERP and other systems with
common data set
Automated and improved integration:
Enhanced integration with data warehouse, SAP
ECC and BPC enabling distribution of required
data attributes entered in MDM to consuming
downstream systems
Data monitoring: Operational Data Quality
Reports and Dashboards to track ongoing quality
and highlight exceptions
Business glossary: Deployed Informatica
Analyst (business glossary) to provide visibility
and controls for common data language across
the organization
• Informatica MDM
• Power Center
• SAP Power Exchange
• Informatica Power Center
• Power Exchange
• Informatica Power Center
• Informatica IDD
• Tableau
• Informatica Analyst (business
glossary)

Key design highlights — managing SAP data
Enabling Informatica tools
SAP master data and hierarchy sets:
Deployed solution to centrally maintain the
alternate hierarchy sets along with the master
data in SAP ECC through MDM
Automated business validations: Deployed
automated business validations required to
maintain finance critical data in SAP ECC
through SAP IDocs/BAPI
Enhanced integration: Built automated and
enhanced integration capabilities to publish
master, hierarchy and X-Ref. mapping data from
MDM to ECC and BW
Data quality profiling: Masked and performed
data quality profiling directly on SAP ECC data
without staging it in a separate layer
• MDM
• Power Center
• Power Exchange
• MDM
• Power Center
• Power Exchange
• Power Center
• Power Exchange
• Power Exchange
• Data Quality
• Data Analyst

Lessons learned and leading practices
Stakeholder experience: Maintaining focus on stakeholder experience, in addition to
streamline processes , is a key to effective adoption and delivery of anticipated outcomes
Design time governance: Establish business ownership starting from the planning and
design phase. Clarify responsibilities and provide visibility into roadmap
Data quality profiling: Mask and profile the data at source such as SAP. Provide
business relevant impact as context to profiling results.
Strategic alignment: Data Quality and Governance initiatives are effective if they are
aligned and executed in conjunction with other large enterprise ventures with measurable
business benefits
Global design for regional execution: Data Governance initiatives should be designed
to support global requirements and a collaborative effort between segments and
corporate‫‏‬functions.‫‏‬It’s‫‏‬operationalization‫‏‬won’t‫‏‬be‫‏‬effective‫‏‬unless‫‏‬there‫‏‬is‫‏‬an‫‏‬
involvement of all.

To summarize
Current challenges:
• Lack of single repository with
required information
• Uncoordinated processes
and governance impacting
workload and timeliness
• Fragmented technology and
tools that are not scalable
Deployed data and
governance to:
• Clarify finance processes
and the language of data
• Cleanse and rationalize
finance data
• Implement Informatica
enabled MDM and data
governance solution
Business outcomes:
• Increased ability to analyze
and manage business
performance
• Improved usage of
information
• Single data governance
model and clear
accountabilities for data

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should consult a qualified professional adviser. No entity in the Deloitte Network shall be responsible for any loss whatsoever sustained by any person who relies on this publication.
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