The Data Management challenges each organization faces are unique in their priority and severity. Therefore the structure and composition of a Data Organization is one of the major success factors for establishing a successful and sustainable data program. In this presentation, we will review the developmental stages of a data organization, the models and the choices for establishing the right structure to the organization in addition to the process for selecting the team members that will produce high-performance business results.

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How to Structure the Data Organization
Data Governance Winter Conference
Stacey Stewart – Johnson & Johnson Healthcare Systems Inc.
David Woods – DATUM LLC

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• Global Leader in Healthcare
• Consist of more than 250 Operating Companies
• Sell Products in over 175 Countries
• 128,000 Employees Worldwide
We Proudly Serve
• Fast-Growth Solutions Company Recognized by Inc. 5000
• Named Leader in Data Governance 2.0 by Forrester
• 70% of ASUG Data Governance 2014 SIG Annual Meeting
“Success Stories” are users of our Solutions

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Data Organizational Challenges
The Data Governance Call-to-Action:
• Where to start …
• Efficiency focus – “do more with less”
• Ability to quickly scale and adapt through growth,
acquisition or divestiture
• Fragmented systems, processes and data
• Disparate BU’s and Regional / Local Data Teams
• Increasing Focus on Big Data (unstructured)
contradicts traditional techniques and skills
Common Points of Failure:
• Project-Focused mindset (lack of a scalable model)
• Inability to link the Business Value to the Program
• Thinking Org Model and not Operating Model

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EMDM Building Blocks and Core Components
1. Data Governance
Organization
(Accountability, Discipline,
Structure)
2. Data Governance Boards
(Composition, Focus,
Representation)
3. Aligning the Rest of the
Organization
4. Setting Expectations
(For all Parties)
Business accountability for
master data and appropriate
Org Structures for data
maintenance.
Business rules for data, accessible
by providing it, and consistent
across relevant business processes
Processes to ensure standards
are assessed, docuemented and
managed for consistency
Tools to capture, monitor and
enforce data standards and
business rules for master data
Sustainable
Data
Integrity

5. UNDERSTANDING DATA ORGANIZATIONS

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Best in Class Organizations Utilize 3 Levels of Ownership
Data Management Support
IT, Data Center of Excellence, Business Support
Operational Data Governance
Data SME - Business
Data Governance Program
Leadership
Data SME - IT
Business Ownership
Business Data Owner
(Mgmt-Level Business Owner)
Operational Data Owner
(Sub-Process Leadership)

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Partnership and Collaboration is Critical
• Define data standards
• Identify business scenarios
• Identify Business Rules
• Define data maintenance roles
• Define governance roles
• Utilize the system
• Prioritize focus areas
• Define Business Metrics
• Infrastructure
• Application configuration
• Deployment
• Targeted Cleansing
• Metric Dashboards
• Support / SLA’s
Successful programs maintain a balanced effort between the Business and IT
Technical
Execution
Business Discovery/
Definition/Ownership

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Evaluating Data-Centric Operating Models
Organizational Data Maturity
AutomatedDataGovernance
Process-BasedGovernance
Decentralized
Centralized
Distributed
(Hybrid)
Resource Dependent Solution
The ideal organizational model design
for ‘data’ will typically change as the
organizational competencies evolve,
data governance techniques are
applied, and the data IT application
capabilities mature

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No Data
Governance
Evaluating Data Governance Models
Data Quality and Awareness
Automation
ErrorRemediationTime
Predictive
Active
Process
Business Interruptions
Each of these data governance
methods are valid – the ‘key’ is
ensuring that the organizational
model complements (and certainly
doesn’t contradict) the data
governance strategy

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Ensure a Complementary Governance Strategy
Distributed / Predictive Centralized / Process
Plant 1
Plant 3Plant 2
Central HQ
Decentralized / Active
• Non-critical or closely governed data
maintenance processes
• Local autonomy & decision-making
• MDG PoE or no governance
Ex: Create P-Req, Assign Bin
• Role(s) centralized within a BU,
Plant, location or function
• Critical data requiring expertise,
control and governance
• Ex: Transportation, Planning Data
• Role(s) centralized across all
locations (impacts all)
• Critical data requiring centralized
expertise, control and governance
• Ex: Maintain G/L, Payment Terms

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Enterprise Data Governance Boards
Enterprise Data
StewardsQ&C
IT Representation
Regional Data
Mgmt Leadership
Business Unit
Representation
(Pharma, MD&D,
Consumer)
Global Process
Representation
(Plan, Source, Make,
Deliver, Finance)

12. ESTABLISHING THE OPERATING MODEL

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Seizing the Opportunity
Who will provide
the data?
What data is
required ?
When will the data
be utilized?
Where must the data
be triggered?
How will the data
be measured?
“How do we get there?”
By understanding
(identification and visibility)“How do We Drive
Business Value with Data?”
Operational
Efficiencies
Compliance
Adherence
Analytical
Insights

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A Unified Platform is Critical
How do we effectively …
• Capture actionable information from
different experts, systems, regions,
functional areas and formats?
• Have a sustainable platform to establish our
data governance strategy and accelerate
implementation?
• Ensure sustainability after implementation?
Governance Strategy Project Execution
(Tools, Processes & Procedures)
Decentralized Data Governance Knowledge
Platform for Data Governance Collaboration
Data and Implementation Teams

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Operating Model Construction
We typically see three (3) methods of forming a Data Governance Organizations – each one has
benefits and risks, which need to be carefully considered
• Net-New: Formation of an entirely new
organizational entity focused on data and
information governance.
• Lift & Shift: Identify existing, data-centric
organizational constructs and repurpose
them into a formal governance operating
model.
• Leverage Existing: Augment an existing
organizational governance model to include
data and information governance.
Data
Governance
Operating Model

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Two Paths to Development
Data managed at
business unit,
regional, and local
levels
Data managed at
business unit,
regional, and local
levels
Critical data
aligned and
centrally
governed across
the enterprise
Align on a vision, execution strategy, priorities,
ownership, accountability, tools and coordinate
efforts across all organizations and projects
Local data
managed
regionally
(following
enterprise
model)
Align on a vision and organizational construct, but
allow the model to evolve through individual
deployments, disparate approaches and indirect
ownership of priorities and execution strategy
Alternative Approach
• Program Based
• Direct
• Deliberate
• Measured
• Purposeful
Traditional Approach
• Evolution Based
• Indirect
• Reactive
• Tacit
• Function Driven

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Defining the Data Steward Role
Data Steward Primary Responsibilities:
 Ownership for the development and
Implementation of Data Standards, Business Rules,
Policies and Procedures
 Driving Strategic Master Data Initiatives across the
Enterprise in Collaboration with Business & IT
Leadership
 Monitoring Performance Measures & DQ Metrics
 Managing the Demand / Approval for Changes to
Existing Governance Procedures
 Developing and Coordinating Strategic Training
Plans and Training Materials
 Supporting business functions to ensure Data
Quality Procedures are being followed
Data Stewards are NOT responsible for…
Day-to-Day Master Data Maintenance Activities
Mass Creation or Change Requests
Execution of Data Quality Reports
Performing Ad Hoc Data Clean-up Tasks

18. ALIGNING THE ORGANIZATION

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4. How Many?3. How?2. Who?
Operating Model Alignment Key Points
Who should perform
these tasks?
• Ownership
• Business vs IT
• Roles
• Contextual Knowledge
• Maturity of tools for
automation
How should the
operating model be
aligned to best support
these activities?
• Central, Distributed,
Hybrid, Decentralized
• Cultural Challenges
• Funding/Costs
• SLA’s & Plant needs
How many resources
are required?
• Data Volumes
• # of Reqs
• SLAs
• Complexity
• Tool Maturity
1. What?
What are the tasks
and activities we
manage?
• Data Maintenance
• Data Governance
• Data Quality
• Other

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Understanding Your Company Culture
Organizational Charts drawing by Manu Cornet, http://www.bonkersworld.net

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Operating Model
Enterprise Data
Steward (EMDM)
 Enterprise Data Stewards exist for the Material,
Customer, Supplier and Finance Domains
 Regional Data Management Leaders are defined
as a Central POC for each Region (cross business
unit responsibility)
 Regional Data Management coordinates across
data department SME’s

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Data Management Leadership

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The business needs to change, but so do we…
As your program continues to evolve, your organization will not only need to develop new competencies, but
an entirely new perspective
Improve your
Business Skills
Focus on
Business
Value not
Application
Value
Create a
Business Model
for your Data
Management
Organization
Apply New
Tools and
Innovate
(Disruptive
Innovation)
Think Business
Architecture,
Not Data
Architecture
and Develop
those Skills

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Stacey Stewart
Worldwide Director, Enterprise Data Management
Johnson & Johnson Healthcare Systems Inc.
sstewa16@its.jnj.com
732.770.5031 (m)
David Woods
Principal Partner, EIM Strategy
DATUM LLC
david.woods@datumstrategy.com
610.812.5476 (m)
Thank You & Questions