Todays’ increasing emphasis on differentiation in the digital economy further complicates the data governance challenge. Learn about today’s common challenges and about the new adaptations that are required to support the digital era. Avoid the pitfalls and follow along on Johnson & Johnson’s journey to: - Establish and scale a best in class enterprise data governance program - Identify and focus on the most critical data and information to bolster incremental wins and garner executive support - Ensure readiness for automation with SAP MDG on HANA

1. Most Common Data Governance Challenges in
the Digital Economy
Justin McCullough (Janssen Pharmaceuticals) & David Woods
(DATUM)
Session ID #5095

2. WHO WE ARE
• The world’s sixth-largest consumer health company
• The world’s most comprehensive medical devices business
• The world’s sixth-largest biologics company
• The world’s fifth-largest pharmaceuticals company
• 128,300 Employees Worldwide
We Proudly Serve
• We help large enterprises achieve competitive advantage &
profitability faster by leveraging data as an asset
• Recognized thought-leader in Data and Analytics
• Information Value Management® is the leading software
platform for information management
Johnson & Johnson DATUM LLC

3. THE DIGITAL ECONOMY
SAP’s DIGITAL BUSINESS FRAMEWORK
1. Outcome-based customer
experience
2. Re-platforming core business
processes and bringing together
business process and analytics
in real-time
3. Smarter, engaged workforce
4. Supplier collaboration
accelerating growth innovation
5. Harnessing the IoT and Big Data
to drive real-time insights and
new business models
SAP Defines Digitization
Across Five Key Pillars

4. DIGITAL DICTATES A NEW ‘REALITY’ FOR DATA
Data
validated
decisions
Batch
processing
of data
Historical
data drives
decisions
Governance
improves
data
quality
Process
dictates
data design
Data based
decisions
Real-Time,
instant
access
Real-Time
data drives
decisions
Governance
ensures
data
quality
Data
insights
drive
process
changes
BEFORE AFTER

5. THE DATA LANDSCAPE IS MORE COMPLEX …
Enterprise Data
Structural Data
Org Data
Reference Data
Core Master Data
Functional Data
Transactiional Data
Documents
Metadata
Configuration Data
Market Research
Customer Feedback
Mobile Data
POS Data
Demographics
Experimental Data
User Generated Data
Social Media
Search History
Transaction ‘click’ Data
Effective data management no
longer just involves managing large
data volumes, but now must handle
data variety and velocity.
Data governance is becoming an
integral part of our ability to fully
capitalize on our investments in ERP
platforms and analytics tools.
While our program started with a
focus on the data we manage
internally, our model is designed to
scale beyond that as we prioritize
the governance of ‘other’ data
sources that are critical to our
growth in the digital economy.
Data Created Within
Our Organization
Data Created Outside
Our Organization

6. … AND WE ALL THINK ABOUT DATA DIFFERENTLY
Operations
Compliance
Analytics
Our Governance FocusHow We Define It
Functional Usage
(Business Teams)
Type of Data
(Data Scientists & Architects)
System Representation
(IT Teams)

7. THE DIGITAL CALL-TO-ACTION FOR DATA GOVERNANCE
• Standardization and simplification
of business processes and analytics
(in real-time)
• Ability to quickly scale and adapt
through organic growth, acquisition
or divestiture
• Informed collaboration across
fragmented systems, processes and
disparate data sets
• Leveraging data to become more
efficient - “how can we do more
with less”
Strategic Data Value Drivers

8. 1. Misdirected focus (prioritizing what matters)
2. Project-Focused mindset (lack of scalable model)
3. Misaligned tool strategy (theory to execution)
4. Inability to transition from Project to Steady-State
5. Failure to Measure Readiness
FIVE MOST COMMON POINTS OF FAILURE

9. Challenge # 1:
Prioritizing What Matters

10. PRIORITIZATION DRIVES A DATA SIMPLIFICATION PROCESS
Why does
this Data
matter?
Context
Matters!
Metrics Processes People
Systems
Success for
this Data is…
• Better Performance
• Reduced Risk
• Improved Security
• Better Use of Capital
• Increased Agility
• Speed to Market
• Improved Use of Tech
What Level
of
Governance
?
“Data governance value to many organizations is non-
financial and embedded in the intrinsic value, business
value and performance value of information*”
*Referencing Doug Laney’s published work on “Infonomics”

11. Challenge # 2:
Developing a Scalable Model

12. ESTABLISHING A UNIFIED GOVERNANCE PLATFORM IS CRITICAL

13. Challenge # 3:
Misaligned Tool Strategy

14. FOCUSING ON THE ‘VALUE’ OF DATA GOVERNANCE
Maintenance
Execution
Processes
“Data is for
Critical to
enable Digital
Growth”
• Standardized Tools
• Low Cost Execution
• “Lift & Shift” to
Automate Current
Process
Governance
Scenario
Business
Rules
Governance
Strategy
Critical Data
Standards
• Clear Decision Rights
• Ownership & Accountability
• Data Standards for Analytics
• Business Rules for Execution
• Regulatory Compliance
• Data Quality
• Data Process Efficiency
• Prioritized Focus
Over the past few years, the proliferation of exciting new technology solutions have led
companies to quickly implement tools expecting to find a silver bullet for data…
…however, they have struggled to implement effective solutions that only provide
automation capabilities, but don’t actually realize any business value

15. Challenge # 4:
Inability to Transition from Project to Steady-State

16. GOVERNANCE OPERATING MODEL EVOLUTION
Governance Model
• Standards and Business Rules drive
design and build activities
• Governance Model should be
formalized during BluePrint/Design
• Governance Roster largely represented
by Project Team (IT and Business)
• Data Standard and Business Rule
Ownership is in a transitional stage
Project Mode
(Design, Build, Test, Deploy)
• Standards and Business Rules approved
and managed as part of the deployed
system
• Standards and Business Rules driving
program benefits
• Governance Roster represented by key
Business Owners taking ownership
• Broad communication of approved
standards and business rules
Steady-State
(Sustain / Optimize)
Process
People

17. Challenge # 5:
Failure to Measure Readiness

18. MEASURING ‘TRUE’ READINESS
Capabilities
Organizational
Process
Tools
Focus on capabilities and ensure that ALL the
of enabling components are lined-up at each
stage of program evolution ….
… and providing meaningful metrics
that identify specific gaps and facilitate
closer to ensure information trust

19. Summary

20. APPROACH TO DATA IN THE DIGITAL ECONOMY
Strategic Program
Alignment
Establish
Digital Core
Operationalize
Instant Insights
Optimize &
Differentiate
“RUN” “GROW” “INNOVATE”
Data Governance Framework
(Information Value Management®)
Data Applications
(SAP EIM Tools)
In-Memory Analytics
(BW on HANA & HANA EE)
SAP S/4HANA
Challenge #1
Prioritize the data that
matters and build the
Digital Core
Challenge #3
Select tools that are
‘fit for purpose” and
drive digital value
Challenge #4
Plan for and manage
the transition of
information ownership
Challenge #5
Measure readiness for
business transformation
into a Digital Enterprise?
Challenge #2
Build a scalable model
to Support trusted
data in the Enterprise

21. THANK YOU & QUESTIONS
David Woods
Principal Partner
DATUM LLC
david.woods@datumstrategy.com
610.812.5476 (m)
Justin McCullough
Director, Enterprise Business Architecture
Janssen Pharmaceuticals, Inc.
Johnson & Johnson Family of Companies
JMccull3@its.jnj.com

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