ASUG Philadelphia Chapter Meeting Presentation by Matthew Vandevere - DATUM 1. How Johnson Controls Mobilized Their Data Governance Program for Big Data & MDG on HANA Matthew Vandevere – DATUM 2. Organizations struggle to balance ERP roll-outs with Data Governance initiatives, but there is a way to integrate deployment activities to achieve maximum value by: establishing a vision for MDG on HANA with proven strategies and tactics for deployment  taking advantage of MDG on HANA to drive value in advance of ERP deployment  Utilizing platform capabilities to accelerate ERP Implementations LEARNING POINTS 3. About Johnson Controls Today, there are nearly 170,000 employees and many business partners in the Johnson Controls’ family delivering products and services wherever their customers live, work and travel. 4. JCI’s Evolving Strategies Deliberate and explicit choices Company choices v. business unit-only choices Play to win in the markets they choose Data-driven v. supported anecdotes What does JCI want to be? 5. Quick Facts about DATUM • Fast-Growth Solutions Company Recognized by • Recognized by SmartCEO Magazine as a Future 50 rising star • Named Leader in Data Governance 2.0 by Forrester - February 2015 • 70% of ASUG Data Governance 2014 SIG Annual Meeting “Success Stories” are users of our Solutions DATUM Customers 6. The ERP Program Challenge for Data GovernanceInformationTrustworthiness Time ERP Data Readiness Mobilization Point Organization Data Gov. Policy Recognition of Lost ROI The Scramble How Could That Be? We Own It Let’s Start with Data! Migration Degradation Reliability Team Work RISK REDUCTION VALUE CREATION Mobilize Earlier! Typical ERP program activities are inherently designed to focus on the critical data governance requirements until AFTER Go- Live 7. Prioritizing what is Governed Strategic Insights Information Data KPI’s / Measures The foundation of the governance program is set based on the ERP program, but will expand based on the data and information that is most important to the business (processes and analytics) 8. Governance Model Evolution Governance Model  Standards and Business Rules drive design and build activities and are identified and evaluated as part of Unity Program  Governance Model will be formalized as part of the Unity Program during DD (e.g. BPD’s, Build, Testing, ..) focused on SAP (Unity); reviewing and approving standards & rules  Governance Roster largely represented by Unity Program Team (business representatives)  Data Standard and Business Rule Ownership (business ownership) still transitional  Targeted external communication of proposed standards and business rules Project Mode (Design, Build, Test, Deploy)  Standards and Business Rules approved and managed as part of the deployed system  Governance Model implemented to support deployed sites, pending deployments (and targeted legacy sites) – SAP and non-SAP focus  Standards and Business Rules driving Unity benefits (

1. How Johnson Controls Mobilized Their Data
Governance Program for Big Data & MDG on HANA
Matthew Vandevere – DATUM

2.  Organizations struggle to balance ERP roll-outs with Data
Governance initiatives, but there is a way to integrate
deployment activities to achieve maximum value by:
 establishing a vision for MDG on HANA with proven strategies
and tactics for deployment
 taking advantage of MDG on HANA to drive value in advance of
ERP deployment
 Utilizing platform capabilities to accelerate ERP Implementations
LEARNING POINTS

3. About Johnson Controls
Today, there are nearly 170,000 employees
and many business partners in the
Johnson Controls’ family
delivering products and services
wherever their customers
live, work and travel.

4. JCI’s Evolving Strategies
Deliberate and explicit choices
Company choices v. business unit-only choices
Play to win in the markets they choose
Data-driven v. supported anecdotes
What does JCI want to be?

5. Quick Facts about DATUM
• Fast-Growth Solutions Company Recognized by
• Recognized by SmartCEO Magazine as a Future 50 rising star
• Named Leader in Data Governance 2.0 by Forrester - February 2015
• 70% of ASUG Data Governance 2014 SIG Annual Meeting “Success
Stories” are users of our Solutions
DATUM Customers

6. The ERP Program Challenge for Data GovernanceInformationTrustworthiness
Time
ERP Data Readiness
Mobilization Point
Organization
Data Gov.
Policy
Recognition
of Lost ROI
The Scramble How Could That Be? We Own It Let’s Start with Data!
Migration Degradation Reliability Team Work
RISK
REDUCTION
VALUE
CREATION
Mobilize Earlier!
Typical ERP program activities are inherently designed to focus
on the critical data governance requirements until AFTER Go-
Live

7. Prioritizing what is Governed
Strategic Insights
Information
Data
KPI’s / Measures
The foundation of the governance program is set based on the ERP
program, but will expand based on the data and information that is most
important to the business (processes and analytics)

8. Governance Model Evolution
Governance Model
 Standards and Business Rules drive design and
build activities and are iteratively identified and
evaluated as part of Unity Program
 Governance Model will be formalized as part of
the Unity Program during DD (e.g. BPD’s, Build,
Testing, ..) focused on SAP (Unity); reviewing
and approving standards & rules
 Governance Roster largely represented by Unity
Program Team (business representatives)
 Data Standard and Business Rule Ownership
(business ownership) still transitional
 Targeted external communication of proposed
standards and business rules
Project Mode
(Design, Build, Test, Deploy)
 Standards and Business Rules approved and
managed as part of the deployed system
 Governance Model implemented to support
deployed sites, pending deployments (and
targeted legacy sites) – SAP and non-SAP focus
 Standards and Business Rules driving Unity
benefits (operations, analytics, compliance, …)
 Governance Roster represented by key
Business Owners taking ownership of the
system(s)
 Data Standard and Business Rule Ownership
transitioned to end-state Business Owners
 Broad communication of approved standards
and business rules
Steady-State
(Sustain / Optimize)
Process
People
Data Governance Model needs to be established during the ERP Program

9. BluePrint (Design) – Think Governance
Data Object List Customer Master Credit Master
Customer Master
Info Record
Billing Document
DesignActivities
Business Data Dictionary
(Customer Example)
Customer Name
(KNA1-KUNNR)
Account Group
(KNA1-KTOKD)
Industry Code 1
(KNA1-BRAN1)
Ref Acct Group
(KNA1-KTOCD)
Data Design
 Required,
Optional, Not Used
 List of Allowed
Value Settings
 Security
Data Standards
 Business Usage
 Business Owner
 System of Record
 Allowed Values
Business Rules (DQ)
 Scenario Specific
Rules (e.g.
Acount Group
rules for Sold To,
Ship To, Payer,
and Hierarchy)
Data Conversion
 Baseline for Wave
System Mappings
 Conversion Rules
 Data Construction
Rules

10. Process Definitions – What about Data ?
Does not clearly define the data process within a business process context
Business stage gates are not clear and/or not defined
Documentation is driven from IT or founded on technical specifications
Insufficient documentation providing required data to support the process (and impact)
Lack of consistent process documentation existed or adherence in the current state

11. MDG-enabled, Optimized Data Processes
Roles/Tasks now clearly align and roll-up to support business processes
Business process driven stage gates defined and managed via workflow
Optimized process enables a Just In Time (JIT) data collection process that
aligns to enable business processes and support critical milestones
Leveraging our process documentation and complementary tools provides
scalable, standardized, and governed processes

12. Establishing a Repeatable Data Governance Framework
Is there Compliance,
Financial or
Operational Impact?
Where
should we
govern?How should
we govern?
(Point of
Entry,
Passively,
None?)
Frequency of
change?
Impact?
Risk/Benefits
EXAMPLE: We need to add a commodity code for
Lead/Cores supporting compliance and analytical
requirements.
Who has decision rights?
Governance decisions for every data element are
evaluated and determined independently, but
always follow the same methodology and approach

13. Importance of a Data Governance Framework

14. Linking the Data Governance Framework
Business
Data Glossary
(BDG)
Business
Data Dictionary
(BDD)
Governance Rules
(Scenario Based)
Process Decomposition
(L1 – L5)
Level 1
Process
Level 2
Sub-Process
Level 3
Activities
Level 4
Process Steps
Level 5
Data Elements
Data Glossary
and Dictionary
Link to
Governance Model
Governance Rules and Standards on Process Steps and Data Elements
Data Standards
Repository
Measures & Metrics
Definitions
Data Governance Framework

15. Synchronizing Process, Data and Rules
Overview of Planning Item and Finished Good Global Workflows
Version 1.1 – September 27, 2013
QuEST Activity
Brief Approved
Marketing
Request
Planning Item
0
days3
Material Master LDA
Create New
Planning Item/
ZREP
1
days2
Pricing LDA
Populate
Planning Price
4
days0
This Database
Waiting for IR
Approval
5
days0
Marketing
Update New
Finished Good
Request
0
days2
Sales & IT Services
Update
Classifications
1.1
days2
Demand Planning
Update
Classifications
1.2
days2
LDAs
Update FG Setup and
MOE & Classifications
2
days2
R&D QA
Populate Shelf
Life Data
4.2
days2
R&D SST
Populate
Dimensions and
Weights
4.1
days0
Workflow
Awaiting
Packaging
Specs to be
moved to
Pending
3
days2
LDAs
Create PKI and
PKI Det Rules and
Activate the FG
5
End of FG Global
WF
days2
R&D SST
Populate
Declared Weight
2.2
days2
Demand Planning
Populate
Classifications
2.3
Workflow triggers
Pricing Workflow to
circulate.
days2
Material Master LDA
Populate MOEs
and
Classifications
3
QuEST
Investment Reco
Approved
Approve Waiting
for IR Step
Marketing
End of Planning
WF
Planning Item
Workflow
Finished Good
Global Workflow
days2
Sales & IT Services
Populate
Classifications
2.4
days2
Ops Planners
Assign Production
Plant
2.1
Workflow sends email
notification to Sales
Planning, Demand
Planners, Ops Planners &
Replen
(only for non-seasonal)
Workflow sends email
notification to Sales
Planning, Demand
Planners, Ops Planners &
Replen
______________________________________________________________________________________
Business Process Flows
Process Overview List
Governance Rule Composer
Governance Rule #
Field Level
Details List
Process Stage Gates
Rule Reference #
Enabling MetaData
Process Flows
Standards, Rules and Metrics Business Process Flows import and remain “synced” with the data and
business rules required to operationalize and govern them
1 to many
1 to many
many to many

16. Rules before Tools !
“Rule Readiness”
dictates “Tool
Readiness” … how well
the organization is
positioned to develop
governance rules will
dictate solution and
initiative success.

17. Rules will support both ERP and MDG Activities
Leveraging Governance Rules…
 Assess Data in order to…
 Validate Governance Rules
 Determine if site data is ready for
Deployment
 Define timing & staffing required for
cleansing & enrichment
 Cleansing & Enrichment Goals
 Only cleanse & enrich data required
for migration
 Ensure process is intuitive & driven by
business (not IT)
 Cutover …
 Pre-Mock Load Testing against target
configuration
 Multiple Mock Loads (SIT, UAT, SIM)
 Reduce timing of outage window
Example
Ingest data - 111k records
De-
Dupe
1200
records
Execute Relevancy Rules – 17k records
*Narrow down the data to what’s relevant to the business.
Auto Cleanse – 1300
records
*Address Standardization, etc.
Manual Cleansing
*Data Construction, Data Enrichment,
Duplicate selection, Data Corrections

18. Operationalizing Business Knowledge for MDG
JCI Data Governance Framework
Information Value Management (IVM)
SAP MDG
SAP Information
Steward
Existing docs
Business knowledge
System Config
Business Metrics
Rule Extract
(Func. Spec)Load
Template1
2
3

19. Step 1: Establish Data Governance Framework
• Interview Data Stewards, BPO, IT and SME roles to determine critical
governance and MDG configuration inputs
• Configure Rule Composer to reflect JCI’s Data Governance
Framework
• Produce standardized templates to capture existing business rules
Rule Load
Template
Required MDG Inputs1

20. Step 2: Define Data Stds & Business Rules
Existing docs
& processes
Tacit knowledge System Config
Governance
Template
• Capture existing rules and transform
into JCI Data Governance Framework
• Complete Rule Definitions for specific
intended Governance Strategy
JCI Data Governance Framework
Information Value Management (IVM)
2

21. Measure Rule Readiness for MDG
105 Rules 75.4%
MDG based
governance
requires
robust rule
definitions

22. Targeting Rule Definition Gaps
By establishing and
following a structured
governance framework,
we can easily define
GAPS within each
business rule that are
required functional inputs
for MDG

23. Ensuring a ‘Complete’ Functional Design
Ready for Execution

24. Step 3: Functional Extract Ready for Build
Functional
Spec. Extract for
MDG
3

25.  Competing with ERP objectives, timelines and
deliverables can often impede Data Governance
objectives
 Ensuring that Data Governance is actively part of (not
informed by) the ERP program is critical
 Establishing and following a structured, repeatable Data
Governance Framework ensures relevancy and success
 Early identification, capture and measurement of critical
MDG-specific inputs is the only way to avoid delays and
successful deliver
KEY LEARNINGS

26. Questions
Matthew Vandevere
Strategy Principal
DATUM LLC
matthew.vandevere@datumstrategy.com
610.659.3114