How Johnson Controls Mobilized Their Data Governance Program for Big Data & MDG on HANA
Matthew Vandevere – DATUM
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
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.
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?
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 SolutionsDATUM Customers
The ERP Program Challenge for Data GovernanceIn
form
atio
n Tr
ustw
orth
ines
s
Time
ERP Data Readiness
ERP
Trig
ger
Dip in Data Trust
Mobilization Point
Organization
Data Gov. Policy
Steward
ship
Tools
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
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)
Prioritized Data for Governance
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
BluePrint (Design) – Think Governance
Data Object List Customer Master Credit Master
Customer Master Info
RecordBilling
Document
Design Activities
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
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
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
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
Importance of a Data Governance Framework
Linking the Data Governance Framework
Business Data Glossary
(BDG)
Business Data Dictionary
(BDD)
Governance Rules(Scenario Based)
Process Decomposition (L1 – L5)
Level 1Process
Level 2Sub-Process
Level 3Activities
Level 4Process Steps
Level 5Data Elements
Data Glossary and Dictionary
Link toGovernance Model
Governance Rules and Standards on Process Steps and Data Elements
Data Standards Repository
Measures & MetricsDefinitions
Data Governance Framework
Synchronizing Process, Data and Rules
Overview of Planning Item and Finished Good Global WorkflowsVersion 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
1.2days2
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 manymany to many
Rules before Tools !
“Rule Readiness” dictates “Tool
Readiness” … how well the organization is
positioned to develop governance rules will dictate solution and initiative success.
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
ExampleIngest 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
Operationalizing Business Knowledge for MDG
JCI Data Governance FrameworkInformation Value Management (IVM)
SAP MDG
SAP Information Steward
Existing docs
Business knowledge
System Config
Business Metrics
Rule Extract(Func. Spec)Load
Template1
2
3
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
Step 2: Define Data Stds & Business Rules
Existing docs& processes Tacit knowledge System Config
GovernanceTemplate
• Capture existing rules and transform into JCI Data Governance Framework
• Complete Rule Definitions for specific intended Governance Strategy
JCI Data Governance FrameworkInformation Value Management (IVM)
2
Measure Rule Readiness for MDG
105 Rules 75.4%
MDG based governance requires robust rule definitions
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
Ensuring a ‘Complete’ Functional Design
Ready for Execution
Step 3: Functional Extract Ready for Build
Functional Spec. Extract for MDG
Example Rows from Extract
3
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
Questions
Matthew VandevereStrategy PrincipalDATUM [email protected]
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