How JCI Prepared a Data Governance Program for Big Data & MDG on HANA
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The document outlines how Johnson Controls effectively mobilized their data governance program alongside big data and Master Data Governance (MDG) initiatives on HANA. It details strategies for integrating data governance with ERP deployments to optimize data readiness and operational effectiveness, emphasizing the importance of a structured governance framework. Additionally, it highlights the evolution of governance models, the role of business process management, and the need for proactive alignment between data governance and ERP to achieve better outcomes.
How JCI Prepared a Data Governance Program for Big Data & MDG on HANA
- 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