Using SAP Master Data Technologies to Enable Key Business Capabilities in Johnson & Johnson Consumer

Transcription

1 Using SAP Master Data Technologies to Enable Key Business Capabilities in Johnson & Johnson Consumer Terry Bouziotis: Director, IT Enterprise Master Data Management JJHCS Bob Delp: Sr. MDM Program Manager Johnson & Johnson Consumer Companies

2 Agenda Introduction Master Data Management in Johnson & Johnson Business needs drive technical solutions How can this align with your Analytics strategy?

3 Who is Johnson & Johnson? Global Leader in Health Care More than 250 Operating Companies Selling Products in more than 175 Countries 128,000 Employees Worldwide

4 BI and Data Center of Excellence a Global IT organization within J&J Consumer responsible for creating and fostering a vision, passion, and strategy for data management partnering with business master data teams to develop and maintain data standards driving and enabling a global data governance strategy implementing SAP MDM as a core global data management platform for the consolidation of product, customer, and supplier master data Empowering data stewards with tools and capabilities to monitor and improve data quality, leading to more trusted analytics providing the strategy & solutions for Global Data Synchronization (GDS) while aligning with business and enterprise initiatives. 4

5 We knew we needed to change Referee Syndrome data only came into focus when there was problem Strategic data issues permeated every aspect of our business Post conversion data integrity lost over time Error reconciliation dominated our data management activities Data Management resource projections expanding globally Internal lessons learned provided clear warning signs How can we get rid of the traditional data clerk role? Traditional CoE model dictated hiring more and more resources Motivational issues resulted in poor data metrics and quality issues Tactical focus resulted in retention issues We operated the classic Data Governance Model NONE!! 5

6 Focus Areas MDM Framework Workflow: Ability to effectively manage complex and cross functional master data scenarios consistently across sectors Data Integrity: Ability to consistently monitor data quality and demonstrate that we are measurably improving Data Strategy: Ability to enable, and build towards, end state (BtB model) while also positively impacting current state

7 Technology and Capability Roadmap HIGH Complexity LOW Data Governance Regional Governance Projects Data Quality Assessments Master Data Management 60% master data visibility for product and customer data Data Synchronization Pilot Established Metadata Repository Master Data standards Industry Data Standards Repository Data Governance Predictive Governance in all Regions Select rollout of Active Governance capabilities Master Data Management 100% master data visibility for Product, Customer, and Supplier Platform upgrades Raw Material Repository Version 1.0 Data Synchronization rolled out to 3 regions and 10 markets Standardized Business Processes New Product Introduction (NPI) Process Standardized Technologies Data Consolidation Process Orchestration / Active Governance Data Stewardship Tools Data Synchronization Master Data Management Data Enrichment Services (3 rd Party) Integration w/ Enterprise MDM initiatives Chemical ingredient synchronization Raw Materials 2.0 Integrated R&D and Supply Chain data Digital asset management Investigate New Capabilities Data Quality Scorecards External Data Cloud Services Increased Value Over Time 7

8 Enterprise Master Data Management (EMDM) Program The EMDM Program was established to: Establishment of an enterprise governance model (non-affiliated) to drive acceptance and compliance Core set of foundational data standards (for our most critical and crossfunctional information sets) Organizational accountability and alignment to the core data standards Org Alignment Data Governance Framework Data Standards

9 Approach / Philosophy Refine data standards Identify business scenarios Refine Business Rules Define data maintenance roles Define governance roles Utilize the system Re-prioritize focus areas Define Business Metrics Business Discovery / Definition / Ownership Enablement / Innovation Infrastructure Application configuration Deployment Targeted Cleansing Metric Dashboards Support Successful programs maintain a balanced effort between the Business and IT

10 EMDM Objectives Operational Governance Model Required Capabilities Data Standardization Platform Optimization An executive sponsored council to drive strategy and gain control over the landscape Core capabilities and map to required technologies Primary standardization targets, business rules, baseline compliance Standard technology platforms

11 EMDM Operational Model Executive Sponsors Provide business strategy and handles escalations Business Partner Examples Supply Chain Provide business needs for Finance data and information HR EMDM Governance Council Provides strategic direction for data management across the Enterprise Aligns EMDM and IT efforts in support of business need, and Enterprise strategies Data and initiatives Standards IT Portfolio Strategy & Provides & decision Architecture making and oversight Management for governing the Capability Enterprise Architecture data framework IT Partner Examples Provide Enterprise input to IT technical data design Provide Sector technical IT delivery of data architecture External Partners Enterprise Data Governance Board Enterprise Develops, Data monitors Regional and prioritizes Data enterprise Global data Process standards and maintenance processes Quality & IT Provides Stewards tactical direction Leaders for data standardization Owners and reliability improvement efforts Compliance Master Data Teams Provide input to data standard Regional development / sector Govern master data maintenance activity Local

12 Identifying Required Capabilities The ability to manage the process of maintaining data The ability to enforce business rules, actively, before data is entered The ability to consolidate disparate sources of data Workflow for Data Point of Entry Governance Consolidation Data Profiling Single Version of the Truth Data Quality Measurement The ability to rationalize master data The ability to measure the reliability of our data against desired dimensions of quality Data Integrity Monitoring Business Glossary Data Cleansing The ability to measure data in accordance with business rules The ability to remediate data quality issues The ability to know how our data is being used The ability to centralize data terms and definitions

13 Identify Standardization Targets Procedural Data Governance Policies & Procedures Discovery Interviews Governance Rule Composition Cycle Active Data Governance (SAP MDG) SAP Configuration Data Quality and Validation Rules Predictive Data Governance (SAP Information Steward) Operational Data Hub (SAP MDM)

14 Simple Steps for Strategy Execution Determine Program Goals Successful Tool Selection and Deployment Identify Required Capabilities Evaluate and Select Tools Identify Key Enablers Measure Readiness Close Gaps and execute

15 Everyone wants a Silver Bullet Are we ready to be successful? What tools best match these requirements? What capabilities are required to achieve our goal? What do you want to achieve? What s our focus? Implement Data Governance Capabilities Improved Analytics Ownership Data Standards Business Rules Org Alignment Change Management Operational Data Hub (SAP MDM) Active Data Governance (SAP MDG) Predictive Data Governance (Information Steward) Data Services Consolidation Golden Record De-duplication Cleansing Workflow Synchronization Decision-Making Operational Efficiency Reduced Cycle Times Improved Compliance

16 Everyone wants a Silver Bullet Consolidate master data from various systems (SAP ECC and non SAP systems) Consolidation Normalization, Data Cleansing Cleanse, normalize, and enrich disparate data from various sources Create a single Golden Record of merged data from multiple systems Identify and eliminate duplicate data SAP MDM SAP MDG Publish master data to downstream systems Data Synchronization Data and Process Orchestration Govern data at point of creation with discrete business rules and validations Workflow processing Approvals to control when data is transactable in downstream systems

17 Matching Desired Capabilities to Tools SAP MDG and Information Steward SAP MDM Workflow for Data Point of Entry Governance Consolidation Data Profiling Single Version of the Truth Data Cleansing Data Integrity Monitoring Business Glossary Data Quality Measurement

18 Establish a Common Vision for Growth Capture the right information to baseline tools across All levels of maturity required for success OPTIMIZED Program Maturity Level DEVELOPMENTAL Informal Data Standards Request Forms Silo d Data Applications Transactional Data Design REACTIVE Data Standards Repository Governance Projects Rule Collection / Creation Data Policies / Procedures Timeline (Key Milestones) ESTABLISHED Integrated Standards and Change Management Data Strategy Data Quality Metrics Managing Data Actively Data Mgmt Platform Data Process Optimization Collaboration Platform Business-Centric Metrics Enablers Tactical Use of Analytics (Likely) Targeted Tactical Use of DG Tools Predictive Governance with SAP Info Steward Consolidation / Discovery with MDM - Procedural Governance Active Governance with MDG

19 Enterprise Data Management Existing ERP Landscape Data Migration Services (Initial & Repetitive) Predictive Data Governance (System Monitoring). Global Template Workflow Based Data Governance (Point of Entry) Operational Data Hub(s) will be leveraged to create, manage, and in some cases, distribute critical master data attributes across our core data entities (e.g. Product, Customer, Supplier, FI, etc..) Operational Data Hubs will also provide a single source of truth of this critical data for analytical applications (e.g. EDW) Integrated SAP EIM Suite Data Services Info Steward SAP MDG SAP MDM Data Standards / Business Rules (Common Repository) Reference Data Model(s) built using Data Standards

20 Key Analytical Capabilities require strong foundational Master Data Supplier Reliability & Forecast Reporting Consolidated and Enriched Supplier Information Regional Dashboards Standardization of master data for KPIs and Metrics Central global master data repository: Product & Customer & Supplier Spend Analytics Consolidated and Enriched Supplier Information Raw Material Visibility Data NPI Sales vs. Forecast NPI SKU/Country Consolidated product information linked to forecast Net Trade Sales: COT/Franchise Customer Spend Waterfall Consolidated Customer Master Complex Hierarchy Management Point of Sales Analytics Standardized product hierarchies

21 Next Gen BI Architecture Frontend Presentation Layer Single Access Portal Strategic Dashboards (SAP Xcelsius) HANA DB engine SAP BW 7.3 Global Information & Solutions / Enterprise Layer Common Regional Information & Solutions Extended BI SAP BW 7.3 Visualization (Tableau) Extended BI Operational Reporting (SAP BOBJ, COGNOS) Integration & Consolidation of regional SAP BW and non-sap platforms SAP BW 7.3 Extended BI SAP BW 7.3 Extended BI NA EMEA LATAM ASPAC SAP DataServices SAP SLT DataRep NA ECC xxx APO Data Integration & Master Data Services LATAM ECC xxx APO ASPAC ECC xxx APO EMEA ECC xxx APO Tableau Viz Server MDM/G Information Steward SAP HANA based Next Gen BI Concept Single point access & Integration Leverages single HANA DB engine to host all regional SAP and Non SAP BI. Near Real-Time reporting access ECC data via SLT Ability to merge structured and unstructured data for Agile data marts Superior performance Flexibility to meet changing business needs Best in class tools.

22 Key Takeaways Learn how to tackle master data complexity in a heterogeneous, multi-instance environment Apply a usage model for linking SAP EIM technologies to business capabilities Leverage your master data strategy as a catalyst for driving your analytics strategy 22

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