How Global Data Management (GDM) within J&J Pharma is SAVE'ing its Data. Craig Pusczko & Chris Henderson

Transcription

1 How Global Data Management (GDM) within J&J Pharma is SAVE'ing its Data Craig Pusczko & Chris Henderson

2 Abstract See how J&J Pharma organizational alignment drove the evolution of Global Data Management (GDM). The globalization of functions such as planning, procurement and finance precipitated i the need for standardized di d data across disparate ERP platforms. GDM evolved from a data maintenance organization to become one which addresses the full spectrum of data management across the supply chain, including Data Quality, Data Governance, Data Integration and Stewardship. See how technologies such SAP MDM enabled this transformation and laid the foundation for supporting new global business processes. Real Experience. Real Advantage. 2

3 Agenda Company Overview Evolution of Global Data Management (GDM) Introduction of SAVE Approach to SAVE ing our Data through Governance Real Experience. Real Advantage. 3

4 Learning Points Sr. Management Support Approach for establishing Data Ownership Data Standards Process Prioritization Standard Content & Roadmap Achieving i Data Quality Data Governance Framework Technology Real Experience. Real Advantage. 4

5 Johnson & Johnson Company Structure Johnson & Johnson has more than 250 companies located in 57 countries around the world. Our Family of Companies is organized into several business segments comprised of franchises and therapeutic categories. Consumer Health Care The Consumer segment includes a broad range of consumer health and personal care products in the beauty, baby, oral care and women s health categories, as well as nutritional products and over-the-counter medicines and wellness and prevention platforms. Medical Devices & Diagnostics The Medical Devices & Diagnostics segment focuses on technologies, solutions and services in the fields of cardiovascular disease, diabetes care, orthopaedics, vision care, wound care, aesthetics, sports medicine, infection prevention, minimally invasive surgery, and diagnostics. Pharmaceuticals The Pharmaceutical segment's broad portfolio focuses on unmet medical needs across several therapeutic areas: oncology; infectious disease; immunology; neuroscience; cardiovascular and metabolism. It includes products in the anti-infective, antipsychotic, cardiovascular, contraceptive, dermatology, gastrointestinal, hematology, immunology, neurology, oncology, pain management, urology and virology fields. Real Experience. Real Advantage. 5

6 : Data Maintenance Organization No formal Processes, Data Errors, Disparate Systems SCOPE: Region: North America (Pharmaceuticals) Domain: Material Data No Governance role for Data Administration Disparate systems(amaps, PRMS. etc) No standardized process for Data Ambiguous ownership for data Low Data Integrity and system confidence ERP Platform No standardized process for Data No formal procedures No data ownership Unclear data requirements led to 120, plus Data Migration errors Corporate internal audit Governance role for Data Administration created Standardized, formal process for data authorization & creation Master Data group training 25,000 plus GMP errors corrected through million dollar project Corporate Internal Audit observations mitigated NO FOCUS on Master Data Real Experience. Real Advantage. 6

7 [ 2009: One Supply Chain Expanded Scope FROM: TO: North America Domain: D i M Material i ld Data Platform: One ERP System Global Domain: Material Data Platform: Multiple ERP Real Experience. Real Advantage. Systems 7

8 Supply Chain Data Maintenance War Stories Lack of data standardization across the Supply Chain Process has an impact Real Experience. Real Advantage. 8

9 Result of One Supply Chain Disjointed Governance Responsibilities split across multiple organizations Inconsistent accountability increases complexity and minimizes opportunities to leverage best practices globally Disconnected Tools Absence of central tool has resulted in disparate local l solutions Scalability / extensibility are curbed Significant manual effort to get reports with consistent data Inconsistent Standards Difficult to leverage best practices due to limited common data standards Inconsistent use of Data, Different Standards across Sites Code conflicts exist and no plan to harmonize with ERP roll outs No VISABLITY into the Health of our Data Multiple Processes Lack of visibility / transparency to master data process Multiple, l disjointed i d processes are required to create master data across the supply chain Real Experience. Real Advantage. 9

10 GDM s VISION: An organization capable of making effective business decisions through Standard, Accurate, Visible, and Efficient Data (SAVE). Standard Enable business processes to be efficient, complete, accurate (end 2 end) by ensuring data is standardized at the appropriate level and remains fit for its intended use in the business Enabling effective business decision making as data, its purpose, and its relevance to business process are well understood d by the business Define Data Ownership & Accountability Accurate Ensure that data is of appropriate quality (accuracy, completeness, etc.) to meet business needs Enforcement of Data Standards Visible Ensure the quality of data is readily available to put awareness of compliance / efficiency risks with the business Ensure that data is appropriately stored, segmented, standardized, etc. (overall data model) to support business intelligence (access to raw data) Visibility to Master Data Management Process Efficient Enable efficient business processes by ensuring data within that process is valuable and necessary for that process Data is appropriately maintained throughout its lifecycle to ensure it remains valuable to the business process it support Real Experience. Real Advantage. 10

11 Approach to SAVE ing our Data Real Experience. Real Advantage. 11

12 Data Ownership: Tactical Approach Define Ownership Model Gain Support from Senior Leadership Identify Functional Owners Alignment with Owners Commitment from Individual Data Owners Real Experience. Real Advantage. 12

13 Data Ownership: Ownership Model Share Ownership: Data that is consumed by multiple departmental processes or material types. Condense Ownership: Where we want to drive global standardization and there is no value in site differentiation. Functional Area: Dependant on the accuracy of the data. Local Domain: Data specific to a site or system, ownership resides in that level. No broader use expected. Real Experience. Real Advantage. 13

14 Data Ownership: Results for High Priority Data Real Experience. Real Advantage. 14

15 Data Ownership: Share Ownership (Brand) Real Experience. Real Advantage. 15

16 Data Standards: Prioritization Breakdown Full criticality analysis of Data (2050 elements) Prioritization of high and medium criticality (227 elements) Value in standardization assessment (39 Elements) Data proposed for standardization (30 Elements) Initial Assessment 2050 Prioritization 227 Value Assessment 39 Target 30 Real Experience. Real Advantage. 16

17 Data Standards: Prioritization Matrix Priority Pi i / Effort Criteria Definition Priority Priority Priority Business Critical Defines the impact in product compliance and/or regulation. Also rates those fields that are critical for the business execution or continuity (material movement, order creation, inventory, impact production). 9 High Required to meet or support regulatory requirements or ensure product quality. 3 Med Required to support business requirement, analytics or trending. 1 Low System required or informational, but not critical to the process, compliance or other 0 None No apparent requirement for this field. Does Standardization of this field help reduce compliance Risk Compliance Risk Reductions 0 No Improvement, 1 Minimal Risk Improvement, 3 Some Risk Improvement, 9 Greatly Improves Risk Customer Feedback Alignment to the feedback received from key customers and stakeholders for GDM opportunities to improve data standardization. Also includes feedback from Global VOC tour. 0 = None No feedback from customer, 1 = Low Minimal feedback,3 = Medium Some feedback,9 = High Strong feedback from Global VOC or direct customer feedback Priority Potential Return ($) NPV 0 = Negative Return, 1 = No Return, 3 = Positive Return, 9 = Extremely Positive Return Priority Priority Priority Effort Cost ($) Effort Effort Reduction of Data Complexity Standardization Needed to Enable other GPSG initiatives Frequency of Change Impact to Organization System's Impacted Real Experience. Real Advantage. Assessment whether implementing the data standard for a data element will reduce the data complexity 0 = None 0%, 1 = Minimal Reduction 1 25%, 3 = Limited Reduction 26 75%, 9 = Significant Reduction % (i.e. reduces interfaces) Define if standardization is needed for a current, future, or potential project. 0 = None No need for seen, 1 = Potential Long Term Need Need foreseen within the next 12 months 3 = Potential Short Term Need Need foreseen within the next 1 3 months, 9 = Immediate Need Current need exists Defines the frequency that the field is changed or maintained 9 = Never changes, 3 = Changes 1 3 times a year,1 = Changes Weekly, 0 = Changes Daily Financial assessment or estimation that considers the total cost to implement a data standard for a data element. Includes resources needed to define, implement, train and maintain the data standards within the source systems. 0 = High (>$250K), 1 = Medium (>$5K and <$250K), 3 = Low (<$5K), 9 = None People impact that quantifies the changes or additions to an organizations responsibilities or process re design due to data standards. 0 = High Significantly impacts organizational operations, 1 = Medium Some impact to organizational operations 3 = Low Minimally impacts organizational operations, 9 = No impact to organizational operations Identification of the number of systems affected by the implementation of data standards. The more systems that a field is standardized across adds more value to that standard. 0 = None, 1 = Single (1 system), 3 = Multiple (>1 system),9 = Enterprise (JNJ) 17

18 Data Standards: Prioritization: Priority vs Effort HH Matrix Plot of Priority vs Effort HL Priority LH Effort LL 120 Real Experience. Real Advantage. 18

19 Data Standards: Overall Tactical Approach for Top Opportunity / Value to Standardization 2. Assess Current State - Data Content - Technical State 3. Define Standard: - Business Defines Requirement 4. Approach to Standardizing - Consolidation (MDM) - Procedural Control - Harmonization Across ERPs 5. Roadmap - Next Standardization Steps - Timing / Alignment with ERP Roadmap Real Experience. Real Advantage. 19

20 Data Standards: What s in a Data Standard? Data Identifier - The Table and the technical field name of the element(s) being standardized. di d Data Source(s) - The system and/or table, which is considered to be the primary source, in which the data can be retrieved. Data Standard Owner - The manager title of the functional area, at the lowest level, charged with maintaining the standard and allowable values for the data element. Data Providers(s) - The individual or department who provides the content of the data and ensures its compliance to the Data Standard. Data Definition - Description of the purpose of the data. Data Dependencies - Details any other data elements or standards that are related and can be impacted by changes either upstream or downstream. Data Consumers - The intended applications, processes or reports that use the data within the element, and are supported by this standard. Anything not identified here may not be considered when making a change to the standard. Data Criticality Type - The criticality of the data element as defined by the Risk Assessment. Data Governance Level - The level that the data standard will be governed. Data Categorization - Classification of how primarily the data element is used. Data Standard Level - The level within the organization for which the standard is set. Data Monitoring - Description of how the data will be checked e to adhere e to the standards. a s. Business Rules Statements that define or constrain the way data is entered. Allowable Values The Allowable values that can be maintained for the data element. (optional) Real Experience. Real Advantage. 20

21 Data Standards: Sample Roadmap Phase 1 Identify data owner Establish data governance Create a consolidated Global Brand list in MDM and map existing ERP brand data to it Create a data quality report to monitor the usage and alignment to the material description Phase 2 Implement procedural controls around the data element to ensure it is maintain Cleanse data to ensure brand is completed for all applicable data elements (i.e. finished products) Phase 3 Harmonize values within the ERP systems Real Experience. Real Advantage. 21

22 Data Standards: Achieving Data Quality Governance Processes Standards - Tools Tools to consolidate, profile and monitor data against standards MDM platform as single source for product master data Consolidation of data into global values in MDM for key data elements Data Quality Toolset (INFORMATICA) with ability to profile data and monitor against standards Standards for data critical to the business, and roadmaps to implement them Standards for 30 high priority data elements Roadmaps for implementing 30 standards Processes required to appropriately govern data and relevant standards Standards creation and maintenance Data risk evaluation Data quality profiling & monitoring Data lifecycle management process improvements Governance the organizational structure (GDM) and roles & responsibilities required to support data management GDM Organization supporting full spectrum of data governance Establishment of R&R and commitment within Business, linked to Enterprise Real Experience. Real Advantage. 22

23 Data Standards: Achieving Data Quality Dimension of Data Quality Real Experience. Real Advantage. 23

24 Data Governance: Framework - Standards Governance Process Real Experience. Real Advantage. 24

25 Data Governance: Framework - Roles & Responsibilities Real Experience. Real Advantage. 25

26 Technology: Architecture & Capabilities Capability Current Master Data Lifecycle Management Processes Excel Forms ERP-1 Informatica Scanned Docs Workflow Tool ERP-2 ERP-3 Data Profiling Data Monitoring KPI / Metrics & Quality SAP MDM Data Repository Metadata Management (SharePoint) Proactive Data Quality View Product for Single Source Consolidation of data into Global values Manage Metadata Profile Data Monitor Data Quality 26 Real Experience. Real Advantage.

27 Technology: SAP MDM Enabling Strategic Business Initiatives Supply Chain Example roject Data Need ds P Global Procurement Standardized Cross Platform Commodity Codes Global Finance Financial Flow Automation Global Planning Cross Platform Planning, Demand Mgmt SAP MDM Single source for product master data Duplication check Global data values ERP 1 ERP 2 ERP 3 Real Experience. Real Advantage. 27

28 Takeaways Sr. Management played a key stakeholder role, vital for change management, committed resource for data standard ownership. Creating a Global Data Management Organization, not a project, was the catalyst for bringing visibility to the disparate data / processes so standardization work can begin. Having business partners that understand how data interacts with their processes is critical when defining standards for a global supply chain. Establishing a 'single source' repository for your data is vital to supporting functions that cross disparate ERP platforms. Real Experience. Real Advantage. 28

29 QUESTIONS Real Experience. Real Advantage. 29

30 Thank you for participating. Please remember to complete and return your evaluation form following this session. For ongoing education on this area of focus, visit the Year-Round Community page at [ SESSION CODE: 0702 Real Experience. Real Advantage. 30