benelux intervet 09 12 2004

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Information about benelux intervet 09 12 2004

Published on October 16, 2007

Author: Kestrel


Data Management:  Data Management in a GxP validated environment Agenda:  Agenda Akzo Nobel & Intervet GxP validated environment Atlas Data Management Generic Data Standards Data Migration (Legacy) Data Archiving & Retention Data Quality (Future) Data Management Organization Summary Questions? Akzo Nobel:  Akzo Nobel Akzo Nobel, based in The Netherlands, serves customers throughout the world with healthcare products, coatings and chemicals. Consolidated sales for 2003 totaled EUR 13 billion. The Company employs approx. 64,500 people in 80 countries. Akzo Nobel – business groups:  Akzo Nobel – business groups Prescription drugs, hospital supplies, veterinary products, raw materials for the pharmaceutical industry Car Refinishes, Decorative and Industrial Coatings, Industrial Finishes, Industrial Products, Marine & Protective Coatings, Powder Coatings Base Chemicals, Catalysts, Energy, Functional Chemicals, Plastics & Processing Additives (Akcros Chemicals), Polymer Chemicals, Pulp & Paper Chemicals (Eka Chemicals), Resins, Salt, Surface Chemistry Akzo Nobel’s pharma business:  Akzo Nobel’s pharma business Business units Product group Organon Prescription drugs Diosynth Special raw materials for the pharmaceutical industry Intervet Animal Health products Intervet International:  Intervet International Intervet develops and sells veterinary products (vaccines, diagnostics, pharmaceuticals and zootechnical feed additives) Intervet is represented by 56 commercial companies in 53 countries worldwide Intervet has approximately 5,200 employees Intervet has 14 R&D and 18 production sites Intervet – mission statement:  Intervet – mission statement Commitments to: research and development new technology innovative animal health products Competences: high quality products excellent technical support customer service Respect for: its customers its employees animal welfare health, safety and the environment Intervet’s product range:  Intervet’s product range Vaccines Poultry, livestock, companion animals & aquatic animals Diagnostics Detection of infectious diseases in animals (e.g., FMD) Antiparasitics Endo- and ectoparasitics Endocrine products Fertility treatments, breeding improvement, performance enhancers Antibiotics Oral and parenteral antibiotics, mastitis and metritis products Pharmaceutical specialities ACE inhibitors, NSAID, Insulin Zootechnical Feed Additives Digestion Enhancing Antibiotics (DEA), anticoccidials Intervet product line:  Intervet product line Vaccines ± 300 Pharmaceuticals ± 360 Feed additives ± 40 Excl. ± 200 local products Main Species:  Main Species Poultry Swine Ruminants Small Animals Equine Aqua GxP validated environment:  GxP validated environment What is it? GxP: GMP, GLP, etc. Regulated by regulatory institutions like FDA & USDA The FDA is leading with their 21 CFR part 11 Regulations are fully applicable to the Animal Health Industry It is all about Good Practices (just like Financial Audits or SOX) GxP validated environment:  GxP validated environment It is not magic !!! Use your common sense; Do everything with a good reason How much validation is required? This is a management decision. Determine an acceptable amount of risk and build a defensible case for this. Good and strong security, password, username and authenticity policies Static systems are easier to validate. A system with 40+ changes per month is difficult to validate. Doesn't give a warm / fuzzy feeling to the auditors. Validate the intended use of the system. For what will it be used? Audit your Vendors (like Oracle or SAP) Quality cannot be inspected or tested. It must be built in. Validation is multi-disciplinary team-based. It is not just only IT. Say what you do! Do what you say! Be able to prove it! Validation = Well organized, well documented, common sense 3 D's = Documentation, documentation, documentation If it is not documented, then it is has not happened V-Model for the Validation of SAP Systems :  V-Model for the Validation of SAP Systems Atlas:  Atlas Implementation of a standard package SAP Vanilla SAP Globalize & harmonize where possible For 7 of Intervet’s main companies Global Template approach Why does Data Management Matter?:  Why does Data Management Matter? Data is the foundation for the (SAP) systems Systems cannot generate information and knowledge without (the right) data Data enables integration across systems (EDI) Data management is required in order to consolidate data through standardization Data Management Objectives:  Data Management Objectives Sub-goals: Design & establish data management organization: Data management roles Long-term processes for data management Establish Data Standards Provide Test data during the Template Phase Define Migration strategy, tooling and process Establish (Legacy) Data Archiving & Retention Establish Data Quality Initiative: Create awareness of the importance of good quality data Facilitate data cleaning and translation Key Objective: Deliver data of sufficient high quality to Intervet during and after the Atlas program. DM Organizational Overview:  Local / International Organization DQ Teams DM Organizational Overview LBPO LDE LROM LDC Weekly Reports Site Management GBPO Data Other GBPO’s Central DM Business Streams Accountable for DM Accountable Data Owner Data Sponsor Input Monthly Reports Accountable for DQ Focal point & coordinator DM Plan DQ Approach Local Data Owner Key-areas of Data Management :  Key-areas of Data Management Data Standards Data Migration (Legacy) Data Archiving & Retention Data Quality (Future) Data Management Organization Atlas Data Management Initiatives:  Atlas Data Management Initiatives Data Standards:  Data Standards First draft documented by DM; Reviewed and fine-tuned by Business Streams Data standards are captured during the program in working-documents called the Data Code Books Data Code Book (DCB): These documents describe for some important and relevant Data Fields (like Item Number) what they are and what their proposed Coding Convention is. For some of these Data Fields the actual Coding Values will be documented. These documents also do contain a first indication for the Future Data Management Organization (like ownership and a category indicating centralization, harmonization or localization). For each item there is a Word document with: Definition Format, Coding, Categories Code values Ownership: Data Owner Data Administrator Data Maintainer Data Code Book information will ultimately migrate to SOP’s & Atlas glossary Data Standards (Category definition):  Data Standards (Category definition) Centralized / Globalized: Data is defined centrally, coded centrally and maintained centrally. It may be applied on local level but can’t be changed on the local level at any time. Hence, data changes must be requested to the data owner. Examples: Company codes, plant codes, controlling areas, global partner data, chart of accounts structure, currencies/exchange rates Harmonized: Data is defined centrally and initially coded centrally but may be maintained locally to suit local requirements. The harmonization may take the form of initial data sets or number ranges. Examples: Payment terms, cost centers, Batch master data Localized: Data is defined, coded and maintained locally in each country due to country specific requirements (e.g. legal requirements). Examples: G/L Accounts, External Customers, Warehouses Data Standards (Responsibility roles):  Data Standards (Responsibility roles) Data Standards (Analysis Sheets):  Data Standards (Analysis Sheets) Master Data Analysis Sheets (MDAS): These documents describe which fields in which Master Data Object (like the Material Master) will be used. In these sheets you can find information like the format and the optional/mandatory indicator per field. Transaction Data Analysis Sheets (TDAS): These documents describe which fields in which Transaction Data Object (like Accounts Receivable) will be used. In these sheets you can find information like the format and the optional/mandatory indicator per field. Migration Analysis Sheet (MAS): Mapping legacy to the defined SAP objects based upon the MDAS & TDAS. They provide a detailed definition of the legacy data that will be converted. Data Migration (Approach):  Data Migration (Approach) Covers the transfer of master data & transaction data from legacy systems to SAP. Migration requirements are defined in the URS’s Generic Template deliverables containing migration programs, documentation, cut-over plan, procedures, roles & responsibilities Orchestrated by the Central DM Team but executed locally Standard SAP R/3 tool for data migration (LSMW) Not all data migration will be automatic Historical data requires an approved Business Case Related to: Data Quality, Data Standards and Data Retention & Archiving Data Migration (Overview):  Extract Convert Load LSMW LSMW Template phase Rollout phase(s) MDAS/TDAS MAS Migration Func Spec Migration Tech Spec Migr Just Form Data Migration (Overview) Data Migration (Documentation):  Data Migration (Documentation) Data Migration Strategy Business Case for data migration Includes conversion justification i.e. auto rather than manual History data is covered by separate business cases Functional specification for Data Migration 1 spec covering all objects Includes basic information on checks required per object LSMW load programs Fully documented (TS) and documented testing (TP + TC) Generic cutover plan Data Migration List MDAS, TDAS & MAS Data Migration (Flow):  Data Migration (Flow) Data Migration (Cutover Plan):  Data Migration (Cutover Plan) The generic cutover Plan includes (documented again): Data loading sequence: Based on SAP constraints and Business constraints Agreed approach for loading the data Manual, semi-automatic, automatic, none Agreed minimum guidelines for checking the data Responsibility for sign-off of loading process per object In most cases this is the LBPO Rough timing for the data cutover The actual Local cutover plan is responsibility of the roll-out project (Legacy) Data Archiving & Retention:  (Legacy) Data Archiving & Retention Whatever is not migrated needs to be retired or archived Split between current legacy data and future Atlas data Local data retention requirements must be identified: Identify legal, regulatory or audit requirements Data must be available either in the online transaction system or accessible in some other format Data that is not required to be retained should be retired from the system Resulting in a Local Data Retention Policy signed off by Local Senior Management The Local Data Retention Policy: Must be signed off and owned by the Local Senior Management Must be reviewed by the central team Might be reviewed by Local Auditors (Legacy) Data Archiving & Retention:  (Legacy) Data Archiving & Retention Why is Data Quality important?:  Why is Data Quality important? Why important to Atlas project: History of companies with SAP failing to ship after go live Intervet’s own experience with Data Migration Potential lack of faith in new system post go live SAP is not as forgiving as our current systems or spreadsheets SAP: Garbage in; Garbage out SAP: Once in SAP; Always in SAP Value of Investment in SAP diminished by continued use of spreadsheets … Data Quality (Objectives):  Data Quality (Objectives) To get all data at required levels of accuracy two or three months prior to going live To provide a reporting mechanism to the organization which factually records current accuracy of data by site Provide foundation for long-term improvements in Data Quality across Intervet resulting in improved business (better and more accurate informed decision process) and lower costs (e.g. less wrong shipments and less correcting of errors in data) Data Quality (Approach):  Data Quality (Approach) 1. Review the approach with the stakeholders 2. Identify local teams & ownership 3. First local workshop & audit by Central DM to Outline agreed standard for accuracy Agree Audit process for measuring accuracy Illustrate root cause analysis Prepare an ongoing action plan for local Data Quality Teams 4. Continuous weekly or monthly audits (also after the Atlas implementation) 5. Startup parallel processes to fix structural problems in database 6. Monthly support from Central DM to review progress and provide help 7. Occasional random audits from Central DM to verify auditing process 8. Weekly reporting to LROM from Local Data Quality teams 9. Monthly reporting from LROM to Program Data Quality (Ownership):  Data Quality (Ownership) Overall approach and organization Data is a business issue and needs to be managed by business people Putting together local teams to resolve data issues People from business involved in day to day operation Establishing project and operational responsibility for all data items Overall accountability for Data Quality will be Rollout manager, who will hold Operational Manager responsible for their data [e.g Warehouse Manager for stores] Data Quality (Report 1/3):  LROM and/or Local Site Management Data Quality (Report 1/3) Local Report Local Report Local Report Local Report Steering committee Week 1 Week 2 GBPO data And other GBPO’s LBPO Report By LROM Week 3 Week 4 DM … Data Quality (Report 2/3):  Data Quality (Report 2/3) Report: Summarized Data Accuracy Report Site: Intervet Inc. Millsboro Example only Data Quality (Report 3/3):  Data Quality (Report 3/3) Report: Summarized Data Accuracy Report Month: April 2004 Example only Data Quality (Overall timeline):  Data Quality (Overall timeline) 3-day Workshop Plus initial audit Quality teams Start with Control Groups and random audit samples Root cause analysis Fix current database Log and identify issues for SAP Update database Sample audit 2 or 3 months Go / No go decision 6-12 months Control Group = 100 % Transfer data All data accurate Go live Continuous audits However, there might be other Business Reasons to start sooner Critical Success Factors:  Critical Success Factors Awareness & Urge of importance Management commitment and support Sufficient priority, resources & time Global long-term SAP-direction (GDAS) Local short-term legacy systems consideration (LDAS) Reuse existing local initiatives Start early !!! Factual reporting mechanism (progress, attention, resources) (Future) Data Management Organization:  (Future) Data Management Organization Data Management Policy Data Management Manual SOP’s for Data Maintenance Roles & Responsibilities Training on SOP’s, Roles & System (Future) Data Management Organization:  (Future) Data Management Organization EXAMPLE ONLY Line Management There will be a central DM organization: Defines data standards and procedures Coordinates and monitors the overall data administration function Performs central data administration tasks Maintains DM documentation Audit compliance / data quality Provides education and training on DM Summary:  Summary Data Management in a GxP related environment is about: Documentation (standards, agreements, proposals, decisions, etc.) Clarity of ownership, accountability & responsibility Following Good Practices (like 1st clear bus. requirements, then analyze and then build) Thorough documented testing Documented SOP’s, Roles & Responsibility Following these Good Practices can be also beneficial for non GxP related industries (if used with Common Sense) Thank you for your attention:  Thank you for your attention

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