Enterprise Information Integration die Schlüsselkomponente für erfolgreiche SOA-Infrastruktur Dr.Siegmund Priglinger 18.04.2007 spriglinger@informatica.com 1 Informatica im Überblick Fast Facts Founded: 1993 Headquarters: Employees: 1,100+ Offices: Redwood City, California North and South America, Europe, Asia Pacific Revenue: $267 million (2005) Market share leader (Gartner Dataquest) Customers: 2,600+ 83 of Fortune 100 80%+ of Dow Jones Government organizations in 20 countries Unser Fokus Data Integration Products and Services Unsere Mission Wir helfen Firmen, eine Data Services Architecture zu implementieren, um damit ihre Daten optimal für den eigenen Geschäftserfolg einsetzen zu können. 2
Agenda Die ewigen Integrationsthemen Der Prozess- und Informationsfluss Die Vision von Data Services BestPractice Beispiele 3 Die ewigen Integrationsthemen Book: ICC Integration Competency Center, John Schmidt + David Lyle, Informatica 2006 4
Agenda Die ewigen Integrationsthemen Der Prozess- und Informationsfluss Die Vision von Data Services BestPractice Beispiele 5 Der Prozess- und der Informationsfluss (1) SAP-Modellschema für Prozesse Wo ist da der Datenfluss beschrieben? R/3-Referenzprozeßm odell SAP P Branchenm odell M odellfilter K unde Kundenm odell K unde Kundenm odell M odellfilter M odellfilter K unde Projekt 1 Projekt 2 6
Der Prozess- und der Informationsfluss (2) Wo ist da der Datenfluss beschrieben? EPK-Modellelemente 7 Der Prozess- und der Informationsfluss (3) SADT-Modellelemente Input Output 8
Der Prozess- und der Informationsfluss (4) Anwendung A Anwendung B 1 2 3 A 4 B Data Services: Lagerstammdaten 9 Der Prozess- und der Informationsfluss (5) Anwendung A Anwendung B Objektmodell A a~b Objektmodell B Redundante Daten: Synchronisation durch Data Services, NICHT durch Messages zwischen Methoden. 10
Beispiel: Versicherung 11 Generisch konfigurieren Modelle mappen Business Object Level versus Relational Table Level 12
Agenda Die ewigen Integrationsthemen Der Prozess- und Informationsfluss Die Vision von Data Services BestPractice Beispiele 13 Die Vision: Data Services entwirren diese Komplexität greifen auf Daten zu, wo sie auch sind. bringen Datendefinitionen in Einklang miteinander ( konsistent, wiederverwendbar). führen zu garantierter Datenqualität, zu richtigen und vollständigen Daten. wandeln Daten jeden Formats in nutzbare Daten um. 14
Die Vision von Data Services On-Demand Integration von Daten Enterprise SCM CRM External Data HR PLM ERP Custom BPO ITO SaaS Data Services 15 Data Governance oder was fehlt Data Governance ist in etwa so definierbar: Data Governance ist das System, mittels dessen das Unternehmen den internen und externen Informationsfluss steuert und die gesamte Datenstrategie und deren Architektur festlegt. Die Data Governance legt die Prozesse, die Fähigkeiten, die Führungsart und die Anlagen fest, die erforderlich sind, um die Informationsressourcen des Unternehmens erfolgreich einsetzen zu können. Gesichtspunkte eines ERP for IT 16
Informatica Produkte und Lösungen Integrierte Plattform für eine unternehmensweite automatisierte Datenintegration Audit, Monitor, Report Sicherstellung von Datenkonsistenz, Analyse von Fehlerquellen und laufende Datenqualitätsüberwachung Data Explorer Data Quality Access Discover Cleanse Integrate Deliver Auf jedes System im Batch oder Real-time Data Profiling sämtlicher Daten aus beliebigen Quellen Validieren, korrigieren und standardisieren Transformation und Datenharmonisierung Korrekte Daten überall zur richtigen Zeit PowerExchange PowerCenter Develop & Manage Zusammenarbeit auf Basis gemeinsamer Repositories und Metadaten 17 Service Orientierte Datenintegration 18
Agenda Die ewigen Integrationsthemen Der Prozess- und Informationsfluss Die Vision von Data Services Best Practice Beispiele 19 Data fragmentation is a key barrier to realizing the full business potential of your initiatives Manufacturing Goal: Support Engineering Development Ensure 24x7 availability of data for worldwide team to engineer and deliver solutions Challenge How We Helped Business Value Downtime could trigger costly interruptions to time-sensitive engineering PowerCenter Grid support 24x7 data availability Nearly 3x improvement in data load performance Three-year ROI of $2.5 million in reduced development time 20
Data fragmentation is a key barrier to realizing the full business potential of your initiatives Manufacturing Goal: Integrate M&A and Rationalize IT Environment Challenge How We Helped Business Value Migrate 30+ J.D Edwards and legacy application instances to SAP and SAP applications to new SAP platform Build 150 interfaces to enable synchronization PowerCenter PowerCenter Connect for SAP Certified SAP integration - IDOCS, ABAP, ALE, BAPI, and DMI 75% component reuse to accelerate deployment across multiple projects 80% improved productivity; millions of dollars in ROI End-to-end data integrity through validation and MDM Improved business agility and precision with unified, global SAP data infrastructure 21 Data fragmentation is a key barrier to realizing the full business potential of your initiatives Manufacturing Goal: Enhance Manufacturing and Business Processes Support transaction processing calibrating replenishment, production, distribution and billings to drive business insight, efficiency, and maximize profitability Challenge How We Helped Business Value Integrate SAP Data with 6 Legacy Applications PowerCenter Native capability to treat SAP IDocs as flat files 30% increase in IT personnel efficiency Improved data quality & customer satisfaction Greater precision in manufacturing and business processes 22
Data fragmentation is a key barrier to realizing the full business potential of your initiatives Manufacturing Goal: Gain Global Supply Chain Visibility Provide global, enterprise wide real-time visibility of supply chain via dashboards Supplier collaboration through a portal for vendor managed inventory Challenge How We Helped Business Value Time-consuming, manual effort to integrate data across supply chain, vendors Need to access disparate sources PowerCenter PowerCenter Connect for SAP Highly productive development tools Native connectivity to SAP, i2, BroadVision ~$10M monthly supply chain savings Increased inventory turns Reduced IT staff from 25 to 5 23 Data fragmentation is a key barrier to realizing the full business potential of your initiatives Manufacturing Goal: Gain Global View of Business Operations Challenge How We Helped Business Value Enterprise data distributed across the business Various development tools; custom code PowerCenter PowerCenter Connect for SAP and BW One standardized platform Improved performance and forecasting capabilities Improved development productivity 24
Data fragmentation is a key barrier to realizing the full business potential of your initiatives Manufacturing Goal: Single Source of Truth for Supply Chain Consolidate 6 versions of the truth 1 for each business unit down to 1 Reduce supply chain costs Challenge How We Helped Business Value Reporting data scattered across spreadsheets, Access databases and other silos Business users lack easy access to information Pre-built access to Oracle and other sources Built-in data presentation capabilities $15M per month savings in supplier spend Low administration overhead / costs 25 Data fragmentation is a key barrier to realizing the full business potential of your initiatives Manufacturing Goal: Improve Supply Chain Visibility Real-time visibility into global supply chain to view inventory and sales records Analytic dashboards for metrics-driven management across the organization Challenge How We Helped Business Value Time to implement had to be less than one year PowerCenter Quicker, more risk free and cost effective implementation $2 million savings from improved delivery of analytic information Anticipated tens of millions of dollars in savings through improved, real-time, global supply chain visibility 26
Data fragmentation is a key barrier to realizing the full business potential of your initiatives Manufacturing Goal: Modernize Mainframe Challenge How We Helped Business Value Migrate from legacy mainframe environment to client server PowerCenter Real-time access into warehouse inventory levels, sales and marketing activity Improved productivity, leading to cost savings (reduction of 40%) Sharp reduction in number of customer redeliveries 27 Data fragmentation is a key barrier to realizing the full business potential of your initiatives Manufacturing Goal: Integrate M&A Gain a holistic view of organization Challenge How We Helped Business Value Limited visibility into business operations, product and sales performance Tremendous overlap in products PowerCenter PowerCenter Data Analyzer Significantly lowered IT maintenance; lowered IT staff by 10 Saved $3-$4 million in implementation costs with fast implementation Real-time view of product and customer profitability 28
Data fragmentation is a key barrier to realizing the full business potential of your initiatives Manufacturing Goal: Deliver Superior Customer Service Challenge How We Helped Business Value Reliable customer and vehicle information not easily accessible PowerCenter Data Analyzer Provided single view of customer Increased accuracy and response of call center agents Increased customer satisfaction and loyalty Better data visibility for executives 29 Reuters - Increasing effectiveness of Global CRM processes through data quality Key Business Requirements: Fix data quality within existing Siebel systems Approach: Provide data quality metrics to drive improvement processes Implement one off and ongoing data quality processes Challenge Lack of ROI on Siebel due to low quality data Inaccurate mailing processes Inefficient marketing processes Solution Informatica Data Quality To implement a Data Quality Scorecard per country To implement one off cleansing and standardization Informatica Data Explorer Expected Results Increase in salesforce and marketing efficiency Recognised Data Quality metrics process in place 30
ref123 Third Largest Bank in the US KEY BUSINESS IMPERATIVE Informatica In Action IT/BUSINESS INITIATIVE: Regulatory Reporting DATA QUALITY INITIATIVE: DQ Reporting & Monitoring THE CHALLENGE Regulatory Compliance Compliance with anti-money laundering regulations Provide robust DQ reporting and metrics system for AML Unit INFORMATICA ADVANTAGE RESULTS/BENEFITS Enable AML team to build, manage and customize AML business rules Track and monitor data quality across key systems Data quality workbench for business users Scorecard aggregating data quality metrics from multiple systems Avoided regulatory penalties of up $20m Implemented AML DQ Monitoring ahead of deadline using existing AML team resources Saved estimated $3m+ cost of bespoke of AML solution 31 ref123 Supplying Gas & Electricity in 5 States KEY BUSINESS IMPERATIVE Informatica In Action IT/BUSINESS INITIATIVE: System Consolidation DATA QUALITY INITIATIVE: Data Match & Consolidation THE CHALLENGE Merge & Acquire Successful and cost effective post merger integration Build an integration hub for effective integration and data quality management of new data sources INFORMATICA ADVANTAGE RESULTS/BENEFITS Rapid post-merger data consolidation and quality management DQ platform to drive standardization and re-usability across multiple merger projects End-to-end data quality solution for all data types DQ as part of data integration platform Will deliver savings of $Ymillion per year through faster post-merger business integration Will reduce time and cost of post-merger data consolidation by X% 32
P&G Implementation of data quality processes as a critical element of Master Data Management (MDM) Key Business Requirements: Implementation of data Quality processes to improve master data management (MDM) data quality Approach: Provide data quality dimensions, metrics and cleansing functionality to drive improvement processes as part of data integration processes with PowerCenter Challenges Requirement to synchronize Gillett SAP instance with P&G SAP instance for master data (following the acquisition of Gillette) Enablement of global data quality analysts to implement data quality projects Solution Informatica Data Quality to Identify & categorise low quality data Implement a DI-Q process as part of a synchrosization process Informatica PowerCenter Expected Results Increased quality of master data Better management of master data Recognised Data Quality metrics process in place 33 VWR Implementation of data quality processes for a SOX initiative including Know Your Customer and Know Your Product Key Business Requirements: Implement data Quality processes to improve master data management (MDM) across the organisation to support SOX processes Approach: Provide a standard process for data quality management including data quality dimensions, metrics and cleansing functionality to support a Master Data Management strategy Challenges Build an effective business case for data quality Recognise that data quality was impact the core business processes Provide a solution for data quality Solution Informatica Data Quality to: Replace mannual DQ processes via automated processes Rationalise master data Be proactive by enabling data analysts Expected Results Increased quality of master data Reduced mannual review process (team of 20 people) Recognised Data Quality metrics and cleansing process in place 34
AIB Bank Data Governance / Stewardship Key Business Requirements: Retail CRM unit: Data Quality processes for all customer data within AIB group Compliance: Basel II, IFRS & SOX processes Approach: Implementation of Data Quality metrics processes to support both retail and compliance led solutions Challenge Due to some high profile errors in data i.e. overcharging, AIB needed to ensure 100% accurateness in customer information Inability to measure quality of data within compliance datawarehouse (Teradata) Solution Informatica Data Quality to Cleanse and standardize data Implement a data quality scorecard Implement ongoing cleansing processes Results Massive improvement in CRM processes. Compliance with Basel II, IFRS and SOX regulations though the implementation of Data Accuracy Scorecards Vastly improved data governance and data remediation processes 35 Nestle Data Standardization in preparation for SAP implementation Nestlé selected Informatica Data Quality to carry out data cleansing and transformation as it offered an easy to use, flexible and complete data quality solution with powerful reporting capabilities. -- Data Manager IS/IT Application Development / Data Conversion Nestec GLOBE Challenge Streamline business processes for implementation of central SAP ERP system Standardizing product, inventory and customer data across 60 locations Solution Defining internal data standards Validating & standardizing information from Nestle companies around the world Providing centralized control while enabling local data analysts to ensure DQ to local & global standards Tracking DQ via scorecard process Results Nestle rolled Data Quality out across its International operations in less than six months Significantly mitigated the risk associated with the GLOBE project Increased operational efficiency due to single reliable view of product and item data 36
Stryker Data Migration to Oracle ERP in Four Regions Key Business Requirements: Implement data Quality processes to reduce risk during an Oracle ERP implementation Challenge Approach: Provide a robust data quality process to prepare the master data prior to the data migration process across several manufacturing plants in the US and EMEA Solution Results Streamline business processes with Oracle ERP Extract master reference data from source systems, standardize, cleanse and integrate into Oracle Mostly product and inventory data to be migrated Quality of the data must exceed 98% Informatica Data Quality for: Profiling & analyzing all data Designing business and conversion rules Providing exception files Automated cleansing and standardization Creating extracts for ERP DQ monitoring over time Significant data quality improvement in weeks instead of months 37 eircom Data Governance / Stewardship Informatica Data Quality is an open product that is easy to understand and to use. It is also flexible, it can be used across different types of data sets and in different operating environments. -- Daragh O Brien, Data Quality Manager, eircom Challenge Large number of inefficient legacy systems Underperforming CRM and Business Intelligence applications due to poor data quality Solution Informatica Data Quality Ongoing name and address data cleansing from multiple systems Day to day reporting of DQ from source systems Automated cleansing of simple single line entries Reporting on key performance indicators (KPI) in individual business areas Results Increased efficiency in CRM & BI systems Reduced costs due to reduction in headcount at telephone directory unit Revenue growth 38
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