Designated in 2001 as one of the top 10 most influential IT consultants in Europe (by Info Economist magazine), Dr Wolfgang Martin is a leading authority on Business Integration, Service Oriented Architectures (SOA), Corporate Performance Management (CPM), and Customer Relationship Management (CRM).

White Paper Informatica — Data Integration The Foundation of Business Process Management Special: Performance Management in France An analysis of the VOLFGANG MARTIN TEAM powerful connections www.wolfgang-martin-team. net © 2006 S.A.R.L. Martin, 6 rue Paul Guiton, 74000 Annecy, France Informatica — Data Integration WOLFGANG MARTIN TEAM powerful connections Copyright Wolfgang Martin Team S.A.R.L. Martin authored this report. All data and information was gathered conscientiously and with the greatest attention to detail, utilizing scientific methods. However, no guarantee can be made with regard to completeness and accuracy. S.A.R.L. Martin disclaims all implied warranties including without limitation warranties of merchantability or fitness for a particular purpose. S.A.R.L. Martin shall have no liability for any direct, incidental special or consequential damage or lost profits. The information is not intended to be used as the primary basis of investment decisions. All rights to the content of this study are reserved by S.A.R.L. Martin. Data and information remain the property of S.A.R.L. Martin for purposes of data privacy. Reproductions, even excerpts, are only permitted with the written consent of S.A.R.L. Martin. Copyright 2003-2006 S.A.R.L. Martin Disclaimer Reference herein to any specific commercial products, process, or service by trade name, trademark, manufacturer, or otherwise, does not necessarily constitute or imply its endorsement, recommendation, or favouring by S.A.R.L. Martin © 2006 S.A.R.L. iviartin www.woiTgang-marlin-Team.nel ‘ Informatica — Data Integration Table of contents 1 Management Summary 4 2 Process-Orientation — The New Business Paradigm .6 3 Data Integration 3.1 Data Migration 3.2 Corporate Performance Management. 3.3 Data Integration Platform 3.4 Meta Data Management 3.5 Performance Management in France — Market Evolution and Trends 4 Data Integration with Informatica 10 10 10 12 13 14 16 5 Appendix 18 © 2006 S.A.R.L. Iviarlin www.woITgang-marlln-Team.nel ‘ Informatica — Data Integration 1 Management Summary Leading and winning enterprises have put focus on implementing their strategy by world-class business processes. This emphasizes the importance of Business Process Management (BPM). BPM means: • Compliance. New regulations like the Sarbanes-Oxley Act in the US, the international Financial Reporting Standards (iFRS) in the EC, for banking Bale ii, and for insurance Solvency ii impact financial reporting and consolidation. The required transparency and traceability can be best achieved by process-orientation. • Innovation. Business effectiveness has to be continuously examined, and creativity of management is needed for inventing mind breaking new business processes for doing the right things and leapfrogging competitors. • Optimization. Business processes have to be continuously optimized for improving business efficiency. Budgets become tighter and tighter. indeed, taking wrong decisions today ends in disasters. identifying potentials for profit, rigorously cutting cost as well as precisely calculating where to optimally spend the remaining resources are key issues not only for top management. Geopolitical uncertainties make planning much more difficult, but more important than ever. • Collaboration. Business processes do not stop at the border line of an enterprise. Indeed, the challenge is to link the customers of the customers with the suppliers of the suppliers. What is needed are end-to- end, integrated and industrialized business processes for all business activities. Winning and losing in the global market depends on compliance, integration, quality and flexibility of business processes. This is why BPM has gained attraction. BPM is implemented as a closed-loop model for managing the life cycle of business processes, from analysis and design via flow and execution to planning, monitoring and controlling. The infrastructure for implementing BPM is an SOA (service oriented architecture). The planning, monitoring, and controlling of processes and their performance is called Corporate Performance Management (CPM). BPM and CPM create automated, reliable, and flexible processes across business functions, departments and even across enterprises. This cuts cost and boosts revenues. But even more importantly, businesses can change processes with the speed of market dynamics and customer needs. You keep sailing close to the wind. The challenge is continuous adaptation of strategy and processes to market and customer demands; and moreover.. .processes must be ,,intelligent” and proactively controlled by means of predictive models. The mission is: to identify problems early enough to introduce measures to counteract them. An example from day-to-day life explains how predictive models work: in a department store, the sales areas are stocked at the right time, before products are out of stock. This avoids the situation where a customer wants to buy a product and finds himself standing in front of empty shelves. In a process oriented enterprise, CPM is the model enabling a business to continuously align business goals and processes and keeping them consistent. The concept is metrics-driven management, the methodology is CPM, and the technology is business analytics. © 2006 S.M.rl.L. iviarin £1 www.woITgang-marLln-Team.nel r Informatica — Data Integration Enterprises evolving to process-orientation by implementing BPM and CPM must not overlook a teclmical, but mission-critical pre-requisite: Data Integration. Data integration means • Building a 360° view of customer. Integrated customer data was one of the most critical success factors when moving to customer relationship management. Knowing the customer means acquiring customers with the highest potential, boosting cross up-selling and retaining the most profitable customers. • Managing product data. Consistent and comprehensive product data is the pre-requisite for all procurement, production, logistics, sales, marketing, and service processes. Updating product data like a price in one location means all processes will automatically and immediately use the updated price. This means “quality”. • Managing supplier, dealer, and employee data in the same way providing a 360° view. • Building a common business vocabulary. All constituents — partners, employees, customers, and suppliers — collaborating across end-to-end processes need this common vocabulary so that an order means the same to everybody: right products, right volume, right price, right shipment. Data integration is the foundation for multiple business solutions. it includes classic ETL (extract, transform, load) and data quality services for business intelligence as well as templates for data migration processes. Indeed, by these templates, Data Integration is inalienable for application and data warehouse consolidation, and it is also an essential pre-requisite for building an SOA. in the framework of an SOA, it provides the necessary data, master data and meta data services by its EH (enterprise information integration) capabilities. Finally, Data integration is key for compliance by its traceable and transparent services. Goal of this White Paper on Informatica’s Enterprise Data Integration Enterprises developing BPM and CPM solutions will have to decide which basic platform and components to choose for implementing BPM and CPM on an SOA infrastructure. Here, one of the key questions and mission critical decisions is the selection of a data integration platform for providing data migration process templates for application and data warehouse consolidation and for building an SOA, and for providing data services, master data services and meta data services in the framework of an SOA. The focus of this White Paper is to assist any decisions in the described environment. © 2006 S.A.R.L. iviarin www.wolTgang-marLln-Team.nel r Informatica — Data Integration 2 Process-Orientation — The New Business Paradigm in the ‘90s businesses were sold on the promise that they could run exclusively on applications. As a result, businesses became application-oriented. All business-relevant data was meant to reside in a single database and all business functions were meant to have been supported by standard (ERP) functionality. Unfortunately, this promise was not fulfilled. What lessons have been learned? • Application implementations, especially of an ERP are time and resource-intensive. Multi-million dollar projects are the norm. • “One size ERP application fits all” does not work. The majority of enterprises run several heterogeneous instances of ERP plus legacy and other systems. Enterprises have an average of 50 mission critical OLTP systems. • IT performance suffers. The huge number of interfaces necessary to link applications drives up costs for implementing new applications. The budget for maintaining these interfaces killed iT innovation. • Process automation is minimal to non-existent. Data has to be manually re-entered from application to application. This makes process quality low and results in mistakes, failures and lost money. • Process integration is modest to non-existent. Processes end at the boundaries of applications making collaboration with suppliers, partners, and customers impossible. As a result, enterprises are sluggish and unable to react to changes in the market. Costs are driven sky-high. • Changing your strategy and adapting your business processes to the speed and dynamics of the markets is impossible. Because business processes are hard-coded in the applications, if you need to change the business process, you need to change the application and every other application with which it interacts. This is not practical. Application-oriented enterprises are not agile and will ultimately lose to the competition. • Master data is caught in applications. Each application has its own business vocabulary. Product or order numbers are defined completely differently from one application to the next. Collaboration across networks of suppliers requires master data translation. Each time you add a new supplier, customer, or product you must create new translation tables and or add the new item to all the translation tables. This makes changes slow and costly. • Information management is impossible. Timely access to business information across application islands becomes a luxury enterprises can’t afford. The price of not having access to business information is even higher. Companies must be able to answer questions like: — Do you know your niost profitable custoinen and do you provide superior services to theni in order to retain them? - Do you know which of your suppliers are mission critical to your production and would their failure bring your production to a standstill for hours or even days? • Traditional business intelligence/data warehousing failed to deliver. Traditional business intelligence tools simply could not provide the right information in the right place at the right time for the right reason. They can give you a look in the ‘rear view mirror,’ but they are not designed for driving the business intelligently with predictive models and forecasts. Return on investment for these tools is low, if measurable at all. They are also difficult to master, creating a situation where information becomes privileged. © 2006 S.A.R.L. Martin 6 www.wolfgang-martin-team.net r Informatica — Data Integration Oniy a handful of experts (the power users or business analysts) are in a position to exploit information with these old tools. How can the enterprise be transformed from one that is application-based to process-based? The answer is business process management. Closed-loop management requires that execution and exception management be synchronised with continuous and comprehensive planning and monitoring. This synchronization keeps business processes optimised in line with real time events and intelligent planning and forecasting. Business processes are becoming the common communication platform between business and 11 people. For the first time we can create a genuine dialogue between business and IT. The Process-Oriented Enterprise Figure 1: Business Process Management (BPM) is a closed-loop model. Management of business processes becomes the center point of all entrepreneurial actions and activities. Processes are modeled, executed, planned, monitored, and controlled independently of the existing application framework. The infrastructure is a SOA (service oriented architecture). Corporate Performance Management (CPM) is a second closed-loop model for managing planning, monitoring and controlling of business processes and their performance within 8PM. This process-orientation is the foundation of an intelligent, agile real-time enterprise. Business Process Management creates (Fig. 1): Processes that run independently of applications. Enterprises must shift the focus to end-to-end processes across applications and platforms that are defined, monitored and controlled by integration hubs including an enterprise service bus (ESB) and a data integration platform (Dl). A critical point is that we are now dealing with cross-functional, cross-departmental, and even cross-enterprise processes. www.wolfgang-martin-team.net © 2006 S.A.R.L. Martin © 2006 S.A.R.L. Martin Informatica — Data Integration Processes that run across the underlying application data models. In order to automate event-driven processes across functions, departments, and enterprises, commonly-used application touch-points and data across the enterprise must not only be integrated and synchronised, but data models must be aggregated into a common information model to support collaboration processes. This common business vocabulary is the heart of master data management. Uniquely defined and centrally managed ‘meta’ data provides a common platform for all business terms and items across different applications and business constituents. This is essential when defining new products, gaining new customers, or adding suppliers to the business network. One simple update in the master database propagates changes safely and automatically to all related systems. • Processes that consume and publish services. The shift here is from application-oriented to service-oriented architecture (SOA — Fig. 2). For a specific business process, operational, analytical, collaborative and data services are orchestrated by a rules-based process engine. The result is that a business process either becomes a service or a group of services. • Processes that drive the transformation to intelligent real-time enterprises. Business intelligence is gleaned from metrics associated with each business process. Business metrics are defined by goals and objectives to manage a process in a measurable and proactive way with information, key performance indicators (KP1), rules, and predictive models. Example. A tactical business metric for a shipment process could he the terms of delivery, for example, a goal could be that 90% of all shipments should be fulfilled within 2 days. An operational business metric could be a predefined threshold for stock in a dealer warehouse. if stock falls below the threshold, an order is automatically executed. As the example shows, metrics are not only engineered for diagnostics (“looking backward”), but for forecasting (“looking forward”). Metrics allow processes to improve over time: enterprises can use forecasts to control processes proactively and to compare process performance to market dynamics. Metrics are designed to be consistent. Metrics specified to monitor execution of a particular group of processes must not contradict other metrics. indeed, metrics are cross-functional and cross-process: the performance of a business process may influence and interfere with the performance of other processes. For example, delivery time, a supply chain related metric, may influence customer satisfaction, a customer relationship management metric. © 2006 S.A.R.L. iviarin www.wolTgang-marLln-Team.nel r Informatica — Data Integration Role of a Service Oriented Architecture 7 Strategy and Culture I © 2006 S.A.R.L. Martin Figure 2: An SOA (service oriented architecture) is the design of an infrastructure for business process management. It consists of the integration hub supporting the life cycle management of processes and managing the back- end services including data services (DI = data integration) and meta/master data services. It also provides the B2B interface. Other business domains like content and knowledge management, office and CAD/CAM can also be incorporated via the integration hub. Corporate performance management (CPM) acts as the brains of the process- oriented real-time enterprise. It provides the “intelligence” for optimal monitoring and controlling all business processes and their performance. Intelligence is embedded into the processes for anticipating problems and risks. The PM Portal acts as the human interface. It supports human interactions through collaboration and presentation services. (PM Portal — process management portal, ESB — enterprise service bus, ERP — enterprise resource planning, CRM — customer relationship management, SCM — supply chain management, PLM — product life cycle management, DW — data warehouse, B2B — business to business) Presentation and Collaboration Services _J_•_ _- — _ BPM _ Integration I Meta Lata Ma—- B2B 4n I/Iariae me nt Backend-Services I ERP I CRM I SCM I PLM I DW LI legacy etc. 0 CD -I 0 © 2006 S.A.R.L. Iviarlin www.woITgang-marlln-Team.nel ‘ Informatica — Data Integration 3 Data Integration Data integration became an issue, when it was about to fill a data warehouse. Solutions have been extraction, transformation and load (ETL) processes. But in the times of process-orientation, data integration gained a much higher, even mission-critical importance, and a much more wide-spread usage. Let us walk through the various flavors of data integration. 3.1 Data Migration The traditional application-centric view of enterprises has created application islands. 50 and more operational applications are ruiming even in medium-large enterprises. Despite all integration efforts, this has created rather considerable data redundancy. Data redundancy is not limited to operational applications, in data warehousing, the situation is quite similar. The classic enterprise data warehouse was a dream as it was a dream running an enterprise on one single ERP instance. Enterprises are forced to reduce data redundancy for the simple reasons of cost cutting and manageability of iT systems. There are two trends in removing data redundancy. Application and Data Warehouse/Data Mart Consolidation. Reducing the number of ERP systems and data marts is a valid approach to lower data redundancy. This requires the migration of operational and analytical data into consolidated ERP systems and data warehouses. As a side effect, home grown applications can be replaced, another driver for data migration. Data migration is quite a standardized process including activities like data profiling, data cleansing, data extraction, transformation and load (ETL), as well as master and meta data migration. Enterprises should not try to apply home grown solutions for data migration. Due to the high degree of standardization of this process, a data integration solution is recommended. it comes with process templates for data migration and all necessary tools for the requested activities. Using data integration for data migration comes with obvious advantages. The process templates improve process quality and speed. it means to apply a best practice to the data migration process. Master Data Consolidation. Data redundancy can be well lowered by master data consolidation. When consolidating master data from various applications into a larger unit, redundant data can be removed. Here, data integration plays a double role. First it supports master data consolidation by a process template for master data migration. Second, it provides data services for those applications that have lost their data base by the consolidation. The data integration platform now plays the role of a virtual data base for these applications, it takes the unchanged data calls of these applications, transforms the access logic and passes it to the new consolidated data bases. in the end, this is an important step towards building an SOA. The use of a data integration platform again has obvious advantages, it means quality and speed for the process of master data consolidation. But here, use of the data integration platform goes beyond data migration. it is key for building an SOA. Hence, enterprises should not try to apply home grown solutions for master data consolidation, but to deploy a data integration solution that will later add value when building an SOA for moving to process-orientation. 3.2 Corporate Performance Management Traditional business intelligence enabled decision support in the context of strategic planning and tactical analysis. Today, process orientation operationalizes business intelligence. Operational processes are to be monitored and controlled in right time (“real time”) via intelligence (BAM — business activity monitoring). These ideas stem from control theory. As room temperature is monitored and controlled by a closed loop feedback model, business processes must be also monitored and controlled on the operational level, i.e. © 2006 S.A.R.L. Martin 10 www.wolfgang-martin-team.net Informatica — Data Integration real-time. information is treated as the duty of the information provider, in the data warehouse model, information was treated as the duty of the information consumer. Now, the provider of information can be a system or a person. it is his / her its responsibility to propagate information via the publish and subscribe communication method to all registered information consumers in right time. Example. in a web shop, product availability is a valuable metric when controlling the order process. Product availability is an operational metric, it measures the stock via sales and supply transactions. Hence, product availability is synchronized with these transactions. When product availability gets below a certain pre-defined threshold, an alert can be launched. Such an alert could trigger an additional shipment. if shipment is not an option, then the product could be blocked in the product catalogue so that customers cannot place any orders for this product. This is a pro-active action that avoids countermanding of customer orders. in the end, frustration of customers by unavailability of a product is minimized. Furthermore, the blocked product could be tagged by a note stating when the product will again be available. This example shows how to monitor and control business processes on the operational level by information. Processes are automated; manual interactions of product managers are minimized. By the way, what is the meaning of “real-time” in this example? Typically, product availability is measured twice a day. This also shows a fundamental difference between CPM and traditional business intelligence. The focus of business intelligence was tools, e.g., OLAP, spreadsheets, reporting, adhoc querying, statistical and data Business Analytics © 2006 S.A.R.L. Martin Figure 3: Reference architecture of CPM. Key is the coupling of modeling of processes and metrics as well as the top down implementation of metrics by analytical services and bottom up by data exploration. Analytical business content is delivered by templates for pre-defined metrics. Customization and development of metrics is supported by the analytical services life cycle management framework. Classical business intelligence tools become embedded components that provide services in the service oriented architecture (SQA). Data integration is the foundation for analytical services and data exploration. It provides parallel and simultaneous access of operational and analyt ica data via services within the framework of the SOA. www.wolfgang-martin-team.net © 2006 S.A.R.L. Martin 11 Informatica — Data Integration mining tools, etc. CPM comes with new methods and technologies. The goal is to empower everybody collaborating in the context of a business process by analytics without becoming a specialist in analytics. This principle does not hold only for employees, but also for suppliers, partners, dealers, and even customers. Analytics must become consumable by everybody. But this does not mean that we do not need power users and business analysts any more. They will continue to support the business, but their role is changing. One of their new tasks will be to manage analytical services in an SOA and to provide the interface between business and iT for the management of business components of analytical services (Fig. 3). The other fundamental difference between CPM and classic BI is the access to data. Whereas classic Bi sat on a data warehouse and used a data warehouse as its single point of truth, CPM now sits on a data integration platform providing compound data services that combine data warehouse data with any operational data. 3.3 Data Integration Platform it has been common practice to supply a data warehouse supplied by ETL processes. ETL processes are either supported by batch and / or message / queuing, depending on whether time is critical for data supply. This will continue, and this is one task that is still addressed by data integration platforms. But now we need more. We need data services (Fig. 4) enabling the simultaneous access of data warehouse and operational data. in the past, one has tried to solve this time critical data access problem via an ODS (operational data store). Using the ODS approach is not always sufficient, because storing data in an ODS already costs time, and unfortunately, business logic needed for calculating more complex metrics may be hidden in the application logic and is not available on the data level. Figure 4: Real-time data integration bridges traditional data warehouse architectures and operations. ESS = enterprise service bus, LLDM = low latency data mar, ODS = operational data store, OLAP = online analytical processing. Innovative enterprises already use real time data propagation to drive operational systems with cross-process metrics via a data integration platform. www.wolfgang-martin-team.net Real-Time Data Integration Operational Data r I Operational CPM (BAM/PPM) © 2006 S.A.R.L. Martin © 2006 S.A.R.L. Martin 12 Informatica — Data Integration Data integration can be either low latency or zero latency data integration. So, the key point is first to determine what latency can be tolerated for a given process. Note that latency is correlated with cost: the lower the tolerated latency, the higher the cost. The low latency model is based on a data integration platform that collects all relevant transactional data and analytical data and stores it in a so-called low-latency data mart (LLDM). This requires integration of the data integration platform with the ESB where the processes across all backend applications are managed. The LLDM is refreshed either by message queuing or by batch, where the batch is executed in short periods according to the tolerated latency (e.g., hourly etc.). innovative “real-time” enterprises use the LLDM for real-time data propagation. This is a feedback loop for triggering events in operational systems via cross-process metrics. This coupling with operational systems requires managing the data integration platform like the ESB platform: The data integration platform is an operational system. This model is different from an operational data store (ODS) where data from operational data bases only is stored via ETL processes. So, all transaction logic that is not stored in the operational data bases cannot be mapped to operational data stores. Furthermore, The ETL process is not synchronized with the transactions, i.e. ODS data is not always in synch with the state of transactions. This stresses the need for low latency data marts, especially in the case of legacy systems. The zero latency model is also called Eu (enterprise information integration), it can be understood as a logical data base access layer spanning across all operational data bases and the data warehouse providing data as services. The access is done via XML and the Eli resolves the data request into various SQL statements accessing the corresponding data bases and transforming the data so that the requested compound data is published as a service and available for the process. indeed, such a data service could be also implemented as a web service. 3.4 Meta Data Management Process-orientation needs meta data management. The meta data layer spans all layers of the SOA. Meta data is key to a consistent data model including life cycle management for a consistent comprehension and communication of the data model, for data quality, data protection and security. Meta data builds the business vocabulary of the enterprise and even across enterprises. Meta data is organized by three layers: • Layer 1 — Business Meta Data: Meta data on business structures and operations, i.e. definitions (structures, e.g., suppliers, customers, regions, products, etc.) and business rules, e.g., “How to calculate ‘revenue’?”, “What data belongs to what structure (regions, products etc.)?” • Layer 2 — Navigational Meta Data: Meta data on navigation (e.g., sources and targets of data, cross references, time stamps) • Layer 3 — Administrational Meta Data: Meta data on administration (information profiles including responsibility, security etc., monitoring and controlling usage) The business vocabulary plays a central role. it controls both, BPM and CPM: Processes and metrics need a common and uniquely defined language for modeling and for communication to all business constituents in collaboration contexts. The repository as container of all meta data plays the role of the integration hub for the meta data of all back end systems in the BPM model. When services of back end systems are invoked by an integrated solution representing the integration logic and the flow of the process, then they must speak to each other in the same language that is based on the business vocabulary of the repository. © 2006 S.A.R.L. Martin 13 www.wolfgang-martin-team.net Informatica — Data Integration A point-to-point communication would again lead into the chaos of isolated islands. The only solution is to transform the meta data model of each back end system into the central business vocabulary of the repository of the BPM integration hub. Then, all back end systems can speak to each other and adding additional back end systems becomes straight forward, easy, and fast. Meta data standards are slowly evolving: in Sept. 2000, the Meta Data Coalition initiative merged with the CWM (common warehouse model) of the 0MG (object management group). Despite the slow progress, there is no alternative to this standard. Meta data and master data are not static. On the contrary, any merger and acquisition, any market change, any internal organizational restructuring, any update of a business definition and rule creates new meta data and master data. But it is absolutely insufficient just to update meta data and master data and store the most recent and actual version in the repository or in a data base. For enterprise planning and for any comparisons between past, now, and future, the availability of the total life cycle of all meta data and master data is a must. This is why meta data management and master data management are to be based on life cycle management. The repository must include the life cycle of all meta and master data. Today, this is a weak point, sometimes even a gap in vendor offerings and enterprise architectures. 3.5 Performance Management in France — Market Evolution and Trends Year 2005 will have been year of regulatory rules implementation: From iFRS accounting standards with opening balance beginning of 2005, with Sarbanes Oxley compliance, with the Loi de Sécurité Financiêre (LSF) specific to France, with Basel H regulatory constraints for banking sector, this year has been very busy with regulatory constraints. Since a couple of years, almost all large organizations in France were convinced of the need to have reliable, consistent, trustworthy information across the enterprise. Those who were not fully convinced have been forced to change their mind this year. There are no more questions about the need of access to reliable information. The Corporate Performance Management market is still dominated by 2 major vendors, Cartesis and Hyperion, with couple of outsiders like Outlooksoft. The trend is clearly development of application suite, with more and more integration between statutory consolidation, group reporting, business planning and budgeting and other Bi applications. Even if there have been significant announcements by vendors this year, there is clearly a non covered demand of integration for those tools. A lot of large projects are planned in the area of Corporate Performance Management for 2006 and 2007 by large French based companies. Those projects are most likely planned by those companies having chosen to separate their iFRS implementation and their group reporting system replacement. There is still a real challenge on the market to find the right level of resources and competencies for this kind of projects. in classical Business intelligence, most companies are struggling to implement transversal approach for information management. Even if the need to restructure silo data marts developed in the past is now clear, there is still a heavy historical legacy, based on silo data repositories, and things are changing progressively. Besides the need to develop a transversal view of information across the enterprise, the main challenge on this area is technical. With the increasing complexity of iT systems, reduction of cost, and reduction of infrastructure investment, data integration projects are sometimes difficult to justify and launch © 2006 S.A.R.L. Martin 14 www.wolfgang-martin-team.net Informatica — Data Integration because they rely on infrastructure investment. There is still a real market demand in France in the area of Master Data Management (MDM) in order to develop a shared and collaborative “Master Data Dictionary”, and also in the area of Enterprise information integration (Eu) where progress is fast, but demand is still not covered, in this area, solutions brought by Data integration Tools or ETL Tools are improving and delivering real added value. The main challenge for year 2006 and probably at least 2007 will be the integration between Corporate Performance Management and Business intelligence. This need of being able to share common views between the different types of reporting and analysis across the enterprise, the need to ensure that there is a unique reference for financial information (one version of the truth), the need to add non financial information in the same environment is clear. Real solutions are not so clear despite the fact that there is a strong marketing from vendors on this specific topic. in this context, the market should continue to see more and more acquisition, with probably larger M&A operations than in the past years. This is caused and probably justified both by the market demand in integration of application, the need to reduce costs and the objective of large organisation to reduce number of their suppliers © 2006 S.A.R.L. iviarin www.wolTgang-marLln-Team.nel r Informatica — Data Integration 4 Data Integration with Informatica informatica is well-known for and has gained high market reputation with its leading ETL solution. Now, informatica addresses the next challenge for data integration by its Enterprise Data integration platform. With Enterprise Data integration, informatica continues to address the data warehouse and data migration needs, but it now also addresses the need for master data, meta data, and data services in an SOA. indeed, informatica has continuously evolved and expanded its traditional ETL engine to a complete data integration platform (Fig. 5). Informatica: Platform for Service Oriented Data Integration Data Services q Web Services, Messaging, JDBC, ODBC < . __________________________________ )D) (j) . Data Integration Services . Data Profiling, Data Cleansing, Data Transformation, I— u a Data Movement, Data Federation G) • . 0 u Packaged Applications, Mainframe, RDBMS, . Messaging Systems, Flat Files E — (Structured, Unstructured & Semistructured Data) ERP Databases Messages Flat Files XML Unstructured Mainframe Data Source: Informatica Figure 5: Inform atica’s Enterprise Data Integration technology is a state-of-the-art data integration platform for master data, meta data, and data services in a SOA. It is also implemented as a SOA, and it is complemented by an organizational concept, the Integration Competency Center, a center of Excellence for managing the data integration tasks. Strengths of informatica’s Enterprise Data integration (PowerCenter 8) are in particular Data Services - An informatica data service can be a master data service, a meta data service, and or a “pure” data service, i.e. informatica’s data integration tools can be applied to any type of data. - A data service can be a workflow orchestrating data services. informatica provides templates of such workflows for data mapping, data migration, ETL, SAP data extraction etc. - Meta data services include publishing of data lineage and cross references as data services - Data services can be web services, but can be also published and accessed by JDBC, ODBC, messaging etc. © 2006 S.A.R.L. Martin 16 www.wolfgang-martin-team.nei Informatica — Data Integration Data Integration Services - informatica offers both, low latency and zero latency data integration - Domain concepts provide easier and more flexible administration of services - Fail-safe and scalable performance by grid capabilities and load balancing - Performance optimizer can allocate mapping tasks to be fully executed in the database or all in PowerCenter Universal Data Access - High performance by change data capture - Access to all types of data, unstructured data included Summary. Informatica is one of the world’s leading specialists in data integration. It has gained its market reputation by its ETL (extraction, transformation, load) processes for filling data warehouses in the ‘90s. By continuously evolving and expanding this technology for “turning information into insight”, Informatica was one of the first vendors that realized the need for providing data services in the context of a service-oriented architecture (SOA) beyond the classic ETL and data migration tasks. Data services in an SOA provide the infrastructure for building the information supply chain: Provide the right information for the right purpose to the right location in the right time for the right information consumer. This is information integration linking the suppliers of the suppliers with the customers of the customers — the foundation for business process management. Data services supply all internal and external processes with up-to-date and/or real-time information and provide the audit-proof, single point of truth. This infrastructure is the prerequisite for saving costs and boosting revenues by collaborative processes that are • compliant and auditproof • automated and integrated • end-to-end and synchronized • flexible, i.e. built for change Informatica’s technology, its “Data Integration Platform”, is based on an SOA, and it comes with a methodology for orchestrating data services for various IT processes like classic ETL, SAP data extraction, data migration, master data and meta data services etc. Informatica compliments its technology and methodology by its roadmap for building and running the Integration Competency Center, the best practice organisational unit within an Information Technology (IT) group for information management. © 2006 S.A.R.L. iviartin ii www.woiTgang-marlin-Team.nel ‘ Informatica — Data Integration 5 Appendix Related Reading Martin, W., Nul3dorfer, R.: PM Portals — Collaboration and Presentation Services: Pulse Check — Processes and People, iBond White Paper Vol. 4, www.eaiforum.de, Munich, 2005, 30 pages Martin, W, Nul3dorfer, R.: Corporate (Business) Performance Management: Pulse Check — Operational, Tactical, and Strategic CPM, iBonD White Paper Vol. 2, www.eaifonim.de, Munich, 2005, 38 pages Nul3dorfer, R., Martin, W.: RTE — Real-Time Oriented iT Architecture: All Together Now, Strategic Plaiming of iT Architectures, iBonD White Paper Vol. 1, \\\\\v.eaiforurn.de 2003, Munich, 35 pages Nul3dorfer, R., Martin, W: iSO — integrated Solutions: All Together Now, End-To-End Processes Across integration Hubs, iBonD White Paper Vol. 3, www.eaiforum.de Munich, 2004, 41 pages Cost-free download of these white papers at www.wo1fgjjg-martin-team.net IN FO RM ATI CA The Data Integration CompanyTM Informatica Corporation (NASDAQ: iNFA) is a leading provider of enterprise data integration software. Using informatica products, companies can access, integrate, migrate and consolidate enterprise data across systems, processes and people to reduce complexity, ensure consistency and empower the business. More than 2,300 companies worldwide rely on informatica for their end-to-end enterprise data integration needs. For more information, call +1 650 385 5000 (+1 800 970 1179 in the U.S.), or visit www.inforrnatica. corn About the Authors Dr. Wolfgang Martin Designated in 200 lone of the top 10 most influential iT consultants in Europe (by info Economist magazine), Wolfgang Martin is a leading authority on Business integration, Service-Oriented Architectures (SOA), Business intelligence (Bi), Corporate Performance Management (CPM), and Customer Relationship Management (CRM). After 5’/2 years with META Group, latterly as Senior Vice President © 2006 S.A.R.L. Martin 18 www.wolfgang-martin-team.net Informatica — Data Integration international Application Delivery Strategies, Mr. Martin established the Wolfgang Martin Team. Here he continues to focus on technological innovations that drive business, examining their impact on organization, enterprise culture, business architecture and business processes. Mr. Martin is a notable commentator on conference platforms and in TV appearances across Europe. His analytic skills are sought by many of Europe’s leading companies in consulting engagements. A frequent contributor of articles for iT journals and trade papers, he is also an editor of technical literature, such as “Data Warehousing — Data Mining — OLAP” (Bonn, 1998), “Strategic Bulletin on EAi” (Munich, 2002, 2003 & 2004), ,,Strategic Bulletin on CRM” (Munich, 2002, 2003 & 2004), “Strategic Bulletin on Bi” (Munich, 2003, 2004 & 2005), ,,Jahresgutachten CRM”, (Würzburg, 2002, 2003, 2004 & 2005). Prior to META Group, Wolfgang Martin held various management positions with Sybase and Software AG, responsible for business development, marketing and product marketing. Prior to this, he became an expert on decision support while with Comshare. His academic work included Computational Statistics at the Universities of Bonn (Germany) and Paris-Sud (France). Dr. Martin has a doctoral rer.nat. degree in Applied Mathematics from the University of Boim (Germany). FrédéricDoche Frédéric Doche is president and founder of Decision Performance Conseil. He has over 20 years of industry and consulting experience. Decision Performance Conseil has a team of veteran senior consultants specialized in Business Intelligence, Project Risk Management and iT strategy. Before joining Decision Performance Conseil, Frédéric Doche was partner with PricewaterhouseCoopers, responsible for the EMEA (Europe, Middle-East, Africa) practice in Business intelligence and CRM Analytics. in addition to managing key assignments for leading multinationals in various industries, he is a key speaker and moderator in conferences as well as author in Business intelligence, integrated Analytics approach and CRM Analytics. He also gives course on Business intelligence and project management. Prior to his consulting role, Frédéric Doche has worked in industry and banking, mainly in process improvement and iT management. Frédéric is a member of DFCG (Finance Directors and Controllers Association) © 2006 S.A.R.L. iviartin www.woiTgang-marlin-Team.nel