Volume 2: CPM Corporate (Business) Performance Management

Transcription

1 The ibond Series intelligent Business on Demand Volume 2: CPM Corporate (Business) Performance Management White Paper Pulse Check : Operational, Tactical, and Strategic CPM Part 1: Vendor Independent White Paper and Reference Architecture English Version 2.0 March 2005 Authors: Dr. Wolfgang Martin Wolfgang Martin Team, S.A.R.L Martin, Annecy Richard Nußdorfer CSA Consulting GmbH, München Sponsored by

2 Preface The present White Paper CPM Corporate Performance Management is the second white paper of the series ibond intelligent Business on Demand. It describes the business and technical architecture of operational, tactical, and strategic CPM. CPM is defined as a model enabling a business to continuously align business goals and processes and keeping them consistent. CPM works as a closed-loop model for managing the performance of business processes on the operational, tactical, and strategic level, i.e. planning, monitoring, and controlling. From a business point of view, this is one logical model, but from a technological point of view, rather different technologies from vendors with completely different roots are clashing together: Traditional business intelligence vendors meet business integration vendors. CPM is also called business performance management - BPM. These two terms are absolutely equivalent. We prefer the term CPM, since the abbreviation BPM has multiple meanings, e.g., business process management and business process modeling. We will always use the term CPM in this white paper, and readers used to the term business performance management should always understand this equivalence. We will also use the term BPM, but in this white paper, BPM will always mean business process management. Traditional Business Intelligence Vendors These are vendors who used to act in the market of traditional business intelligence tools and data warehouse and who have evolved into CPM vendors. First, these vendors addressed tactical and strategic CPM. Today, they are moving to operational CPM, too (sometimes called BAM business activity monitoring). Traditional Business Integration Vendors Vendors of integration platforms (see Nußdorfer, Martin, 2003) have started to offer first operational CPM solutions using the labels BAM and PPM (process performance management). Their challenge is to put CPM metrics into the financial business context. Best of Breed Products In addition to the holistic approaches of the vendors of the two camps, there are specialists offering best-of-breed tools and technologies. These products are especially interesting when business integration platforms do not include operational CPM features, but provide interfaces for accessing transactional information for BAM or PPM purposes. Goal of the CPM White Paper Enterprises developing CPM solutions will have to decide which basic platform to choose for CPM in the context of ibond and which additional best-of-breed-products will be required. The focus of this series of White Papers will be to assist any decisions in the described environment. Both authors have lots of experience in IT Business in Management functions as analysts and business-oriented project leaders. They both have many years of practical experience in BPM White Paper / R.Nußdorfer / Dr. W. Martin 3/31/2005 Page 2

3 addressing and dealing with strategic deliberations and future developments. The present CPM-White Paper is divided into two parts. First there is a general part describing the concepts and facilities of CPM as well as its reference architecture. The second part describes vendor platforms necessary to realize the mentioned reference architecture. To enable readers getting a fast survey of the actual market, the authors have created a separate description for each vendor, but an identical agenda is applied to each of the vendor related papers. Version 1.2 of this white paper was published in August The new version 2.0 is a completely reworked and updated version taking into account the fast evolution of the CPM market. The authors will be delighted to receive reader feedback, commentary, criticism - and of course compliments! Munichünchen, March 2005 Annecy, March 2005 Richard Nußdorfer Managing DirectorGeschäftsführer CSA Consulting GmbH Dr. Wolfgang Martin Wolfgang Martin Team BPM White Paper / R.Nußdorfer / Dr. W. Martin 3/31/2005 Page 3

4 The authors biographies: Dr. Wolfgang Martin Biography Recently designated one of the top 10 most influential IT consultants in Europe (by Info Economist magazine), Wolfgang Martin is a leading authority on Customer Relationship Management (CRM), Enterprise Application Integration (EAI), Business Intelligence (BI), and Business Performance Management (BPM). He is a founding partner of ibond Ltd, and a Ventana Research Advisor. After 5½ years with META Group, latterly as Senior Vice President 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), "Jahresgutachten CRM", (Würzburg, 2002, 2003 & 2004). 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 Bonn (Germany) S.A.R.L. Martin Richard Nussdorfer Biography Richard Nussdorfer has worked for more than 30 years in the IT-industry. His current expertise includes Business Integration (EAI), Client/Server- Architectures (C/S) and strategic planning of IT-Architectures (RTE). He is a founding partner of ibond Ltd. Richard s technical knowledge has been used extensively for integration projects, modernizing IT-Architectures, re-centralizing Client/Server- Architectures to Web-Architectures. He has published 2 e-books: Information-Technology and the EAI-Book. He regularly contributes articles to IT journals and is asked to speak at numerous congresses and seminars such as EAI, DataWarehouses and WebServices. He is a key source of knowledge on EAI on the internet (see ) Richard Nussdorfer s professional experience started in 1970 at Siemens AG in software development. He then continued as an expert on databases and project leader for database projects, nationally and internationally, from London to Moscow and from Stockholm to Johannesburg. His professional career continued as manager for Software-Marketing in Munich and Business Development Manager in South Africa. From 1990 to 1993 he worked as a consultant for Plenum AG in strategic IT-projects. In 1994 he founded CSA Consulting GmbH where he works today as Managing Director. Richard Nussdorfer has a degree in computer science from the Technical University in Vienna (Austria) ibond Partnership BPM White Paper / R.Nußdorfer / Dr. W. Martin 3/31/2005 Page 4

5 Copyright CSA Consulting GmbH/Richard Nußdorfer and S.A.R.L. Martin/Dr. Wolfgang 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. CSA Consulting GmbH and S.A.R.L. Martin disclaim all implied warranties including without limitation warranties of merchantability or fitness for a particular purpose. CSA Consulting and 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. CSA Consulting GmbH and S.A.R.L. Martin reserve all rights to the content of this study. Data and information remain the property of CSA Consulting GmbH and S.A.R.L. Martin for purposes of data privacy. Reproductions, even excerpts, are only permitted with the written consent of CSA Consulting GmbH and S.A.R.L. Martin. Copyright 2003/2004/2005 CSA Consulting GmbH, Munich/Germany and S.A.R.L. Martin, Annecy/France 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 favoring by CSA Consulting GmbH and S.A.R.L. Martin. BPM White Paper / R.Nußdorfer / Dr. W. Martin 3/31/2005 Page 5

6 Contents 1 MANAGEMENT SUMMARY 7 2 CORPORATE PERFORMANCE MANAGEMENT STRATEGIES, PROCESSES, MEN AND METRICS 9 3 CPM METHODS AND TECHNOLOGIES CPM Business Components CPM Technical Components 17 4 INFORMATION DEMOCRACY 20 5 CORPORATE PERFORMANCE MANAGEMENT THE REFERENCE ARCHITECTURE 23 6 LATENCY MATTERS 27 7 CPM AND CLASSICAL BI: FUNDAMENTAL DIFFERENCES 30 8 PLAYERS IN THE CPM MARKET 31 9 SUMMARY THE SPONSORS 34 BPM White Paper / R.Nußdorfer / Dr. W. Martin 3/31/2005 Page 6

7 1 Management Summary In economical down times, 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. 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. What is the next strategic move to master these challenges? One answer is Corporate Performance Management (CPM), the topic of this second white paper of the ibond ( intelligent business on demand ) initiative. ibond explains what makes up winners in the markets. Winners do: Focus on customers Strip away low value activities Decentralise decision making Speed up processes Collaborate with suppliers, partners, customers and adopt corporate performance management according to the leitmotiv: You can only manage what you can measure To summarize: Processes make up the competitiveness of an enterprise. Winning and loosing in the global market depends on quality and flexibility of business processes. Processes become the new focus of management (see Nußdorfer, Martin, 2004). Business Process Management (BPM) is the answer, a closed-loop model describing the development life cycle of business processes, from analysis and design via flow and execution to planning, monitoring and controlling. The task of CPM within BPM is planning, monitoring, and controlling of processes and their performance. 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 important, business 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 up at the right time, before products are out of stock. This avoids the situation where a customer BPM White Paper / R.Nußdorfer / Dr. W. Martin 3/31/2005 Page 7

8 wants to buy a product and finds himself standing in front of empty shelves. In a process oriented enterprise, CPM and BPM must go together. CPM is the business 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. Corporate performance management is fundamentally different from the traditional business intelligence approach for decision support, executive information, and reporting. Traditional business intelligence tools (reporting, adhoc querying, OLAP online analytical processing, data mining etc.) failed to deliver the right information to the right location in the right time for the right purpose. Traditional business intelligence tools did not meet management expectations: results to be applied to processes and strategy for turning information into value. Return of investment (ROI) in the old tools was typically rather low, if measurable at all. Traditional business intelligence tools were difficult to master. Information remained a privilege in many enterprises. Only a handful of experts (the power users or business analysts) were in a position to exploit information via the old tools. Management decisions and actions were based on guesses, much less on facts. Corporate performance management is a new approach based on business intelligence for optimally planning, monitoring and controlling business processes and their performance on the level of operations, tactics, and strategies. CPM is based on metrics associated with the processes. CPM starts when designing and engineering processes: metrics have to be derived simultaneously and in parallel with the operational process design. Goals have to be metricized. Achievement of goals has to be continuously monitored. Actions must be taken for controlling the performance of processes. CPM is a closed-loop model. CPM provides clear benefits to an enterprise: It is a methodology to link strategy to results. It turns data into actionable information. It empowers all staff by delivering information not only to power users and business analysts, but to everybody inside and outside the enterprise ( information democracy ). It delivers high degree of accuracy and consistency of information. It provides transparency to management and enhances the bottom line. It delivers the right information to the right information consumer to the right location in due time (This is called real-time ). BPM White Paper / R.Nußdorfer / Dr. W. Martin 3/31/2005 Page 8

9 2 Corporate Performance Management Strategies, Processes, Men and Metrics Corporate Performance Management puts business intelligence into the context of strategies, processes and men and uses metrics for measuring the performance of processes. For a better understanding of the CPM model, let us start with processes. Today s enterprises must be process-oriented (Nußdorfer, Martin, 2003). Today, business processes are cross-functional, cross-departmental, and even cross-enterprise. Processes link the suppliers of the suppliers to the customers of the customers within an integrated network of enterprises. The benefits of integrated end-to-end processes are obvious: Cutting costs by automated, straight through engineered processes Speeding up time-to-market and process throughput by integrated processes Minimizing risk by high quality processes Maximizing agility by continuously tuning of process models to market dynamics This is why Business Process Management (BPM) is the most important challenge for today s enterprises. BPM is a closed-loop model consisting of three phases: Phase 1: Analyzing, planning, modeling, testing, and simulating business processes Phase 2: Executing business processes by cross-application workflows by a process engine on a SOA (service oriented architecture) infrastructure Phase 3: Planning, monitoring and controlling the performance of the ensemble of all business processes So, BPM and CPM are the process-oriented latest version of managing an enterprise: planning, execution, and performance management have always been the three basic categories of all management ( make a plan, execute it, and manage to keep the actual in line with the plan ). CPM within the BPM model is all about managing the performance of all processes that extend across all functions within a business, and beyond to all other relationships. Metrics-oriented management is the top down principal of CPM for optimal enterprise management by a closed-loop approach. Business strategy determines which business processes are to be executed and managed by the enterprise. Business metrics are associated with each business process. Business metrics are defined by goals and objectives to manage a process in a measurable way with information, performance indicators, rules, and predictive models. BPM White Paper / R.Nußdorfer / Dr. W. Martin 3/31/2005 Page 9

10 Example: A tactical business metric for a shipment process could be the term of delivery, where a goal could be that 90% of all shipments should be 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. In CPM, metrics may be anticipative as this example shows. The Process-Oriented Enterprise Execute Rules Based, Application Independent Process Engine collaborative business process Business Process Management collaborative business process Model Analysis, Design, Test, Simulation Corporate Performance Management Plan, Monitor & Control Metrics, Business Analytics S.A.R.L. Martin 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 the planning, monitoring and controlling of business processes and their performance within BPM. This process-orientation is the foundation of an intelligent real-time enterprise. Furthermore, the examples show the impact of metrics-oriented management to information management. Information has to be available in right-time (often called real-time, see Nußdorfer, Martin, 2003) for triggering manual or automated decisions for process control. This corresponds to the information supply chain paradigm: supply the right information in the right time to the right location to the right information consumer to trigger the right decision. So real-time means synchronization of information supply with information demand (Note: real-time is a relative term and not necessarily related to clock-time). Business metrics represent management policies within metrics-oriented management. The idea behind is obvious: You can only manage what you can measure. So, flexibility of changing and updating any metrics is one of the top requirements of the model. Furthermore, BPM White Paper / R.Nußdorfer / Dr. W. Martin 3/31/2005 Page 10

11 business metrics must be consistent. Metrics specified to control the execution of a particular group of processes should 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. CPM: Strategy, Goals, Processes, Metrics Strategy Events Business Process Goal Final Result Cycle Speed Act Decide Measure S.A.R.L. Martin Figure 2: Metrics-Oriented Management is a top down model for information-based business management. Measurable goals and objectives are derived from the strategy. Based on strategy, goals and objectives, business processes and business metrics for efficient process control and continuous optimization are modeled in parallel. Technical implementation of processes and metrics follows the principles of a SOA (service oriented architecture) by operational and analytical services. Based on monitoring, decisions are taken either manually by man or automatically by decision engines. Decisions lead to actions for controlling the process and its performance (tactical and operational BPM) as well as updates strategy, goals and objectives (strategic BPM). Synchronizing monitoring, decision and action taking with the speed of the business process and business dynamics is key indeed, this is a foundation of the real time enterprise. These issues are addressed by business scorecards. A business scorecard aligns all management policies across the enterprise and presents the aggregated top management policy of the enterprise as well as all details for all employees. Examples of particular business scorecards are Norton/Kaplan s balanced score card or the six sigma model. The balanced scorecard, for instance, is a collection of metrics that is not only based on financial parameters, but uses also customer, employees and shareholders loyalties to provide a look BPM White Paper / R.Nußdorfer / Dr. W. Martin 3/31/2005 Page 11

12 to the corporate performance beyond the quarterly results. It presents indeed one particular style of metrics-driven management. Despite the wide variety of these metrics, the final goal remains the same: transform data into information and knowledge and maximize its value for the business. CPM is applied to all business domains like customer relationship management, supply chain management, human relations etc. Example: Financial Performance Management like any other analytical solution is a closed loop process depicting the information management of financial information. The process stretches from planning, budgeting, and forecasting to performance measurement and auditing via financial metrics including the statutory legal financial reporting and consolidation requirements. Performance management includes profitability analysis, and planning includes simulations and what if analysis. Decisions are then made based on the financial metrics and analysis and fed back into the planning, budgeting and forecasting activities: The loop is closed. As Fig. 2 already implies, CPM takes place on three levels, the operational, tactical, and strategic level (Fig. 3). Operational CPM is also called Process Performance Management (PPM), and it includes Business Activity Monitoring (BAM) (Note: a business process consists of a group of activities, where the group structure defines the workflow). Operational CPM has been addressed first by vendors coming from process engineering and business integration by adding reporting and graphical features for visualizing operational performance indicators. Today, this approach lacks to put the indicators into a business context. To close this gap from a business point of view, activity based management is a prerequisite for putting the metrics into a monetary context. This means technically to have access to financial data in a data warehouse. Tactical and strategic CPM was first addressed by the vendors of traditional business intelligence by moving from the data warehouse model and business intelligence tools to analytical applications and closed loop processing. Today, the two independent approaches to one and the same problem are confusing the market, but will converge by BPM White Paper / R.Nußdorfer / Dr. W. Martin 3/31/2005 Page 12

13 CPM Temporal Layers Bottom up Project driven Top down Methodology driven Strategic Planning Strategic CPM long term Tactical CPM mid term days, weeks, months Operational CPM (BAM) short term same day at least Tactical Analysis Operational Actions S.A.R.L. Martin Figure 3: Corporate Performance Management (CPM) is the process of managing the performance of business processes by applying metrics, deciding on the outcome of the metrics, and launching actions for controlling the performance and/or the process, a closed loop model. (Note: a business process is considered as a group of activities, where the group s structure is defined by a workflow.) One key issue for all CPM approaches is to put the metrics into a monetary context. This requires process-oriented accounting principles like activity based management/costing. CPM spans from operational to strategic CPM, but is addressed by two separate camps of vendors rushing to exploit the new opportunities of a strongly growing analytics market. These are the Business Integration vendors providing BAM solutions, and the Business Intelligence vendors with their traditional focus on tactical and strategic solutions. This is confusing business and IT people looking for real solutions to solve their more and more complex analytical needs. BPM White Paper / R.Nußdorfer / Dr. W. Martin 3/31/2005 Page 13

14 3 CPM Methods and Technologies In the past, business intelligence enabled decision support in the context of strategic planning and tactical analysis. This was done by metrics designed for long term outlooks. The basic concepts were to measure and to monitor the achievements of strategic goals, for example customer satisfaction, customer value, term of delivery, supplier value, staff fluctuation etc. Long term here relates to the dynamics of the process. Question is how fast can actions influence the process and change the indicators significantly. This is why there is a tactical level. Achievement of tactical goals can be considered as mile stones towards the strategic goals. Actions targeting the achievement of tactical goals typically address a time frame between some few days to several months. 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 controlled by a closed loop feedback model, business processes shall be monitored and controlled on the operational level, i.e. real-time. Figure 3 depicts the 3 levels of CPM. The real-time principles of the information supply chain enable monitoring and controlling of operational systems. 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 storno of customer orders. In the end, frustration of customers by unavailability of a product is minimized. Furthermore, the blocked product could by tagged by a note stating when the product will be again 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. Focus of business intelligence was tools, e.g., OLAP, spreadsheets, reporting, adhoc querying, statistical and data mining tools, etc. CPM comes with new methods and technologies. Goal is to empower everybody collaborating in the context of a business BPM White Paper / R.Nußdorfer / Dr. W. Martin 3/31/2005 Page 14

15 process by analytics without becoming a specialist in analytics. This principle does not held 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 the technical components of CPM and to provide the interface between business and IT for the business components of CPM (Fig. 4). Business Analytics Meta Data Metricized Goals Metrics Analytical Applications Analytical Business Content Business Intelligence Components Application Life Cycle Management Strategy Data Exploration Business Intelligence Tools Datenintegration S.A.R.L. Martin Figure 4: Technical architecture of CPM. It provides a view on the CPM reference architecture for comparing products and offerings of the various vendors for planning / developing, executing and managing of analytical applications and data exploration. Key is the coupling of modeling of processes and metrics as well as the top down implementation of metrics by analytical applications 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 application life cycle management framework. In the framework of analytical applications, business intelligence tools become embedded components that provide services in the service oriented architecture (SOA). Data integration is the foundation for analytical applications and data exploration. It provides parallel and simultaneous access of operational and dispositional data via services within the framework of the SOA. 3.1 CPM Business Components Metrics and Key Performance Metrics Metrics are used to manage the performance of a process and / or to control a process. They are top down derived from metricized goals out of BPM White Paper / R.Nußdorfer / Dr. W. Martin 3/31/2005 Page 15

16 strategy and process analysis. Metrics consist of indicators and scales. Scales define how to interpret instantiations of indicators and what decisions to take. A key performance metric (KPM) is a compound, cumulated metric. Term of delivery is an example for a KPM. It is cumulated of detailed metrics of time of delivery across all customers within a certain time period. Typically, an employee will have a lot of detailed metrics, but just some selected KPMs. KPMs should be related to the personal goals and match the model of management by objectives. In the end, KPMs could have an impact to components of the salary. In the example about term of delivery as a KPM, a decision maker is responsible for the interpretation of the KPM, the making of decisions, and taking actions. In case of such a human interaction, scales are typically visualized by traffic lights and / or speedometers. Green, yellow, and red lights ease and speed up the interpretation of instantiations of KPMs and metrics. In the example about managing the order process by product availability, the interpretation is automated by a decision engine visualization is not necessary. Business Rules They represent the knowledge and expertise of an enterprise. They control the transitions between the various states of business processes. Modeling of rules is either top down by an expert system type approach or predictive models are bottom up generated (e.g., a customer behavior model by a data mining process). Ultimately, rules can be modeled by a combined top down, bottom up approach aligning predictive models with expert rules. Business rules must be managed centrally and independently of business processes. When business rules are implemented into the processes, then a chaos for maintenance of rules is inevitable after some short time. Scorecard This is a consistent and comprehensive group of metrics for monitoring and controlling a group of processes or even the total enterprise. Consistency of metrics is very important, because metrics should not be contradictory and cause conflicts between collaborative teams working in different contexts. The term scorecard was first developed for strategic CPM, but is now used for all levels of CPM. Known models of scorecards are the balanced scorecard of Kaplan and Norton ( Baldridge s scorecard model ( and the Six Sigma model ( It should be noted that he majority of enterprises does not exactly apply one of these models, but uses its own customized scorecard model that is a derivative of one of these models. Event / Alert When an event / alert occurs, the information describing the event / alert is automatically propagated to all recipients that have subscribed to receive this information. This is set up in the publish and subscribe communication method using message / queuing infrastructure. The principle of this communication method is defined by the information supply chain model. All information that is necessary to process the event / alert should be available to all recipients in right time for making the right decision and taking the right action. Again, right time means to synchronize the speed of the process with the delivery of information via the propagation. If speed is high, and the delta between event / alert and decision / action becomes small, then a human interaction may be to slow: The decision / action taking must be automated. Examples for automated decision / action taking can be found on various web sites where recommendation engines are working. BPM White Paper / R.Nußdorfer / Dr. W. Martin 3/31/2005 Page 16

17 Data Exploration This is a bottom up development environment for metrics and predictive models. It is based on a portfolio of business intelligence tools acting on top of the data integration platform. Data Exploration is an adhoc, temporary, project-oriented process. The results of data exploration provide new analytics, e.g., profiles, rules, scores, and segmentation for a better insight into markets, customers, risks etc. Good example for data exploration is the development of predictive models by data mining. The final predictive model is then implemented in a rules engine controlling an operational process. Example. Let us consider the process of credit approval in banking. Standard rules for checking a customer situation for solvency and credit approval can be rather easily modeled by a financial consultant. This top down model can be complimented by a bottom up model describing the risk of credit failure. This can be identified by data mining customer data and providing a risk based customer segmentation. A combination of the expert rules and the generated predictive model provides the final rules. The process of credit approval can now be automated, its workflow is controlled by a rules engine, and customers can now run credit approval as a self service on a web site, for instance. Other examples can be found in the context of cross/up-selling and customer attrition. Data exploration provides intelligence that can be used to enrich processes. This intelligence is embedded in the process, and works as a black box. Special knowledge how this intelligence works is not necessary when working in the context of intelligent processes. Analytics makes consumable for everybody, not only for some thousands of specialists, but for millions and more information consumers. 3.2 CPM Technical Components Dash-Board It implements a scorecard model (e.g., see Fig. 5). The dashboard visualizes all metrics for each information consumer according to his / her information profile. This includes notification. In case of escalation, events or alerts, important information is sent to the information consumer by special channels, e.g. SMS, instant message, e mail etc. A dashboard should be embedded in a portal framework. The information profile describes which information, functions, knowledge and processes an information consumer (employee, customer, supplier, partner, dealer etc.) must have access to according to his / her role. Based on the information profile, the dashboard is personalized according to the paradigm of the information supply chain: Each information consumer gets exactly what he / she needs to do his / her job. Decision Engine It executes the business rules and controls the workflow of processes. A decision engine should also have scheduling features for follow up of events by intervening actions. For instance, if a customer has visited a web site, given a positive response, but did not come back within a certain amount of time, then the decision engine should be able to detect this non-event, and send a trigger, for example to a call center agent for follow up. BPM White Paper / R.Nußdorfer / Dr. W. Martin 3/31/2005 Page 17

18 Decision engines enable intelligent interactions with all business constituents. For example, they can enable intelligent real-time interactions with customers in the web or call center channel. In cross/up-selling, decision engines execute predictive models reflecting customer behavior. The right customer gets the right offer in right time. This boosts revenues as various business cases have shown. Analytical Application This is the service oriented implementation of metrics and KPMs. Analytical applications are component based and consist of analytical business content (customizable templates of metrics and KPMs), business intelligence components (spreadsheet, report generator, adhoc inquiry, OLAP, data mining, statistics etc), and an application life cycle management framework for implementing, customizing, and maintaining metrics and KPMs (Fig. 4). Analytical applications extend the traditional data warehouse oriented business intelligence model. Indeed, CPM puts business intelligence via metrics into the context of strategy, goals, and processes. Data Integration Traditional business intelligence tools worked on the data warehouse. Analytical Applications work on a data integration platform, because operational in particular requires access to operational and dispositional data simultaneously. A data integration platform links CPM to the integration hub of a real-time IT architecture and the data warehouse. In the SOA model, the data warehouse becomes a backend service (Fig. 4 and 7). The application server supporting the analytical applications runs on top of the data integration platform, where data integration provides data services (ideally as web services) for analyzing data and meta data, develop data models, prepare and profile data and meta data, as well as ETL (extraction, transformation, load) services. Data Exploration Tools Analytical applications are complimented by a data exploration environment. Metrics are not only top down derived from strategy, goals, and processes, but could also be derived bottom up from data. This is the purpose of a data exploration environment. For data exploration, business intelligence tools like data mining, statistical tools, adhoc querying, OLAP tools etc are used on top of the data integration platform. Data exploration tools are used by interdisciplinary teams. A specialist for tools and methods and a power user representing the future information consumers jointly drive the data exploration process. The necessary data services for supplying the tools are provided by an IT specialist. The IT specialist ideally is a data architect who knows well the enterprise data and data sources and who can identify and evaluate external data sources and services for enriching the internal data. To summarize, data exploration is a special task for especially trained experts with specialized tools. Real-Time Analytics Data exploration is a process driven by man. When the amount of data to be analyzed is huge (e.g., in the order of terabytes), then the tools become the bottleneck, not the interdisciplinary team driving the process. Then, real-time analytics could help. Real-time analytics is based on three different principles that can be also combined. Vendors are listed in chapter 8. BPM White Paper / R.Nußdorfer / Dr. W. Martin 3/31/2005 Page 18

19 Special Database Technologies Technologies like compression, indexing, vector processing, memory-based caching etc. can dramatically improve the performance of adhoc queries and other business intelligence components / tools. This speeds up the exploration process by faster responses (from hours to minutes and seconds). Technologies in this category are rather mature. In-Memory Databases This is one of the rather recent developments in database technology. Here, the total database is processed in memory providing even more performance than the specialized data base technologies that still store data physically. In-memory data bases especially benefit from a 64 bit address space. Special algorithms They are used for reading and processing data. They overcome the limitations of traditional SQL and OLAP technologies. Many vendors combine these features with special data base technologies as described above. Example for Dashboards Example: arcplan S.A.R.L. Martin Figure 5: This example shows that the user interface of a dashboard can look very differently. Indicators can be visualized by text, speedometers, tables or icons. This example presents the market analysis of a world wide acting vendor of beverages. Sales results are presented by weather symbols. Clicking on the weather symbols opens analytical scenarios for understanding and interpreting sales success, making decisions and launching actions. BPM White Paper / R.Nußdorfer / Dr. W. Martin 3/31/2005 Page 19

20 4 Information Democracy Business Intelligence was a tools based approach. For users, it was mandatory to know how to use report generators, adhoc querying, OLAP tools, spreadsheets, statistical and data mining tools, etc. This know how was typically acquired by training and education. Business analysts and power users evolved as a new class of people that was empowered by all these types of tools. Business departments became dependent on this new type of information empowered employees. So, information became a kind of luxury product that was not available to everybody. This is now changing in the CPM model. Analytics for performance management and analytics for enriching operational processes requires that everybody participating in a business process must be in a position to consume information without training and education. This is enabled by the CPM methods and technologies as described in the previous chapter. But, we will continue to engage business analysts and power users. Their future role will remain data exploration. Less engagement in providing standard information upon request, they can spend more time for data exploration and create more value for the enterprise. Plus, a new task is attached to them: management of the CPM methods and technologies. Identifying and communicating best practices of analytical scenarios will be their charter. This requires a close cooperation and collaboration with the information consumers. If an information consumer will be confronted with a new, not yet encountered problem in monitoring and controlling his/her business processes, a new analytical scenario has to be jointly developed with a business analyst. Once solved, the new scenario can be reused within the CPM framework. The CPM organization learns and gets better the longer they apply the CPM model. Even if CPM provides an intuitive working environment that needs much less training and education, there is still the problem of data deluge to be solved. Who needs what information, where, when and why? The solution comes with the information supply chain model linking processes, metrics, people and organization: Process-orientation comes with a process ownership model. The process ownership model describes who of the constituents (employees, partners, suppliers, customers etc.) participates in and is responsible for what processes or activities within the processes. In metrics-driven management, this process ownership models also includes the metrics that are necessary to monitor and control the process and its performance. This can be understood as information sharing and filtering. The constituents share data, information and knowledge in the context of their process-oriented communication and collaboration. All other data is filtered out. Information sharing and filtering is done via information profiles describing the context of collaboration based on the process ownership model. This is called information democracy (Fig. 6): The process ownership model includes the information profile describing and filtering exactly the information that is needed by all constituents based on the context of collaboration. BPM White Paper / R.Nußdorfer / Dr. W. Martin 3/31/2005 Page 20

21 Information Sharing and Filtering Technology Process Process Ownership Ownership Processes People Information Democracy Information Information Profiles Profiles Metrics Culture Goals & Objectives Business Strategy Information Democracy Balancing Information Hiding and Information Deluge S.A.R.L. Martin Figure 6: Information democracy comes with the information supply chain paradigm. Everybody has access to all information necessary to manage the processes and their performance specified by the process ownership model not less and not more. Visualization of metrics according to the principles of information democracy is done via dashboards (see Fig. 5). A dashboard visualizes a structured set of metrics associated with the functional role of a constituent within the process ownership model. Furthermore, the dashboard arranges the visualization of metrics according to the granularity and importance of information. So-called key performance indicators are always presented whereas more detailed indicators can be invoked by clicking through the dashboard s structure. Trend is to make dashboards active: only alerts that need decisions and human interactions will be presented and pushed to decision makers via message based publish and subscribe communication methods. From a technical point of view, dashboards should be embedded into portal technologies. Portals have evolved from intranet and extranet solutions to the central point of control for collaboration providing the P2S (person to system) interface. A portal is defined as a system that enables sharing and filtering of data / information, functions / functionality, content / knowledge, and processes. This sharing and filtering is related to the functional role of a collaborative team within the process ownership model. A collaborative team is a group of people representing the various constituents that work together according to the collaborative goals and objectives of the team. In this way, portals support cross-functional, crossdepartmental, and cross-enterprise virtual teams. As a special case, a team could also be an individual portal user. To summarize, portals enable information democracy. BPM White Paper / R.Nußdorfer / Dr. W. Martin 3/31/2005 Page 21

22 A portal can be understood as an abstraction layer linking and aggregating contents and services as well as reducing the complexity of their access. In this sense, the team-context defines the collaboration bandwidth, i.e. which data / information, functions / functionality, contents / knowledge, and processes are exposed to the collaborative team together with the appropriate collaborative tools. Each portal user gets a personalized environment that can be further individualized. Indeed, such a person portal can be understood as an integration technology. But the ultimate integration is done via a human interaction, i.e. within the teamcontext; a user can execute a message transfer between contents and services within his context. Furthermore, portals also provide synchronous and asynchronous collaborative tools, e.g., e- mail, co-browsing, chat, instant messaging, web-conferencing etc. We have described the role of portals and their relationship with business process management elsewhere in Martin and Nußdorfer (2004). BPM White Paper / R.Nußdorfer / Dr. W. Martin 3/31/2005 Page 22

23 5 Corporate Performance Management the Reference Architecture Corporate Performance Management is implemented by analytical application frameworks (Fig. 7) corresponding to application frameworks for business operations (see fig. 1 and layer 5 in fig. 1 in Nußdorfer, Martin, 2003). Indeed, analytical applications running on analytical application frameworks are component based and service oriented. The analytical application framework will typically reside on a standard J2EE and/or.net application server. Analytical applications and operational (OLTP-) applications should run in the same application framework, i.e. a SOA (service oriented architecture). This saves IT-costs and resources, and enables a tight communication between analytical and operational services which is a must in certain time critical situations within operational corporate performance management. CPM Technical Architecture Analysis Human Process Deployment Layer Analytical Workflow Presentation Layer Meta Data Analytical Application Server Data Integration Platform Data Warehouse ETL Processes EAI Hub S.A.R.L. Martin Figure 7: Technical Architecture for operational, tactical, and strategic corporate performance management. The heart is an analytical application server that is to be understood as a logical application server. Physical implementation is a SOA (service oriented architecture). The ETL processes are part of the data integration platform. This is the reference architecture for an analytical application framework for developing, executing, and managing analytical applications as analytical services. BPM White Paper / R.Nußdorfer / Dr. W. Martin 3/31/2005 Page 23

24 Analytical application server This is the platform for the analytical applications discussed in Fig. 4 and 7. Traditional business intelligence tools that in the past could only be managed by power users and business analysts turn into embedded components of analytical applications providing transparently their analytical services. This drives another key success factor for information democracy as we have seen: no special training is required for consuming analytics. Furthermore, an analytical application framework includes a data exploration environment enabling power users and business analysts to explore the data in a bottom up way for identifying and deriving new structures and potentially new metrics from the data. (Martin, 2003-A) Data Integration Platform It has been common practice to put an analytical application server on a data warehouse infrastructure, where the data warehouse is supplied by ETL (extraction, transformation, load) processes. ETL processes are either supported by batch and / or message / queuing, depending on whether time is critical for data supply. This is no more sufficient for operational CPM (BAM or PPM). Recent approaches use alternative concepts by embedding analytical components in operational systems and accessing their operational data bases directly. This avoids the redundant intermediary storage of data in the data warehouse, and this saves time so that certain metrics can be used for online monitoring and controlling of time critical operational processes. But, the disadvantage of these approaches is that specification of metrics is restricted by the process data. This problem can be solved when the analytical application server sits on a data integration platform (Fig. 7) enabling the simultaneous access of data warehouse and operational data via an EAI (enterprise application integration) hub. The EAI hub integrates OLTP systems on the application level. 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 rules needed for calculating more complex metrics may be hidden in the application logic and not available on the data level. The question of time critical operational CPM will be expanded in the next chapter. Meta Data An analytical application framework should be meta data driven. The meta data layer spans across all layers of the analytical application framework. 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 and 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 ( master data, 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 sinks of data, cross references, time stamps) BPM White Paper / R.Nußdorfer / Dr. W. Martin 3/31/2005 Page 24

25 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 constituent 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. 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 OMG (object management group). Despite the slow progress, there is no alternative to this standard. Meta data is 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. But it is absolutely insufficient just to update meta data and store the most recent and actual version in the repository. For enterprise planning and for any comparisons between past, now, and future, the availability of the total life cycle of all meta data is a must. This is why meta data management is to be based on life cycle management. The repository must include the life cycle of all meta data. Today, this is a weak point, sometimes even a gap in vendor offerings and enterprise architectures. Presentation Layer It provides all services for visualizing the metrics. These services should be independent of the deployed peripherals. Graphics, tables, spreadsheets, geographical and time series presentations must be automatically adapted to the bandwidth and display capacities of the peripherals. Key peripheral types to be supported are web and mobile wireless (e.g., PDAs personal digital assistant, smart phones etc). Analytical Workflow It provides the analytical scenarios based on best practices for analyzing and understanding complex situations. Typically, such scenarios are jointly developed by information consumers and business analysts. In case of a new situation not yet supported by a business scenario, the analytical workflow is extended by a joint incremental development step. This joint development model for best practices is a key part of the information democracy model. Business analysts and power user s roles are updated. Instead of delivering first line support by creating reports and running adhoc inquiries on demand of information consumers, they now provide second line support for data exploration and the development of analytical scenarios. BPM White Paper / R.Nußdorfer / Dr. W. Martin 3/31/2005 Page 25

26 Deployment Layer It should be embedded as a portlet in a more general portal framework. The portlet acts as a dashboard (Fig. 5). According to the process ownership model, all relevant metrics are arranged and presented to the information consumer. Deployment is either passive, i.e. the information consumer uses search and navigation tools to access its metrics and is guided by an analytical workflow, or active, i.e. only exceptions and alerts are passed by appropriate channels (e.g., SMS or e mails) to the information consumers triggering decisions and actions. Alternatively, in time critical situations, when human decision making takes too long time, alerts and alarms drive decision engines. Then, decision taking and launching of actions can be automated. Finally, certain instantiations of metrics could provide input for updating certain compound metrics, e.g., customer segmentation per data mining could lead to update scores in the customer data: analytics feed more analysis. BPM White Paper / R.Nußdorfer / Dr. W. Martin 3/31/2005 Page 26

27 6 Latency matters Today, CPM must address the operational, tactical, and strategic aspects in a seamless way. Leading process-oriented businesses use highly automated processes for straight through processing. Metrics trigger decision engines and actions are taken in an automated way. Just in case of exceptions, escalation management, authorization, entry of triggers (self-service), and when applying collaborative services human interactions are (still) needed. Now, when the identification of alerts and exceptions becomes time critical, human interactions even become to slow. This is where latency matters and action time becomes critical (Fig. 6). The action time model shows three critical phases, data latency, analysis latency, and decision latency. Real-Time and Action-Time Value Event Real-Time Data Integration Data Latency BAM Data Stored Analysis Latency Information Stored Decision Engines Decision Latency Action Taken Action Time Time 8 After: Richard Hackathorn and Colin White 2005 S.A.R.L. Martin Figure 8: In operational CPM (BAM), time may be critical. The action time model decomposes action time into data latency, analysis latency, and decision latency, and it shows by which approaches, action time can be minimized. Data Latency. This is addressed by real-time data integration (Fig. 9). There are two options, low latency and 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. BPM White Paper / R.Nußdorfer / Dr. W. Martin 3/31/2005 Page 27

28 The low latency model is based on a data integration platform that collects all relevant transactional data and operational data and stores it in a so-called low-latency data mart (LLDM). This requires integration of the data integration platform with the EAI platform where the transactions across all enterprise systems are managed. The LLDM is refreshed either by message queuing or by batch, where the batch is executed in short periodicities 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 EAI platform: The data integration platform is an operational system. Real-Time Data Integration Operational Data OLTP System Meta Data EAI Platform Operational CPM (BAM/PPM) Data Integration Platform Events Real-Time Data Propagation LLDM Data Warehouse ODS Tactical & strategic (traditional) CPM OLAP Datamarts S.A.R.L. Martin Figure 9: Real-time data integration bridges traditional data warehouse architectures and operations. EAI = enterprise application integration, 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. 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 sink with the state of transactions. This stresses the need for low latency data marts, especially in the case of legacy systems. BPM White Paper / R.Nußdorfer / Dr. W. Martin 3/31/2005 Page 28

29 The zero latency model is based on a logical data base access layer spanning across all operational data bases and the data warehouse. For an application, this layer looks and behaves like a data base, all SQL operations update included can be executed across the distributed data. This is another alternative to an ODS. No data is redundantly moved into a special store which saves time. But as with the ODS model, all logic that is included in the transactions, but not stored in the distributed data bases is lost. Analysis Latency. This is addressed by BAM solutions: Analytics must be available in real time. If the BAM metrics are nothing but classical performance indicators that are calculated by simple mathematical rules, analysis latency is no issue due to the performance of today s computers. But if metrics become more complex (e.g., monitoring and controlling of traffic in all kinds of networks in telecommunications, information processing, air traffic, ground traffic etc.), analysis latency is an issue, and special fast algorithms have been developed (e.g., matching algorithms). This is still a young area of development and many solutions are in an experimental stage. Analysis latency is also an issue when using predictive models. In most situations, the predictive model cannot be derived in real time data mining does not work in real time. This is the reason why modeling of predictive models by data mining processes was strictly separated from applying predictive models in operational processes. So, usage of a predictive model is real-time, but not modeling. The predictive model was exploited in an offline mode with the hope that the based on the past model maps the actual and future. An approach to overcome this problem was the periodically remodeling of the model based on the speed of supposed changes (e.g., week, month). New approaches and technologies make a break through. Predictive models can be made self-learning by adaptive algorithms. They match dynamically to the changing process context. Such an adaptive predictive model is always on-line and maps to the presence based on the actual data driving the adaptive algorithm. Decision Latency. Indeed, when time matters, decisions cannot be taken anymore by humans, but this process must be automated by decision engines. Decision engines are based on rule engines. Rules are either generated bottom-up via predictive models. Predictive models are the outcome of data mining processes, so the decision rules have been identified from detected data structures and patterns. Rules may be also specified by experts in a top-down approach. This is a certain revival of the old expert systems popular in the late 80s and early 90s. Ultimately, rules engines can be modeled by a combination of predictive models with expert rules. Decision engines have been discussed in detail in Martin (2003-B). BPM White Paper / R.Nußdorfer / Dr. W. Martin 3/31/2005 Page 29

30 7 CPM and classical BI: fundamental differences CPM has evolved from the old decision support and business intelligence approaches, but today, CPM is a completely different model then classical business intelligence. CPM is a top-down model that begins with business strategy. Business Process Management links process analysis and design with cross-functional and crossdepartmental process flows and CPM. Process performance metrics are created at the same time as the processes. o Business Intelligence (BI) was bottom-up and not process-oriented. CPM is based on an information supply chain model that permanently synchronizes the provision of information with the need for it. o Business Intelligence was solely an information-providing model (Bill Inmon "Information Factory"). CPM is a closed-loop model that controls and monitors business processes at operational, tactical and strategic levels. o Business Intelligence only supported decision making, but not action taking. The operational aspects of Business Intelligence were not covered by a coherent approach. CPM metrics are forward-looking. Predictive models enable the identification of problems before they appear. Of course all traditional retrospective metrics remain useful. o Business Intelligence was retrospective (based on the past). The focus was on analysis and diagnosis. CPM enables information democracy by means of the sharing and filtering of information in accordance with the process ownership model. o Business Intelligence tools did not provide the information consumer with sufficient information. Either one had information that was not accessible (on occasion even hidden or held back), or you had an absolute flood of data ("information for the masses"). CPM is based on an analytical applications infrastructure, which runs on open, standardized application servers. o Business Intelligence was a tool-related approach, based on proprietary technologies. BPM White Paper / R.Nußdorfer / Dr. W. Martin 3/31/2005 Page 30

31 8 Players in the CPM Market Taxonomy CPM/BAM Action Decision Engines CPM CPM BAM BAM LLDM MQ Data Integration Platform Cisco EAI Agents other ETL Data Warehouse S.A.R.L. Martin Figure 10: Action time (Fig. 8) based taxonomy of CPM market players. (BAM = business activity monitoring, ETL = extraction, transformation, load; LLDM = low latency data mart, EAI = enterprise application integration, MQ = message / queuing) From the three phases of action time (Fig. 8), we can derive taxonomy for classifying the players (vendors) in the market (Fig. 10). Key players in the different categories are listed below. More details on specific vendors will be published in part 2 of this white paper, where in each paper we will map the vendors architecture and strategy to the vision and reference architecture developed in this part 1. Data Integration Platforms Ascential, BEA, Business Objects (Data Integrator), Data Mirror, IBM (Information Integrator for DB2), Informatica, Information Builders, ISoft, Oracle, SAS, Tibco etc Data Warehouse (A classical BI Tools) Actuate, Applix, arcplan, Board M.I.T., Business Objects, Computer Associates/Cleverpath, Cognos, Group 1, Hummingbird, Hyperion, IBM, Informatica, Information Builders, Microsoft, MicroStrategy, MIS AG, Oracle, Panorama, ProClarity, SAP, SAS etc BPM White Paper / R.Nußdorfer / Dr. W. Martin 3/31/2005 Page 31

32 Data Warehouse (B - BI Tools and special data base technologies for real-time analytics) Aleri, Alterian, Aruna, EPOQ, IBM / Redbrick, InterSystems, Jedox, KxSystems, NCR / Teradata, Netezza, Panorama, Panoratio, QlikTech, Sand Technology, smartfocus, Sybase Adaptive Server IQ Multiplex, Times Ten, WhiteCross Systems, Xcelerix ETL Ascential, Business Objects (Data Integrator), Data Junction, Data Mirror, ETi, Group 1, Hummingbird, IBM (DB2 Warehouse Manager), Informatica, Information Builders, ISoft, Microsoft, Oracle, Pervasive, SAP, SAS, Sunopsis etc Business Activity Management/Process Performance Management (BAM/PPM) Axway, EPOQ, IBM, IDS Scheer, Informatica, Intellicorp, Meta Software, Microsoft, Oracle, SeeBeyond, Tibco, Vitria, WebMethods, WebTrends etc (also: all vendors in category operational, tactical, and strategic CPM ) Predictive Models Angoss, Chordiant, Cognos, E.piphany, EPOQ, Eudaptics, Fair Isaac, IBM, ISoft, Kana, KXEN, Magnify, Megaputer, Microsoft, Oracle, Prudsys, Quadstone, SAP, SAS, Siebel, SPSS, thinkanalytics, Unica etc Decision Engines ATG, BEA, Chordiant, E.piphany, EPOQ, Eudaptics, Fair Isaac, IBM, ILog, i2, Kana, MicroStrategy, Oracle, Prudsys, SAP, SAS, Siebel, SPSS, thinkanalytics, Tibco etc Operational, tactical, and strategic CPM Arcplan, Board M.I.T., Business Objects, Computer Associates / Cleverpath, EPOQ, IBM, Information Builders, Oracle, QlikTech, SAS etc Financial Performance Management Acorn System, arcplan, Armstrong Laing Group, Board M.I.T., Cartesis, Cognos / Adaytum / Frango, Geac, Hyperion, Longview, Microsoft / FRx, MIS AG, OutlookSoft, SAS, SAP, SRC Software etc BPM White Paper / R.Nußdorfer / Dr. W. Martin 3/31/2005 Page 32

33 9 Summary We believe that our vision on corporate performance management across operations, tactics, and strategies will become a standard for continuously evolving metrics-driven management. We also believe that our reference architecture of analytical application frameworks will be a standard for dynamic enterprise specific real time IT-architectures. This Whitepaper will help to make strategic decisions on strategies and platforms. CPM is the answer to today s challenges running a business: You can only manage what you can measure. This is one of the leitmotivs that will lead enterprises into a successful future. Munich, March 2005 Annecy, March Addresses: [email protected] [email protected] Literature: Martin, W.: Business Performance Management und Real-Time Enterprise auf dem Weg zur Information Democracy, Strategic Bulletin, IT Research, Sauerlach bei München, 2003-A, 32 Seiten Martin, W.: CRM 2004 Kundenbeziehungsmanagement im Echtzeitunternehmen, Strategic Bulletin, IT Research, Sauerlach bei München, 2003-B, 32 Seiten Martin, W.: BI 2004 Business Intelligence trifft Business Integration, Strategic Bulletin, IT Research, Sauerlach bei München, 2004, 32 Seiten Martin, W., Nußdorfer, R.: PM Portale Kollaborations- und Präsentationsdienste, Kompendium Status und Trend Prozesse und Menschen, ibond White Paper Vol. 4, München, 2004, 33 Seiten Nußdorfer, R.: EAI-Buch Nutzen und Architektur des EAI-Konzeptes, Marktüberblick, Eigenverlag als e-book: , 200 Seiten Nußdorfer, R., Martin, W.: RTE Real-Time orientierte IT Architektur, Kompendium Das große Ganze IT Architekturen strategisch geplant, ibond White Paper Vol. 1, , 35 Seiten Nußdorfer, R., Martin, W.: iso integrated solutions - Geschäftsprozesse, Kompendium Das große Ganze End-to-End-Lösungen auf EAI Basis, ibond White Paper Vol. 3, , 41 Seiten BPM White Paper / R.Nußdorfer / Dr. W. Martin 3/31/2005 Page 33

34 10 The Sponsors arcplan Information Services AG arcplan is a globally successful software developer whose business intelligence (BI) solutions seamlessly link businesses with their data to provide consistent access to crucial information across the enterprise. The company was founded in 1993 in Germany. Headquartered in Düsseldorf, Germany and Philadelphia, USA, arcplan delivers its solutions through a global direct sales force and a network of more than 130 service and implementation partners in over 30 countries. arcplan's dynasight analytic development platform marries the benefits of custom-made solutions with the rapid availability of packaged applications. Existing systems and disparate data sources are immediately accessible, comprehensive analysis and different views and applications are quickly developed and deployed. Business and IT professionals design intuitive and end-user oriented applications without programming based on a unique drag & drop principle. Real-time access and analysis to a variety of data sources leverage the investment of already available systems and applications. The flexible environment enables businesses to enrich information with relevant context in order to align operations and processes with corporate goals and objectives. Founded in 1993, arcplan supports more than 1,800 customers e.g. Bayer, DaimlerChrysler, Citigroup, Deutsche Bank und Siemens - and 230,000 users worldwide. For more information, visit Ascential Software Inc. Ascential Software Corporation is the leading provider of enterprise data integration solutions to organizations worldwide. Customers use the Ascential Enterprise Integration Suite to integrate and leverage data across all transactional, operational and analytical applications with confidence in the accuracy, completeness and timeliness of critical information. Ascential Software's powerful data profiling, data quality, data transformation, parallel processing, meta data and connectivity solutions enable customers to reduce total cost of ownership and increase return on IT investment. Headquartered in Westboro, Mass., Ascential Software has offices worldwide and supports more than 3,000 customers in such industries as financial services, telecommunications, healthcare, life sciences, manufacturing, consumer goods, retail and government. More information on Ascential Software can be found on the Web at BPM White Paper / R.Nußdorfer / Dr. W. Martin 3/31/2005 Page 34

35 Board M.I.T. Founded in 1995 and headquartered in Lugano, Switzerland, Orenburg is the developer of Board M.I.T. (Management Intelligence Toolkit), the only programming-free software toolkit for fast, flexible and affordable development of custom analysis and analytic applications. With offices in the United States, United Kingdom and Germany, Board M.I.T. is distributed worldwide through Orenburg's network of Board M.I.T. Solution Partners. With over 1,400 customers across the globe, including enterprises like Chupa Chups, GlaxoSmithKline, Johnson & Johnson, Subaru, Salomon and Novartis, companies of all size and industry utilize Orenburg's unique software toolkit approach to improve the analysis of their information and effectiveness of their management decision-making. For more information: EPOQ GmbH The EPOQ Company was founded in February 2003 in Karlsruhe, Germany. EPOQ has developed the revolutionary "Realtime Dynamic Solutions" method for creating predictive models and operating them dynamically. Primary focus is on customer analysis and decision support for real time dynamic scoring. "Realtime Dynamic Solutions provides forecasts in real time that self-optimize with every customer contact. In terms of informational value they are way ahead of all other conventional statistical analyses. With the automated and selflearning Realtime Dynamic Solutions by EPOQ, desired forecasts on various aspects of customers can be created, e.g. churn danger, customer value rating or expected sales. This novel technology enables forecasting systems to act also as early warning systems that can expose changes in customer behaviour with an extremely fast reaction time. Unknown events can therefore be discovered and identified. EPOQ solutions are significant in eventsoriented architectures, to drive processes in a dynamic events-oriented manner. EPOQ solutions can also be used in dynamic cross and up-selling forecasts for processes with very short reaction times, as in telephone marketing or e-commerce. One reference customer which already uses EPOQ Dynamic Scoring successfully for optimizing its telephone marketing drives is the German mail order and chain store giant QUELLE AG. Informatica Corp. Informatica (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 2,100 companies worldwide rely on Informatica for their end-to-end enterprise data integration needs. Informatica products are designed to help customers simplify their IT infrastructure by providing a single platform for all enterprise data integration initiatives. Informatica products BPM White Paper / R.Nußdorfer / Dr. W. Martin 3/31/2005 Page 35

36 empower the business user with holistic information, reduce the cost and complexity of enterprise IT infrastructure for the IT manager, and provide increased productivity to IT practitioners which improves their responsiveness to the business. These capabilities are delivered through a service-oriented architecture to enable the IT architect to maximize existing and future flexibility. With Informatica PowerCenter enterprises are offered comprehensive information, a scalable solution, and lifecycle productivity. PowerCenter offers flexibility, scalability, and availability required for complex integration initiatives. Organizations realize successful enterprise data integration strategies with its single platform that provides all the traditional capabilities for accessing and delivering accurate and complete data. Informatica PowerExchange is a proven, patented software that provides access to all critical enterprise data systems including mainframe, midrange relational databases, and file-based systems as a standalone solution or as a tightly integrated product with Informatica PowerCenter. PowerExchange eliminates the need for organizations to manually code data extraction programs and ensures that mission-critical operational data can be leveraged across the enterprise. In Europe Informatica has many leading enterprises across different industries amongst its customer base, including companies such as abbey, AUDI AG, DaimlerChrysler, Deutsche Börse, The Dutch Ministry of Defense, GlaxoSmithKline, ING Direct UK, La Poste, Mexx, Nestlé and Prudential. For more information on Informatica please visit in-factory The Enterprise Information Integration company. For more information please visit ISoft For more than 12 years, ISoft has been offering tools and solutions to manage and analyze information. ISoft products help organizations to maximize the value of their data. ISoft provides a complete range of Business Intelligence tools and services from data extraction and modeling to analysis using Data Morphing and Data Mining techniques. AMADEA is a comprehensive and Real Time data integration platform based on the innovative Data Morphing technology. Without programming, users have instant and permanent access to all data managed during IT projects. From data extraction, auditing and transformation to reporting and web-based piloting, AMADEA covers Business Intelligence needs for flexibility, performance and interactivity. ALICE d'isoft is a complete and interactive Data Mining solution based on decision tree and dedicated to business users. For more information: BPM White Paper / R.Nußdorfer / Dr. W. Martin 3/31/2005 Page 36

37 Panorama Software Extending the Reach of Business Intelligence Panorama Software helps global organizations unlock the hidden value of their information assets to improve business performance and results. Panorama extends the Microsoft Platform through integrated business intelligence and corporate performance management solutions. With Panorama decision makers at all levels and functions can easily analyze data, quickly create and distribute reports, and proactively measure performance. Companies gain a greater understanding of their business and make better decisions. These informed decisions improve profitability, increase revenues, reduce costs and time to market and mitigate competitive risks. Panorama, a leading innovator of business intelligence solutions, supports customers worldwide in industries such as financial services, manufacturing, healthcare, retail, healthcare, telecommunications and life sciences. Panorama has more than 100 partners in 30 countries, and maintains offices throughout North America and EMEA. More information is available at Olik Tech QlikTech is dedicated to simplifying business analysis. QlikTech s game-changing QlikView application is dramatically easier to deploy and use, inspiring fervent user adoption by business professionals who require it to run their organizations. QlikTech s patented technology allows instant, in memory, manipulation of massive datasets on low cost hardware, allowing affordable widespread deployment of highly sophisticated analytic applications in days. QlikView s click driven, visually interactive interface allows users to discover unanticipated insights hidden in operational systems with virtually no end-user training. QlikView has over 90,000 users at 1,500 customers in over 40 countries. In addition to hundreds of small and midsized companies, QlikTech s customers include large corporations such as AstraZeneca, Pfizer, Top Flite, London Fog, 3M, and The Campbell Soup Company. QlikTech is privately held and venture backed by Accel Partners, Jerusalem Venture Partners, and Industrifonden. Founded in Sweden, QlikTech has corporate headquarters in the United States, and subsidiaries in the United Kingdom, Germany, and Scandinavia, and over 100 partners across the world. BPM White Paper / R.Nußdorfer / Dr. W. Martin 3/31/2005 Page 37