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The Next Generation of Balanced Scorecards   Article published in DM Review Magazine December 2001 Issue     By Michael J. Schroeck During the past 12 months, the balanced scorecard has re-emerged in many industries. The primary reason is the "executive suite" (CEO, COO, CFO, CIO, etc.) has come to realize that successfully leading a global business today requires managing and fully leveraging information across the enterprise. It is no longer enough to manage discrete functions separately and hope the results of each will aggregate to meet corporate objectives. At the same time, traditional functional silos are collapsing as technologies such as the Internet are making the boundaries between customers, suppliers, manufacturers and retailers more transparent and dynamic. Businesses are broadly internalizing the concept of designing operations from a "top-down," goal- oriented process perspective rather than the traditional "bottom-up" functional approach. Recognizing that the balanced scorecard concept has been around for several years and that some companies have implemented balanced scorecard applications with varying degrees of success, what is different about this next generation of balanced scorecard solutions? The recognition that, in order to be effective, the measures and related accountability associated with balanced scorecards must cascade from the executive level throughout the organization. This suggests that every individual's performance is aligned with one or more of these measures. Past balanced scorecard efforts oftentimes consisted of metrics that were aggregated, usually using spreadsheets on standalone balanced scorecard applications that were not integrated with either the processes or the information being used to measure and manage the underlying business units and functions. As a result, these balanced scorecard implementations lacked the critical alignment, shared responsibility, and "cause and effect" relationships that are absolutely vital. The second fundamental difference is that companies are taking a more pragmatic approach toward implementing balanced scorecard solutions. They have recognized that it may not be necessary to implement every single balanced scorecard concept to get value from their efforts. Previous balanced scorecard applications have failed or been abandoned because companies attempted to implement a conceptually perfect solution only to find that they didn't currently capture or store certain balanced scorecard measures (e.g., qualitative information). Companies today are taking more of an iterative approach, starting with those measures supported by information that they do have which is usually stored in a data warehouse. At the same time, they are beginning to put in place processes to capture the additional measures that will be included over time. Companies are therefore combining two important business concepts: the balanced scorecard and systems thinking. Both recognize and address a fundamental fact: What gets measured gets done. Today, executives continue to be frustrated by the fact that their measurement systems are no longer aligned with their business strategies. During the past several years, for example, most companies have adjusted their strategies toward becoming much more customer-focused. Yet most measurement and reporting systems continue to present results by function and/or product. Measuring the wrong things can distract the business from its strategic plans and make it difficult for the organization to develop an understanding of how specific actions (such as increasing the number of call center employees) affect specific results (such as customer satisfaction). The Balanced Scorecard Popularized by Harvard professors Robert Kaplan and David Norton, the balanced scorecard concept has been widely implemented by corporations around the world. The balanced scorecard is a focused set of performance measures derived directly from the business strategy of an organization. By moving away from the idea that every company in an industry should have the same measures, an organization can focus on the execution of its specific strategy, such as being the low-cost producer or having superior customer service. To accomplish this, these strategic objectives are translated into performance measures categorized into four dimensions: Innovation, Process, Customer and Financial. These dimensions are designed to align with strategy, reflect performance across the entire enterprise, and represent cause-and-effect relationships across each dimension. Systems Thinking The concept of systems thinking was originally developed by Jay Forrester and popularized by Peter Senge's book, The Fifth Discipline. It is a way to understand different perspectives regarding how a system works and to capture that understanding using cause-and-effect, or influence, diagrams. A system is a collection of parts that interact with each other to function as a whole. Our body's organs and skeleton, for instance, compose a system. To be effective, a doctor must understand how to treat a human body as a system rather than as a collection of distinct parts. Similarly, a manager must view a business as an integrated system rather than as several unrelated processes. A main principle of systems thinking is that the structure affects behavior. Systems thinking allows you to: Understand interrelationships. Understand long-term impacts. Gain a greater understanding of the system in which you operate and the implications of the actions you take. The Cause-and-Effect Diagram Within systems thinking, everything is expressed in terms of variables and relationships. A relationship represents how one variable influences another. Only two types of relationships exist: A positive, or same direction, relationship. The easier it is to issue a complaint, for example, the higher the number of complaints you will receive, all other things being equal. This relationship is demonstrated in Figure 1, where the S above the arrow designates it a same direction relationship. A negative, or opposite, relationship. As customer satisfaction increases, for instance, the number of complaints decreases, all other things being equal. This type of relationship is also shown in Figure 1, where the O above the arrow designates it an opposite relationship. Figure 1: Types of Relationships These relationships can be connected in a cause- and-effect diagram as shown in Figure 2. Figure 2: Cause-and- Effect Diagram - Number of Customer Complaints and Custmoer Satisfaction The Balanced Scorecard and Systems Thinking Balanced scorecards have always had an element of systems thinking, specifically as related to cause and effect. Early system implementers tended to design performance measures across the four balanced scorecard dimensions: Innovation: How well the organization develops its people and processes. Processes: The effectiveness and efficiency of business processes. Customer: How well product and service attributes satisfy external customers. Financial: The ultimate financial results. In these early implementations, it was often assumed that performance in the first dimension, Innovation, affected the performance of the second dimension, Processes. The performance of Processes was then assumed to directly affect customer satisfaction. Ultimately, customer satisfaction was seen as the driver of sales, margins and, thus, financial performance (see Figure 3). Figure 3: Interrelationship of the Performance Measurements Set at a High Level While simplistic, this sequential flow represents the interrelationship of the performance measurements set at a high level. More recently, balanced scorecards have been implemented around strategy maps, which can be used to develop a more specific, detailed view of how strategy elements and their associated measures link through the four dimensions. Today, the more advanced companies in this area are trying to integrate full cause-and-effect models into their balanced scorecards. These systems-thinking- enabled balanced scorecards are especially useful for: Defining and then executing the corporate strategy. By identifying the key drivers of success, executing the strategy and then measuring those drivers, it is possible to develop a strategy that is more robust and execute it more effectively. Communicating effectively. By integrating, analyzing and communicating the right information throughout the organization, employees will be able to make the right business decisions consistent with the organization's strategic goals. Quickly identifying the root causes of potential problems and responding proactively. Having a cause-and-effect model of how the performance measures relate to each other provides a list of "likely suspects" to quickly focus diagnostic efforts and action. Alerting decision-makers about early indicators of trouble. A cause- and-effect model allows organizations to trace the likely downstream effects of performance issues. Such a model, for instance, could show how a sudden increase in call-center staff turnover could affect call answer time, which could, in turn, affect customer satisfaction. Identifying these issues early allows management more time to devise positive plans for mitigating the issues. Incorporating these concepts, today's balanced scorecard model can be illustrated as shown in Figure 4. Figure 4: Today's Balanced Scorecard Model Integrated Analytics - Linking the Data Warehouse to the Balanced Scorecard Developing a performance measurement set that links to your business strategy and reflects the causal relationships within the business provides a powerful basis for linking a data warehouse to important business requirements. This linkage ensures the data warehouse is an effective and valuable tool for supporting business decisions and actions throughout the enterprise. The balanced scorecard defines the critical measures needed to monitor the company's execution of its strategy, helping to identify key analytics within the business. A data warehouse structure should be designed to support these analytics and their associated processes. This concept is an important component of integrated analytics, or iAnalytics. iAnalytics is the integration of information from across a company's complete value chain, the front office to the back office, including: customer relationship management, human resources, financial management, supply chain, e-business and enterprise resource planning. This information is further enhanced with advanced analytics that allow businesses to react quickly to a rapidly changing business environment. Combining the balanced scorecard with iAnalytics offers the following benefits: Links the company's strategy with performance measures and cascades these measures and analyses throughout the enterprise. Delivers role-based business intelligence to the information consumer in a timely and personalized manner. Integrates a company's information assets across the value chain, including customers, partners and suppliers. Combines advanced technologies and analytics with key processes to positively affect individual behavior. Thus, the information that individuals receive is both relevant and actionable. The Enterprise System Architecture Combining integrated analytics with a system-thinking-enabled balanced scorecard can be a powerful way to make certain the right analytics are measured, monitored and acted upon. Equally important is ensuring that the data used to drive the calculation of those analytics is: The right data: consistent, accurate and useful. Captured and presented at the right time. Sourced from the most appropriate systems. An enterprise information architecture must be in place to successfully implement a balanced scorecard. This architecture needs to include: A meta data-driven design that links each layer of the enterprise architecture. Just as we have shifted away from the idea that every company in an industry should have the same measures, we need to recognize that the scorecard will be modified due to changes in strategy or as understanding of cause-and-effect relationships evolves. This meta data must be flexible, easily modified and enhanced so it can evolve with the needs of the business while preserving the overall architecture. A data warehouse environment that captures, manages and summarizes the source data as well as supports the analytics that are reported in the balanced scorecard. Because of the integrated nature of the balanced scorecard, the data warehouse stands as the centerpiece of the enterprise system architecture. By taking an open and modular approach (e.g., via XML), the data warehouse and scorecard systems can evolve and grow as the needs placed upon them change. Balanced scorecard users must also be able to drill down through the operational systems and data sources to explore the variances and questions surfaced by the analytics in more detail. Consider, for example, a "complaint backlog" analytic. These scorecards should have a link defined through meta data that allows the user to identify and access the source reporting systems that can be used to query the current backlog. It should not be the responsibility of the user to know where to search for this information. Let's look at one last example to illustrate these concepts. Consider the relationships shown in Figure 2 and assume an earlier version of the balanced scorecard did not include any reference to the complaint backlog. The managers have realized this is an important intermediate issue between "number of complaints" and "time to deal with complaint." They wish to add this measure of complaint backlog because it will be an early warning of a potential issue that may affect customer satisfaction. It is reasonable to assume that data exists somewhere for complaint backlog. The key issue is how difficult it will be to make this enhancement in order to add this information. At a high level, the process should be: To establish a mechanism to identify the source and then capture the data for the metric as part of the data warehouse and meta data design. To query the data warehouse to report these values in a specific format such as XML. To update the scorecard structure to reflect the new metric. Ideally, the balanced scorecard administrator simply updates a diagram similar to that shown in Figure 2 to include the complaint backlog with the details of the relationships of this new metric and how it impacts others fully defined in the scorecard environment. The intersection between the data warehouse and the balanced scorecard processes is that the scorecard picks up the values published by the data warehouse. By isolating the processes associated with the data warehouse with those of the balanced scorecard, each can be made more robust and efficient. At the same time, the consistency of the data is ensured because the balanced scorecard information is being sourced from the data warehouse. A growing number of organizations are applying the concept of systems thinking and iAnalytics to their next-generation balanced scorecard implementations. Using systems thinking to enable your balanced scorecard is a powerful way to drive integrated analytics within your organization. By using a data warehouse, a meta data-driven systems architecture and integrated analytics, you can now build and design the enterprise system architecture necessary to support your company's strategy, monitor its execution and improve performance at all levels. Margaret Pommert, Don Henderson and Valerie Brown, principal consultants for PwC Consulting's iAnalytics solution set, were major contributors to this article. ............................................................................... For more information on related topics visit the following related portals... DW Design, Methodology and Metrics/KPI/BSC. Michael J. Schroeck is a partner and the global leader of PwC Consulting's iAnalytics solution set. An early pioneer of integrated analytics and data warehousing solutions, Schroeck can be reached at mike.schroeck@us.PwCGlobal.com. PwC Consulting is a business of PricewaterhouseCoopers. Solutions Marketplace Provided by IndustryBrains Bowne Global Solutions: Language Services World's largest language services firm offers translation/localization, interpretation, and tech writing. 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