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CompRef8 / IBM Cognos TM1: The Official Guide / Oehler & Gruenes / 569-7 / Chapter 1IIntroduction ChapTer 1Challenges to Meeting Enterprise Performance Management RequirementsChapTer 2The Promise of In-Memory Analytics with IBM Cognos TM1ChapTer 3Evolution of IBM Cognos TM1parT01-ch01.indd 1 2/7/12 10:38:29 AMCompRef8 / IBM Cognos TM1: The Official Guide / Oehler & Gruenes / 569-7 / Chapter 1301-ch01.indd 2 2/7/12 10:38:29 AMCompRef8 / IBM Cognos TM1: The Official Guide / Oehler & Gruenes / 569-7 / Chapter 11Challenges to Meeting enterprise performance Management requirementsThe goal of this chapter is to highlight the increased demands on planning, analysis, and performance management processes in organizations. In todays highly volatile business environment, organizations demand more accurate plans, forecasts, and analysis to meet and exceed market demands. These demands range from increased market pressures, investment demands, profitability, strategy validation, and regulatory requirements for decision transparency. These increased demands require a fusion of planning and analysis capabilities at every level of decision making across an organization to create a more agile organization to meet these diverse requirements. Over time we have seen how markets respond to corporations that fail to meet stockholder expectation with decreased market capitalization. This chapter will focus on the effects of these forces on the following: Increased forecasting and budget cycles Rise of scenario analytics Heightened Enterprise Resource Planning (ERP) requirements Rising demands on data warehousesIncreased Forecasting and Budget CyclesLets start our review with causes of increased forecasting and budget cycles. There are a host of common problems that plague organizations. These problems include Long planning cycles Disconnected operational and financial plans Spreadsheet-based plans3ChapTer01-ch01.indd 3 2/7/12 10:38:29 AMCompRef8 / IBM Cognos TM1: The Official Guide / Oehler & Gruenes / 569-7 / Chapter 1 4 p a r t I : I n t r o d u c t i o n C h a p t e r 1 : M e e t i n g e n t e r p r i s e p e r f o r m a n c e M a n a g e m e n t r e q u i r e m e n t s 5 Lack of ownership and accountability Lack of control, transparency, and governanceLets take a look at some of these key challenges in more detail.Long Planning CyclesThe increased volatility and collection of data planning, budgeting, and forecasting cycles need to be in sync with business cycles in order to reflect current business assumptions. Before we begin, lets review a number of key definitions: Strategic plan A disciplined effort to define fundamental decisions and actions that shape and guide an organizations future, addressing what the organization is, what it does, and why it does it Budget/ annual operating plan Projection of revenues, expenses, and cash for a specified period of time (first year of SP). Identifies targets, at the line of business, functional, or cost center level Forecast A period-by-period projection of either revenue or expense that considers actuals to date and any changes to market conditions Modeling Process of developing models that characterize an organization, allowing it to evaluate the impact of decisions so as to fully understand the financial impact Reporting Collection, analysis, summarization, and presentation of the financial performance of the business Table 1-1 specifies the primary use, frequency, and key participants for each of these key terms.Now that we have key terms defined, lets turn our attention to an example of a planning process. As noted in Figure 1-1, in this example the budgeting, planning, and forecasting processes typically require the collaboration of at least four management layers, which include corporate finance, business unit leaders, line of business leaders, and finally cost center managers. Each of these planning groups has different levels of data and analysis requirements to render an accurate plan for their areas of responsibility. This requires a highly coordinated process for the target definition through distribution, aggregations of plans, variance analysis, and revisions.This process is further complicated by an increasing number of people in the planning process and pressure to recast assumptions based on changing business conditions. The notion of a quarterly plan no longer meets business requirements.Disconnected Operational and Financial PlansCompanies that roll out plans from the top down in the organization will not only have a limited view but will also suffer from lack of commitment and buy-in from many or all levels, which will drive a disconnection between management and contributors. As noted in the simple case shown in Figure 1-2, disconnected planning processes will send inaccurate 01-ch01.indd 4 2/7/12 10:38:29 AM 4 p a r t I : I n t r o d u c t i o nCompRef8 / IBM Cognos TM1: The Official Guide / Oehler & Gruenes / 569-7 / Chapter 1 C h a p t e r 1 : M e e t i n g e n t e r p r i s e p e r f o r m a n c e M a n a g e m e n t r e q u i r e m e n t s 5parT Idemand signals across the organization. For example, a corporate planning process cannot be accurate without answers to the following questions: What are the pipeline revenue forecasts? What are the expense demand signals? What are the planned capital expenditures to drive both current and strategic plans? Based on pipeline projects, what are the demand signals for employee hiring?Key Term Primary Use Frequency Who Is Involved?Strategic plan Define vision, strengths, weaknesses, opportunities, and threat (SWOT), high-level corporate goals, and objectives and strategies for how to attain themHorizon: 3 to 5 years, sometimes 10Frequency: Once per year Corporate executives Senior management Strategy Finance Department Budget/annual operating planFinancial guide for the current year: Control expenses, evaluate performance, and determine bonus compensationHorizon: One year, across months, quarters, weeksFrequency: Once per year/Infrequently updated Corporate planning Line of Business managers Cost Center managersForecast Provides the most current estimates for the balance of the year/horizonHorizon: Balance of the year or rolling week, month, quarter, annual.Frequency: Refreshed often Corporate planning Line of Business managersModeling What if analysis Acquisition modeling Scenario analysis Define contingency plansAd hoc Corporate planning Strategy Sales/HR/ITReporting Comparison with actual Cause and effect analysisHorizon: VariousFrequency: Monthly, Qtrly, Annual, Ad hoc Corporate planning Reviewed at all levelsTable 1-1 Key Terms01-ch01.indd 5 2/7/12 10:38:29 AMCompRef8 / IBM Cognos TM1: The Official Guide / Oehler & Gruenes / 569-7 / Chapter 1 6 p a r t I : I n t r o d u c t i o n C h a p t e r 1 : M e e t i n g e n t e r p r i s e p e r f o r m a n c e M a n a g e m e n t r e q u i r e m e n t s 7Figure 1-1 Planning cycle exampleCorporateFinanceBusiness Unit/LOB FinanceLine of BusinessLeadersCost CenterManagers?CreateTargets &TemplatesComplete/DistributeTemplatesCompleteTemplates4 > MonthsDistributePlanTemplatesSubmitPlanRevisePlanRevisePlanRevisePlanRevisePlanRevisePlanSubmitPlanAggregatePlansAnalyzeTargetvs. planFinalizePlanQ1ForecastAggregatePlans Budgeting, Planning and Forecasting ProcessesFigure 1-2 Disconnected planning processesMarket DemandFinance SalesReports Income statements balance sheet Cash flow Financial ratiosSalesPlanning &ForecastingWorkforcePlanningStrategic FinancialPlanning &ForecastingExpensePlanning &ControlOperatingExpensesOperations,Marketing, etc.HumanResourcesHeadcount& CompensationExpensesRevenue PlanDepreciationExpensesCapitalExpenditurePlanning01-ch01.indd 6 2/7/12 10:38:31 AM 6 p a r t I : I n t r o d u c t i o nCompRef8 / IBM Cognos TM1: The Official Guide / Oehler & Gruenes / 569-7 / Chapter 1 C h a p t e r 1 : M e e t i n g e n t e r p r i s e p e r f o r m a n c e M a n a g e m e n t r e q u i r e m e n t s 7parT IThese simple questions highlight the interrelationship of cross-departmental planning processes and the critical importance of connecting these processes for better decision making.Companies dont often have the complete picture of the impact of business drivers, the key metrics that steer their expenses and profits. These drivers, which are often interdependent and yet changeable given market realities, are buried in hundreds of spreadsheets and disparate databases spread throughout business functions. As a result, companies cannot command a single version of the truth and are hard pressed to produce reliable forecasts and plans, thus hindering growth and profitability.If the annual planning process takes several months to completewith time dedicated to reconciliation and reworkingit has long since been disconnected from the cycle that optimal performance requires. Similarly, by not forecasting frequently or as needed, a company cannot fully and expediently understand the demand for operating cash and thus make necessary reallocations of resources.Companies should seek to avoid disconnected planning processes where divisional or departmental objectives, goals, and targets do not align with those of finance. Such fragmented processes create silos that do not take departmental interdependencies into account. Additionally, plans for revenue, expenses, and capital expenses are often insular. Roll-ups to the profit and loss, cash flow, and balance sheet projections can therefore be slow and error-prone. This fragmentation leads to tedious reconciliation and reworking that drain productivity and hinder crucial, timely analysis.Spreadsheet-Based PlansMany companies carry out planning with spreadsheets that supplement inflexible planning systems or have gone to pure spreadsheet solutions for their annual budget and planning processes, which creates inaccuracies and miscommunication. Although spreadsheets are an excellent personal productivity tool, they are inherently unable to offer the control, security, and structured collaboration approach that enterprises require. Worse still, the manual overhead required in using spreadsheets lengthens the planning process.Not only are spreadsheets prone to data errors, but they also cannot handle the complex processes of business modeling, the aligning of data definitions, business assumptions, and financial and operations targets, and the complex business analytics required today, such as product or customer profitability. They also lack collaborative features such as workflow, metadata management, and version control for interdepartmental planning processes.In a volatile business climate, there is often no time to schedule scarce IT resources or explain evolving requirements. Users with varying software backgrounds, such as marketers or facility managers, want the applications they touch frequently for all aspects of the planning cycle to be easy to use and change. They want a measure of control without having to become programmers themselves. Moreover, when they cannot share spreadsheets and files easily with team members, their work will become isolated.Lack of Ownership and AccountabilityMost planning processes start from a top down approach from finance that imposes goals and targets without proper input from all lines of business. Worst of all, these plans do not have current actual information to provide context for planning decision processes. 01-ch01.indd 7 2/7/12 10:38:31 AMCompRef8 / IBM Cognos TM1: The Official Guide / Oehler & Gruenes / 569-7 / Chapter 1 8 p a r t I : I n t r o d u c t i o n C h a p t e r 1 : M e e t i n g e n t e r p r i s e p e r f o r m a n c e M a n a g e m e n t r e q u i r e m e n t s 9Without bidirectional value from all stakeholders in the planning process, which fosters a collaborative process between both upper and lower layers of management, ownership and accountability will be elusive.Lack of Control, Transparency, and GovernanceOne major concern today as organizations seek to increase their planning cycles and expand their reach in organizations is lack of control, transparency, and governance. Planning and forecasting processes require a number of different source systems to create a baseline forecast for planners. For many companies, critical data, from internal financial and operational sources and from external systems such as vendors and benchmarks (external marketing reports), usually resides in numerous systems, in an array of dissimilar formats. In a typically ungoverned fashion, employees piece together data from the various systems and then analyze and report on it differently, which leads to conflicting data definitions and views of the companys performance. These errors are then compounded when used as a baseline for a planning process. Without a strategy to centrally define and manage the data that provides critical information to planning and forecasting, processes cannot accurately model crossbusiness-function activity, much less access the critical data. Moreover, resources are engaged in redundant, expensive work.A second area of concern is transparency and governance surrounding the planning process itself. Unlike actual data, which requires a snapshot of the transaction at a point in time, plans and forecasts evolve and mature over time as assumptions and business requirements change. Creating an audit trail of some of the following key data points surrounding the planning submission is not only challenging but extremely time-consuming for the finance department. These questions include: How mature is the current plan? When was a plan submitted? Who changed or reworked specific areas, and when? Who approved the plan and when? What was the soft data or rationalization for key decisions? Are there certain submissions that need to be re-forecasted, or is a new planning process required?Most organizations lose this key information during re-forecasting after an initial planning cycle and are required to start all over again, increasing the cycle time.Rise of Scenario AnalyticsNow lets turn our attention to increased business requirements for analysis. As mentioned earlier, given the rise of uncertainty and volatility in organizational assumptions and business models, there is an increased requirement for scenario analytics to manage this uncertainty. Scenario analytics is the ability to evaluate several outcomes of a potential strategy before making a final decision. Scenario analytics provides the ability to: Explore and test what if scenarios Reduce risk and create contingency plans01-ch01.indd 8 2/7/12 10:38:31 AM 8 p a r t I : I n t r o d u c t i o nCompRef8 / IBM Cognos TM1: The Official Guide / Oehler & Gruenes / 569-7 / Chapter 1 C h a p t e r 1 : M e e t i n g e n t e r p r i s e p e r f o r m a n c e M a n a g e m e n t r e q u i r e m e n t s 9parT I Formulate growth strategies Best responses to economic change, a competitor move and marketing Profitability analysis to optimize price, channel, and marketing strategyLets take a look at each of these key areas in more detail.Explore and Test What If ScenariosAs organizations struggle to meet the demands of a highly volatile business environment, the ability to explore and test what if scenarios becomes critical in order to validate strategy assumptions and tactics. Lets first define what is meant by what if scenarios. According to BusinessDictionary.com, a common definition of what if scenarios or goal seeking is the ability to test key quantitative assumptions and computations (underlying a decision, estimate, or project) by changing them systematically to assess their effect on the final outcome. According to Porters Five Forces Model defined by Michael E. Porter of Harvard Business School in 1979, there are five main forces that drive not only an organizations strategy and tactics but also assumptions and decision making. These include the following: 1. The threat of the entry of new competitors 2. The intensity of competitive rivalry 3. The threat of substitute products or services 4. The bargaining power of customers 5. The bargaining power of suppliersEach of these forces as noted in Figure 1-3 drives decision making across finance and operations.These forces drive key questions that impact organizational performance. Some examples include: What if the economy slows? What if inflation rises? What if taxes increase or exchange rates change? What if supplier increases prices? What if a competitor makes an acquisition? What if governmental regulations change? What if ?These examples are only a small sample of business environmental changes that can impact the financial performance of a business and require an analysis process that allows individuals in each business department (Finance, Marketing, Production and Distribution, Customer Service, IT, Sales and Purchasing) to generate best- and worst-case scenarios. Now lets turn our attention to an additional requirement of scenario analytics: risk management.01-ch01.indd 9 2/7/12 10:38:31 AMCompRef8 / IBM Cognos TM1: The Official Guide / Oehler & Gruenes / 569-7 / Chapter 1 10 p a r t I : I n t r o d u c t i o n C h a p t e r 1 : M e e t i n g e n t e r p r i s e p e r f o r m a n c e M a n a g e m e n t r e q u i r e m e n t s 11Reduce Risk and Create Contingency PlansFinance executives are under increasing pressure from governments and business units to improve the quality and speed of risk reporting, insight, and decision making. They need to reduce risk exposures and losses, while anticipating the next big area of concern and opportunity. In order to fulfill this mandate, these areas require the ability to perform scenario analysis that synthesizes disparate data into an integrated, enterprise-wide view of risk across divisions, geographic locations, and risk classes. A common definition of risk management is systematic methods for estimating, assessing, and projecting risk associated with investments and business practices. These systematic methods frequently are founded on mathematical models known as drivers derived from historical data enhanced with statistical algorithms to provide an environment for testing outcomes. In areas like financial risk, models are expressed in logical relationships that enable simulation of scenarios and subsequent outcomes, including testing and consolidation of risk exposure.Risk management has a heightened importance; the increasing complexity of products and correlation across asset classes has elevated the importance of risk analytics to an enterprise level. Four areas, in particular, have fostered evolution in these applications: Increased regulation Cross-border, cross-asset investment strategiesFigure 1-3 Forces driving what if scenariosCompetitiveRivalryBargainingPower ofCustomersThreat of NewEntrySubstituteProducts orServicesBargainingPower ofSuppliersMarket DemandFinanceSalesSalesPlanning &Forecasting???????? ??WorkforcePlanningStrategic FinancialPlanning &ForecastingExpensePlanning &ControlOperatingExpensesOperations,Marketing, etc.HumanResourcesHeadcount& CompensationExpensesDepreciationExpensesCapitalExpenditurePlanningReverse PlanCap Ex.01-ch01.indd 10 2/7/12 10:38:32 AM 10 p a r t I : I n t r o d u c t i o nCompRef8 / IBM Cognos TM1: The Official Guide / Oehler & Gruenes / 569-7 / Chapter 1 C h a p t e r 1 : M e e t i n g e n t e r p r i s e p e r f o r m a n c e M a n a g e m e n t r e q u i r e m e n t s 11parT I Interest-rate risk, long a primary factor in fixed-income analytics and valuations, once again painting a clear picture of its impact on credit markets Magnitude of risk, difficulty in valuation, and lack of transparency in over the counter (OTC) derivativesRequirements for reducing risk and creating contingency plan as noted in Figure 1-4 include: Collect Data Data collection is a critical component of the ability to create accurate scenarios and contingency plans. Data needs to be complete and accurate, and delivered in an automated way. Current business volatility is increasing the requirements for more frequent data updates to feed the ever-growing requirements of business models. Analyze Analysis and definition of benchmarks to compare investment and/or financial performance against a designated standard that fits the firms style. Variations from benchmarks provide insight into over- or under-performance, as well as risking exposure. This provides a proactive approach to identifying risk-to-reward trade-offs. Develop Scenarios Scenario analysis enables the end user to test outcomes and estimate risk under various conditions. Scenarios can be derived from historical data and driver-based calculations or sophisticated statistical functions. The objective is to provide a test environment to garner insight into potential future outcomes and improve the firms ability to manage risk.Formulate Growth StrategiesAn additional area that what if analysis supports is the ability to formulate growth strategies. Business growth goals are essential in any companys long-term planning process. Organizations pursue growth through mergers and acquisitions (inorganic growth), focused business development to drive sales (organic growth), or a combination of both. These strategies go beyond increasing local market share. They serve as a means to enter new markets and seek international growth. Whether a company seeks to evolve through organic or inorganic growth, an in-depth analysis and market research coupled with scenario analysis is required.Figure 1-4 Reducing the risk processDevelopScenariosContingencyPlansCollectDataAnalyzed01-ch01.indd 11 2/7/12 10:38:32 AMCompRef8 / IBM Cognos TM1: The Official Guide / Oehler & Gruenes / 569-7 / Chapter 1 12 p a r t I : I n t r o d u c t i o n C h a p t e r 1 : M e e t i n g e n t e r p r i s e p e r f o r m a n c e M a n a g e m e n t r e q u i r e m e n t s 13Lets start with the key areas of increased business development or organic growth. Organic growth can include the following scenarios that require quantitative validation: Increased market penetration, which includes strategies to sell more products to existing customers. Market development through selling current products to geographic regions. Evaluation of alternative channels, which involves having customers access your product in new ways. These can include online stores, new licensing terms, and so on. New product development for existing and new customers. Development of new products for new customers.The second growth strategy is through acquisitions and mergers (see Figure 1-5). This approach can include the following: Horizontal This growth strategy would involve buying a competing business or businesses. Backward A backward integrative growth strategy would involve buying one of your suppliers as a way to better control your supply chain. Forward Acquisitions can also be focused on buying component companies that are part of your distribution chain, allowing better control over quality and costing of products.Figure 1-5 Formulating growth strategy processScenario AnalysisDivestitureAcquisitionsScenario AnalysisGrowth StrategiesMergersScenario Analysis01-ch01.indd 12 2/7/12 10:38:33 AM 12 p a r t I : I n t r o d u c t i o nCompRef8 / IBM Cognos TM1: The Official Guide / Oehler & Gruenes / 569-7 / Chapter 1 C h a p t e r 1 : M e e t i n g e n t e r p r i s e p e r f o r m a n c e M a n a g e m e n t r e q u i r e m e n t s 13parT IEach of these strategies requires the definition of multiple scenarios that present best- and worst-case scenarios and their financial impact on an organization, which typically includes an impact analysis of the companys income, cash flow, and balance sheet statements.Profitability AnalysisAn additional key area that what if analysis supports is profitability analysis. As noted in a recent International Data Corporation (IDC) market forecast (March 2008), profitability management represents 20 percent of the total performance management software and services market and was named by customers as the single most important business capability in the recent BPM Partners survey. Profitability analysis is a relatively new cross-enterprise discipline that unlocks profit potential to drive performance. Once a reporting exercise driven by management and accounting, it is now being used to help all parts of a company gain the insight necessary to deploy limited resources in pursuit of the most profitable opportunities. Company departments and divisions, such as sales, marketing, operations, and engineering, can use profitability analysis to answer simple profit-focused questions in the context of their day-to-day strategic and tactical business decisions. Key questions include Who are my profitable customers? What are my profitable products or service lines? Which are my profitable sales channels?Each of these questions involves a myriad of what if scenarios, as noted in Figure 1-6.Figure 1-6 Profitability analysis processWhat are our mostprofitable products orservices?Who are our mostprofitable customers?What if ScenariosWhat are our mostprofitable channels?01-ch01.indd 13 2/7/12 10:38:34 AMCompRef8 / IBM Cognos TM1: The Official Guide / Oehler & Gruenes / 569-7 / Chapter 1 14 p a r t I : I n t r o d u c t i o n C h a p t e r 1 : M e e t i n g e n t e r p r i s e p e r f o r m a n c e M a n a g e m e n t r e q u i r e m e n t s 15With this evolution of profitability analysis, profitability itself has moved to the front lines of a business where employees are making micro-optimization business decisions. This transformational shift is compelling finance organizations to move beyond the complexity of costing to focus on an approach that helps their entire business. The requirement that finance become a better business partner underscores the importance of looking at profitability modeling and optimization through the eyes of the company (as opposed to looking at it through accounting and financial lenses only). Finance starts with a business decision that needs to be made and moves back through a number of activities and disciplines to get the source information needed to make the decisions.Organizations are required to apply new business practices, tools, and automation to profitability analysis so that they can effectively: Assess the right profitability measures that optimize business performance. Move from a merely costing-oriented profitability reporting exercise to a forward-looking profitability modeling paradigm. Undertake profitability analysis for different dimensions of profitability (for example, customer, channel, product, and so on). Use statistical and advanced analytical techniques to predict profitability outcomes better and thereby work toward optimizing resource inputs. Tie profitability analysis to all other enterprise performance management processes such as planning, consolidation and control, strategy management, scorecarding, and more.However, given the current economic times, there are a number of challenges preventing organizations from meeting these requirements.Challenges with Profitability AnalyticsFinance organizations have traditionally monitored profit, but in many organizations analysis is done using spreadsheet models, where it is difficult to handle the complex array of cost drivers that are required for effective profitability analysis and forecasting. In a study conducted by BPM Partners, profitability analysis and optimization was ranked as the top business capability requirement. One of the challenges to business analysts is that the data is not being captured, or if the data is captured, it is not available in a consumable structure for interaction with the analyst community within an organization. Additionally, this information may be spread across numerous applications within an organization without a centralized view of this information. The situation may be further exasperated by the collection of this information in a data warehouse that has denormalized the data to a point where it is no longer useful. For these reasons, profitability analysis is a manual process in most organizations where analysts rely on spreadsheets to gather transactional data from source systems into a cohesive view of information, providing a time-consuming and error-prone process.01-ch01.indd 14 2/7/12 10:38:34 AM 14 p a r t I : I n t r o d u c t i o nCompRef8 / IBM Cognos TM1: The Official Guide / Oehler & Gruenes / 569-7 / Chapter 1 C h a p t e r 1 : M e e t i n g e n t e r p r i s e p e r f o r m a n c e M a n a g e m e n t r e q u i r e m e n t s 15parT IHeightened ERP RequirementsNow lets turn our attention to the heightened Enterprise Resource Planning (ERP) requirements driven by increased business volatility and velocity of change. ERP systems have traditionally done an excellent job of capturing transactional information about a whole host of enterprise information that spans a host of departmental requirements, as noted in Figure 1-7.An ERP solution set is a collection of loosely integrated prebuilt applications to meet the key requirements of Supply Chain Management (SCM), Finance Resource Management (FRM), Manufacturing Resource Planning (MRP), Customer Relationship Management (CRM), and Human Resource Management (HRM). Although organizations typically find it easy to accumulate data from their ERP system, it often remains unusable or unavailable to the decision makers who need it. Some of the barriers to better use of data include: Cost Organizations often balk at the cost of strategic investments necessary to make data usable, such as master data management and tools for creating standardized reports, scorecards, and dashboards. Ad hoc tools In the absence of automation, people create their own reporting and analysis tools, typically in Excel, which leads to various versions of the truth, different ways to interpret data, and lost productivity. Static reports Reporting systems based on static reports are costly because they require report builders to continually create new customized report iterations in order to meet end users evolving needs and preferences. Silos of data In the absence of a structured way to use and analyze data, and without integration among data sources, employees get at best a limited view of their business, which severely limits their analytical and decision-making abilities.Figure 1-7 ERP applicationsSCM HRMFRM CRMMRPERP ..01-ch01.indd 15 2/7/12 10:38:34 AMCompRef8 / IBM Cognos TM1: The Official Guide / Oehler & Gruenes / 569-7 / Chapter 1 16 p a r t I : I n t r o d u c t i o n C h a p t e r 1 : M e e t i n g e n t e r p r i s e p e r f o r m a n c e M a n a g e m e n t r e q u i r e m e n t s 17This problem is exasperated as global companies see growth through acquisitions and mergers where each acquired company has a different ERP system to integrate into the planning and analysis process, which requires a composite view of information across all business units, as noted in Figure 1-8.To add to the complexity of different ERP systems with varying account structures and data definitions, organizations are knitting together a composite view of data between these systems with spreadsheets. These spreadsheet shadow systems shown in Figure 1-9 proliferate in organizations due to the inability of ERP systems to meet the broad range of planning and analysis requirements within an organization.These spreadsheets play a valuable role within an organization by allowing individual users to combine disparate data sources to analyze, model, plan, and report. Unfortunately, although spreadsheets are an excellent individual personal productivity tool, their ability to create a collaborative planning and analysis environment that is governed by common data definition and business rules is problematic. Gaining a common view of information and translating that information into a fact-driven decision process that is actionable can be quite challenging. This has led to the implementation of a data warehouse strategy that is implemented to meet the needs of a specific ERP system and has its own challenges, which leads us to our next topic.Figure 1-8 Heterogeneous ERP systemsHRMERP 1SCMFRM CRMMRPHRMERP 4SCMFRM CRMMRPHRMERP 2SCMFRM CRMMRPHRMERP SCMFRM CRMMRPHRMERP 3SCMFRM CRMMRPCorporate Information Systems01-ch01.indd 16 2/7/12 10:38:35 AM 16 p a r t I : I n t r o d u c t i o nCompRef8 / IBM Cognos TM1: The Official Guide / Oehler & Gruenes / 569-7 / Chapter 1 C h a p t e r 1 : M e e t i n g e n t e r p r i s e p e r f o r m a n c e M a n a g e m e n t r e q u i r e m e n t s 17parT IRising Demands on Data WarehousesData warehouse managers are under increased demands to meet the current business requirements. These range from the diversity of corporate systems through acquisitions and/or mergers, increased collection of internal data on customers and employees, as well as the increased demand to access and interact with this information in a way that business users can consume to drive analysis and planning processes with little or zero latency, as shown in Figure 1-10.To solve the issue of data definition diversity within a single or multiple corporate ERP systems, the notion of a separate data store specifically designed for corporate reporting, analysis, and reporting has emerged in organizations. A data warehouse is a repository of data, which can provide most or all of the data and information requirements of an enterprise. This means that the data warehouse pulls data from all the production and other sources. Once the data is pulled onto an offline staging area, it is cleansed, transformed, and loaded in a sanitized, uniform, and well-organized manner so that you can run queries, reports, and all kind of analysis on the data.Figure 1-9 Spreadsheet shadow systemsHRMERP 1SCMFRM CRMMRPHRMERP 4SCMFRM CRMMRPHRMERP 2SCMFRM CRMMRPHRMERP SCMFRM CRMMRPHRMERP 3SCMFRM CRMMRPCorporate Information Systems01-ch01.indd 17 2/7/12 10:38:35 AMCompRef8 / IBM Cognos TM1: The Official Guide / Oehler & Gruenes / 569-7 / Chapter 1 18 p a r t I : I n t r o d u c t i o n C h a p t e r 1 : M e e t i n g e n t e r p r i s e p e r f o r m a n c e M a n a g e m e n t r e q u i r e m e n t s 19Data Warehouse ChallengesData warehousing has traditionally focused on relational technology. While well suited to managing transactions and storing large amounts of data, relational databases are typically unable to handle ad hoc, highly responsive scenario querying for large user communities. In addition to providing limited flexibility to meet the requirements of business analysts, these systems are maintained by IT experts due to the complexity of the data storage, and they lack the process management required of business analysts. Online analytical processing (OLAP) technology, however, provides the scalability, performance, and analytic capabilities necessary to support sophisticated, calculation-intensive queries for large user populations. For these reasons, relational and OLAP technologies are often combined for maximum benefits. This has led to a two-tiered data warehouse strategy or a spoke-and-hub approach, as noted earlier in Figure 1-9, that incorporates data marts to service the specific requirements of the business analyst.Figure 1-10 Increasing real-time demands on data warehousesFinanceDevelopScenariosCollectDataAnalyzedDevelopScenariosCollectDataAnalyzedSales & MarketingManufacturingDevelopScenariosCollectDataAnalyzedDevelopScenariosCollectData??????????AnalyzedSupply ChainHuman ResourcesDevelopScenariosCollectDataAnalyzedData WarehouseReal-Time Real-TimeReal-TimeSCM HRMFRM CRMMRPERP..01-ch01.indd 18 2/7/12 10:38:36 AM 18 p a r t I : I n t r o d u c t i o nCompRef8 / IBM Cognos TM1: The Official Guide / Oehler & Gruenes / 569-7 / Chapter 1 C h a p t e r 1 : M e e t i n g e n t e r p r i s e p e r f o r m a n c e M a n a g e m e n t r e q u i r e m e n t s 19parT ITwo-Tiered Data WarehousingThe two-tiered approach to data warehousing consists of a data warehouse (see Figure 1-11), where data from multiple sources has been extracted, transformed, and cleansed, and one or more data marts, where subject-specific data is deployed to business users. To understand why companies have adopted the two-tiered model, it is useful to examine the historic Figure 1-11 Two-tiered Data warehouse strategyData WarehouseHuman ResourcesFinanceSales & MarketingManufacturingSupply ChainHRMERP 1SCMFRM CRMMRPHRMERP 4SCMFRM CRMMRPHRMERP 2SCMFRM CRMMRPHRMERP SCMFRM CRMMRPHRMERP 3SCMFRM CRMMRP01-ch01.indd 19 2/7/12 10:38:37 AMCompRef8 / IBM Cognos TM1: The Official Guide / Oehler & Gruenes / 569-7 / Chapter 1 20 p a r t I : I n t r o d u c t i o n C h a p t e r 1 : M e e t i n g e n t e r p r i s e p e r f o r m a n c e M a n a g e m e n t r e q u i r e m e n t s 21development of data warehousing. Bill Inmon is widely recognized as the father of the data warehouse. In his book Effective Data Base Design (Prentice-Hall 1981), Inmon defines a data warehouse as a subject-oriented nonvolatile, and integrated, time-variant collection of data in support of managements decisions. A data mart is a subset of the data resource, usually oriented to a specific purpose or major data subject that may be distributed to support business needs. The concept of a data mart can apply to any data whether it is operational data, evaluational data, spatial data, or metadata. Data marts provide a repository of a business organizations data implemented to answer very specific questions for a specific group of data consumers, such as organizational divisions of marketing, sales, operations, collections, and others. A data mart is one or a grouping of multidimensional cubes called a model that is specifically designed to facilitate a departments planning and analysis requirement.Key Software RequirementsAs we have discussed throughout this chapter, there are a number of key capabilities to meet the diverse requirements of planning and analysis across an organization. These requirements were first defined by Dr. E. F. Codd in 19931 and extended in 1995. These papers outlined 12/18 rules on the requirements for OLAPs, and these rules were categorized in four main groups that include the following:Basic FeaturesThe basic features of Dr. Codds key software requirements are considered foundational to an OLAP solution and include the following: Multidimensional conceptual view To support ad hoc slicing and dicing of data across business-defined dimensions. Intuitive data manipulation Support of drag-and-drop manipulation of data as a direct action upon cell-level data. Accessibility OLAP plays a middleware role between transactional data and client. Batch extraction vs. interpretive Includes the support of internal staging of data as well as the ability to drill down to source data. OLAP analysis models Provide support for categorical, exegetical, contemplative, and formulaic models. Client/server architecture The separation of the data store and presentation layers to support the use of various clients. Transparency The use of OLAP embedded in the existing user experience. Multiuser support Provides concurrent data retrieval with security.Special FeaturesThe special features of Dr. Codds key software requirements are Treatment of non-normalized data Updated data in an OLAP environment should not be allowed to alter denormalized source data systems.1 E. F. Codd, S. B. Codd, and C. T. Salley. Providing OLAP (Online Analytical Processing) to User-Analysts: An IT Mandate. (Codd and Date, Inc., 1993).01-ch01.indd 20 2/7/12 10:38:37 AM 20 p a r t I : I n t r o d u c t i o nCompRef8 / IBM Cognos TM1: The Official Guide / Oehler & Gruenes / 569-7 / Chapter 1 C h a p t e r 1 : M e e t i n g e n t e r p r i s e p e r f o r m a n c e M a n a g e m e n t r e q u i r e m e n t s 21parT I Storing OLAP results OLAP data results should be kept separate from source data systems. Extraction of missing values An OLAP system should differentiate missing data from the value of zero. Treatment of missing values All missing values should be ignored.Reporting FeaturesThe reporting features of Dr. Codds key software requirements are Flexible reporting Provides reporting capabilities that present information based on user requirements. Uniform reporting performance As dimension and/or data volumes grow, reporting performance should remain responsive. Automatic adjustment of physical level The OLAP environment should adapt its physical schema automatically to adapt to the attributes of the model, data volumes, and density.Dimensional ControlThe dimensional control aspects of Dr. Codds key software requirements are Generic dimensionality Data dimensions must be equivalent in structure and operational capabilities. Unlimited dimensions and aggregation levels Unrestricted cross-dimensional operations Allow for calculations and data manipulations in any number of dimensions so as not to restrict the relationship between cells.These requirements were further enhanced by Nigel Pendse who coined the term FASMI2 (Fast Analysis of Shared Multi-dimensional Information), which condensed Codds earlier 12/18 rules into five keywords and definitions that allow organizations to summarize and evaluate OLAP capabilities. Lets take a look at these attributes. The FASMI attributes include Fast The ability to deliver user responses in five seconds or less. Analysis The ability to handle a broad range of relevant business or statistical analysis across an organization. Multidimensionality Must provide a multidimensional, conceptual data view and support multiple data hierarchies to meet an organizations actual business dimensions. Information Must contain the data required by the user and offer effective analysis techniques to make this information meaningful to the user.Each of these attributes is critical in meeting the solution requirements in each of the areas discussed in this chapter.2 Pendse, Nigel. What is OLAP? (The BI Verdict, 2005). 01-ch01.indd 21 2/7/12 10:38:37 AMCompRef8 / IBM Cognos TM1: The Official Guide / Oehler & Gruenes / 569-7 / Chapter 1 22 p a r t I : I n t r o d u c t i o nSummaryIn summary, the goal of this chapter was to highlight the increased demands on planning and analysis processes in global organizations. In todays highly volatile business environment, organizations demand more accurate plans, forecasts, and analysis in order to meet and exceed market demands. For organizations, who seek to meet these challenges through a fusion of planning and analysis capabilities at every level of decision making across an organization to create a more agile organization, the market rewards are great. This chapter focused on the effects of these forces, such as the increase frequency of forecasting and budget cycles, the importance of scenario analytics, the heightened ERP requirements, and finally, the rising demands on data warehouses. Lets continue our conversation in Chapter 2, where we will discuss the promise of in-memory analytics and how IBM Cognos TM1 is specifically meeting these diverse requirements in global organizations similar to your own.01-ch01.indd 22 2/7/12 10:38:37 AM

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