This article explores the concept of using Enterprise Performance Management—EPM—to drive big data use cases, primarily focused on integrated strategic, financial, and operational reporting, and analytic capabilities. These big data use cases demonstrate both the business and technical value necessary to justify investments in big data platforms such as SAP HANA.
Key Concept
Enterprise Performance Management (EPM) can be a very confusing discipline with many names, including Corporate Performance Management (CPM), Business Performance Management (BPM), and Total Performance Management (TPM). For the purposes of this article, EPM can best be described as the integration of enterprise strategy, finance and operations domains, and disciplines to enable closed loop and actionable decision-making capabilities within an organization.
Enterprise Performance Management (EPM) means different things to different people in different industries. To whom you speak can also greatly influence this perspective. If you speak with someone from the Business Intelligence (BI) world, they will tell you it’s just BI. If you speak to someone from the Budgeting and Planning area, to them EPM is mainly budgeting and planning. Similarly, someone from Personnel would say its human capital management. The reality is that EPM encompasses those disciplines and many more. A short list of EPM subjects would easily include any or all of the following shown in Figure 1.
Figure 1
EPM definitions by discipline
Historical Barriers to Achieving EPM Nirvana
Up until recently, achieving true EPM nirvana—defined here as the complete integration of enterprise strategy, finance, and operations—faced huge obstacles. To successfully integrate data and processes down to the necessary level of granularity required for drilling-down, from a high level KPI on a balanced scorecard, to detailed product SKUs, individual customers, orders, units and quantities, traditional database platforms posed significant technological challenges. These traditional data warehouse and data integration technologies were limited in terms of the data latency, storage, and performance aspects required for achieving this integrated vision. With the advent of in-memory data warehouse technologies, such as SAP HANA, fully realizing an integrated EPM paradigm is now a realistic goal. This article explores key EPM use cases that can now be enabled with SAP HANA.
Note
For some helpful tips on how to successfully manage your SAP EPM implementation projects, see my sidebar “Top 8 Leading SAP EPM Practices You Can’t Do Without.” This sidebar provides proven leading practices and potential solutions to jumpstart or enhance your EPM program.
EPM Big Data Primer
Historically, reporting and analysis have been vertical or operationally focused. As a result of specific business processes and corresponding data sources such as financial planning, supply chain, and HR, users focused on reporting and analysis from a silo-ed perspective. Gartner refers to this as the “little BI” paradigm. This paradigm is illustrated by the graphic in Figure 2.
Figure 2
Little BI ? Vertical- or operational-focused reporting structure
From an EPM perspective, the main focus is on cross-domain and cross-application integration. Specific business processes and data sources are integrated horizontally to provide a more holistic view from financial and operational perspectives. Gartner has coined the term “big BI” to describe this paradigm. This paradigm is illustrated by the graphic in Figure 3.
Figure 3
Big BI ? Horizontal or cross-domain reporting structure
Key EPM Use Cases for SAP HANA
From an EPM point of view, the use cases are to integrate horizontally across key business domains and applications, providing an integrated view across enterprise financials and operations. The following EPM big data use cases highlight the convergence of these key components.
Driving Expedited Financial Close Processes by Enabling Real-Time Planning, Forecasting, and Consolidations
One of the key use cases is to improve and optimize the financial planning, forecasting and consolidations processes. From a financial perspective, the five most obvious big data use cases are the following.
1. SAP HANA implements driver-based processes to improve planning, enhance reporting, and enable more outcome-based decision making.
In SAP budgeting, planning, and consolidations (BPC), true driver-based planning and forecasting capabilities require integration to source applications including sales and operations planning (S&OP) systems to fully integrate operational drivers used to initiate the planning and forecasting processes. The SAP HANA system enables the performance required to process and integrate operational drivers and underlying transactional details into SAP BPC to drive planning and forecasting.
The graph in Figure 4 demonstrates the sheer volume and complexity of integrating operational drivers into the planning and forecasting process.
Figure 4
The volume and complexities of planning and forecasting drivers
Furthermore, to perform more robust driver-based planning and forecasting capabilities by incorporating supply and demand drivers via S&OP, the BPC system needs to be able to handle granular and dynamic supply and demand driver changes and a bottoms-up, what-if analysis (Figure 5).
Figure 5
Integrating the S&OP process with planning
2. SAP HANA facilitates detailed, bottoms-up planning and forecasting.
SAP BPC on HANA enables planning at detailed levels (i.e., SKU, customer, business sub-entity, and sales district). Creating more detailed budget, plan, forecast, and actual data entails more detailed business dimensionality, resulting in additional data volumes and commensurate performance, which is perfectly suited for SAP HANA. SAP HANA’s real-time data layer enables faster data loading and processing, and write-back capabilities to improve BPC planning, forecasting, and reporting.
3. SAP HANA supports detailed allocations of expense.
Detailed allocation requirements are common for overhead and operational expenses. SAP HANA significantly improves the performance of BPC allocation script logic and Business Warehouse Business Add-In (BAdI) programs by optimizing the processing of the logic at the database layer.
4. SAP HANA enables analysis of actual, plan, and forecast data at all levels of granularity.
To achieve this goal, SAP HANA supports:
- Real-time drill-down into transactional details.
- Real-time loading of data and change data capture processes, which maintains data integrity and data latency.
In addition, SAP HANA’s in-memory processing improves bottoms-up metric and measure calculations and drill-down performance.
Figure 6 demonstrates the aforementioned integration from a product perspective. The full integration of the product dimension from various systems and data sources enables both top-down budgeting, planning and forecasting and bottoms-up profitability via cost allocations, and aligns actual, plan and forecast data at all level of granularity.
Figure 6
Integration of cross-domain processes and applications
5. SAP HANA enables Financial Consolidations, Inter-company Matching and Eliminations at a transactional level.
Whether to support full legal consolidations or financial planning, inter-company matching and eliminations can be supported at a granular level. At the same time, SAP HANA’s real-time data layer enables faster data loading and processing, and write-back capabilities, to streamline legal consolidations and intercompany processes.
Real-Time Integration of a Company’s Strategic, Financial, and Operational Information
One of the key pain points for executives, managers, and staff alike is the lack of visibility and transparency into the true drivers of performance. By integrating the enterprise back office, mid office, and front office, key stakeholders are able to connect the dots between the strategic, financial, and operational domains with the capability to perform both descriptive and prescriptive reporting and analytics (Figure 7).
Figure 7
An integrated EPM framework – Aligning back-office, mid-office, and front-office domains
Business executives and managers are keen to understand the true drivers of performance. For example:
- The CFO needs full visibility and transparency into performance issues.
- The CIO wants to know how ERP, EPM, data platform, BI, and analytic components fit together.
- Executives demand integration of enterprise strategy, financial, and operational information.
- Business wants to focus on actionable information to enable decision making.
Typically, financial and operational KPIs sourced from the EPM layer (i.e., SAP BPC, profitability & cost management [PCM], and S&OP) are visualized via dashboards or balanced scorecards. On one hand, KPIs can be correlated to enterprise strategies and objectives via strategy maps. To complete the vision, driver-based financial and operational KPIs need to be fully integrated to the underlying transactions to understand the true drivers of performance. For example, KPIs such as net sales, net margin, cost per unit, customer churn, and average order per customer can be analyzed by the detailed components that make up these calculations. Drill-down analysis from dashboard to query and analysis and reporting capabilities enable detailed intersections to be analyzed for each KPI by key business dimensions including time, customer, product, sale channel, geography, cost pool, and account (Figure 8).
Figure 8
Integrated reporting and analytics, encompassing strategic, financial, and operational domains
As shown in Figure 9, by analyzing the operational details driving the KPI via drill-down analysis, an organization can identify the performance anomalies and outliers explaining the results and prescribe corrective actions.
Figure 9
Integrated reporting and analytics – Drill-down from summary-level financials to operational details
The sheer volume of data, real-time data latency, and data modeling requirements to enable the integration of the aforementioned components are good use cases for SAP HANA. By leveraging its in-memory capabilities to enable instantaneous drill-down analysis, its replication capabilities to access real-time operational data, and its SQL-compliant modeling utilities to integrate across key EPM and transactional application data sources, SAP HANA can integrate enterprise strategy, financials, and operations.
Real-Time Customer and Product Profitability
As a subset of the previous use case (Real-Time Integration of a Company’s Strategic, Financial, and Operational Information), real-time customer and product profitability can be enabled to instantaneously determine a product or company specific profit and loss (P&L), and to determine whether or not it’s profitable to do additional business with the customer or to keep offering a product. By drilling down from KPIs such as net sales, COGS, SG&A, net margin, and other key components of a P&L statement, you can determine detailed customer and product profitability.
For example, to enable detailed customer or product profitability down to the most detailed level (i.e. customer name or SKU), data needs to be integrated across key EPM and non-EPM applications. The customer and product dimensions are typically comprised of hierarchical levels and members from heterogeneous systems (Figure 10). Some examples include:
- From a BPC planning perspective, users may budget and plan at an aggregate level such as the customer group, product tier, or product family levels.
- At the next level down, the sales and operations planning solution may entail demand-and-supply driven planning, forecasting, and scenario analysis at the customer industry, product group, and product sub-group levels.
- The subsequent levels may include transactional detail from SAP ERP (ECC) to enable operational reporting on actual data.
- At the lowest customer or product level, bottoms-up profitability analysis on detailed revenue and cost allocations data are enabled. The allocation of revenues and expenses enable both top-down and bottoms-up profitability analysis by base level product/service and customer and by actual, plan, and forecast categories.
Figure 10
Driving from summary to detailed level P&L and profitability analysis
To integrate all of the aforementioned customer or product levels and corresponding transactions, SAP HANA can be leveraged as follows:
- The sheer volume of customer or product-related data can be stored in-memory. In-memory capabilities also enable instantaneous drill-down from the aggregate product level to the most detailed product level to enable profitability analysis at the lowest level of granularity.
- The replication capabilities provide the most up-to-date customer or product information and related transactions.
- The calculation engine can facilitate complex customer or product-related calculations in real-time.
- The data-integration capabilities and SQL-compliant modeling utilities can be integrated across key SAP EPM and non-SAP EPM applications, including BPC, S&OP, ECC, and PCM.
Real-Time Customer Relationship Management to Achieve a Single View of the Customer
As an extension of customer profitability, a more holistic view and analysis of the customer can be achieved. The customer dimension can be leveraged throughout the planning, forecasting, profitability, and reporting cycles. As the full customer dimension hierarchical levels and members are integrated from the different EPM and non-EPM applications, key master data can be appended, including customer type, company size, number of employees, industry type, and other attributes from customer master sources (i.e., Dun and Bradstreet). In addition, unstructured data (i.e., email, corporate documents, news and blog articles, and Websites) can be integrated as well.
For example, analyzing detailed customer profitability can be further enhanced by incorporating key customer attributes to provide a more holistic view of the customer (Figure 11). Enhanced views of the customer can also facilitate other types of descriptive and prescriptive analysis, including customer marketing campaign and effectiveness, customer segmentation, and customer churn and buying patterns, which help predict customer behavior.
Figure 11
Extending customer profitability to drive a 360-degree view of the customer
Real-Time Predictive Analytic Capabilities
As the integration of EPM capabilities includes bottom-line financial metrics (i.e., net margin and EBITDA) as well the underlying operational influencers or variables that affect them (i.e., financial and operational drivers—for example, the number of orders, customer churn, and commodity prices), the underlying data models are perfectly suited for forward-looking, predictive, and what-if analytic capabilities. The integrated financial and operational data models can be modified to take advantage of SAP HANA’s open source R statistical programming language support. Multiple regression models can be created to verify the relationships and determine the weighted impact of operational influencers to bottom-line financial metrics. More importantly, SAP HANA’s real-time data integration and update capability ensures that the predictive models are constantly learning and becoming smarter on the fly as new and updated data continuously refine the accuracy of the models. From an end-user perspective, predictive models enable business users to accurately predict and control future levels of performance. Dashboards and balanced scorecards can provide links from KPIs to provide forward-looking, predictive, and what-if analytic capabilities (Figure 12).
Figure 12
Leveraging the EPM data foundation to drive predictive analysis
True EPM Capabilities Are Now a Reality with SAP HANA
At its core, EPM capabilities such as planning, forecasting, consolidations, and profitability transform detailed transactions into new structures and groupings based on specific business rules and logic, as well as incorporating new data and updates. Consequently, each EPM application contains specific, subject-oriented data at different levels of granularity. The challenge is to integrate across the domains and applications to provide a view of strategic, financial, and operational data.
SAP HANA’s real-time and in-memory data integration, modeling, storage, and processing capabilities fully enable and optimize the EPM capabilities listed in Figure 13. Now, with SAP HANA, achieving true EPM nirvana—the integration of enterprise strategy, finance, and operation—is possible.
Figure 13
EPM-driven big data use cases enable these key decision capabilities
Top 8 Leading EPM Practices You Can't Do Without
Whether you’re planning on implementing from the myriad of SAP EPM solution stack options, including budgeting, planning & consolidations (BPC), profitability & cost management (PCM), strategy management (SSM), or integration of the aforementioned solutions with BusinessObjects, there are many pitfalls to a successful implementation.
The following leading EPM practices have been tested and proven through many global EPM implementations. The failure to adhere to these practices may result in the following symptoms:
- Major scope creep
- Costly rework
- Delayed implementation
- Misalignment and animosity between business and IT
- Silo-ed and orphaned applications resulting in shelf-ware
- Eventual failure of the initiative
To maximize your chances of success, incorporate the following top eight EPM best practices and lessons learned on your project including potential solutions to either jumpstart or enhance your EPM program (Table 1).
| EPM leading practices |
Description |
Potential solutions |
|
Rome wasn’t built overnight; neither will your integrated EPM environment.
|
Successful EPM deployments are implemented in a piecemeal fashion. This phased and iterative approach enables small successes and building blocks over time to facilitate long-term success.
|
Address this leading practice by leveraging quick wins, including EPM rapid marts, accelerators, and pre-configured solutions.
|
|
Create a marriage of business and technology.
|
EPM is not driven purely by technology. The ultimate success of the EPM initiative is properly correlating the technical components and features with end-user decision-making capabilities that positively impact the bottom line.
|
Address this leading practice by leveraging holistic EPM assessments to fully define your EPM business architecture. This includes business goals and decisions, user cases, user groups and their entry points into reporting and analysis capabilities, and corresponding data. |
|
Don’t reinvent the wheel—leverage existing investments.
|
Typically, EPM initiatives don’t require the full purchase and the replacement of existing technologies. As long as the data architecture is designed to support and EPM architecture, many existing technologies can be leveraged and appended with smaller technology investments. |
Address this leading practice by leveraging existing EPM technical architecture that connects the dots between your existing investments in ERP, EPM, enterprise information management (EIM), and BI and analytic solutions.
|
|
Walk before you run—create a working prototype.
|
One of the critical success factors for the EPM initiative is to iteratively prototype the solution before full development. Prototyping facilitates buy-in from the business, validates existing requirements, and helps uncover new requirements.
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Address this leading practice by leveraging iterative- and agile-based prototyping methodology-based solutions, including the rapid management cockpit, the accelerated BI/analytics solution, and Mico Yuk’s BI Dashboard Formula (BIDF).
|
| Remember: Data is the key EPM enabler. |
As reaffirmed by Gartner, data architecture is the critical link that determines whether EPM initiatives will be successful or fail. Sound data architecture facilitates the development of bottoms-up planning, reporting, and analytic capabilities, and ultimately drives the decision-making capabilities that impact the bottom line.
|
Address this leading practice by leveraging your EPM data asset strategy profile and understanding your data and EPM information architecture to properly stage, integrate, model, store, and manage your data.
|
|
Create analytics with integrated and personalized storylines.
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The full value of BI and analytics is achieved through the integration of analytic components (e.g., dashboards, scorecards, and reports).
|
Address this leading practice by leveraging integrated EPM and BI solutions to enable comprehensive use-case-driven reporting and analytic capabilities to gauge past, current, and future levels of performance. |
| Actionable information is not enough—you must drive action. |
The availability of front-end reporting and analytics is not enough. Without actionable information, tools are simply technical applications that become silo-ed, obsolete, and, eventually, shelf-ware.
|
Address this leading practice by enabling key performance management processes. These include KPI performance-triggered alerts and root-cause analysis capabilities with drill-down into the business process management layer to understand true drivers of performance. |
| Consistently integrate enterprise strategy, financials, and operations. |
The successful integration of enterprise strategy, financials, and operations is EPM nirvana. Full transparency and visibility into how enterprise KPIs are correlated to corporate strategies and objectives, and how key business drivers correlate to bottom-line financial metrics and operational drivers, are the keys to achieving EPM nirvana. |
Address this leading practice by leveraging the full suite of integrated EPM solutions to connect the dots between enterprise strategy, key financial KPIs, and operational drivers, and bringing together enterprise data, applications, business processes, and decision-making capabilities. |
Andrew Joo
Andrew Joo is the Enterprise Performance Management (EPM) leader for North America within the SAP Business Analytics Center of Excellence at IBM Global Business Services. He has more than 16 years of deep strategy, financial, cost, management, and technology consulting experience, encompassing leading industry firms (Big 4, system integrators) and a multitude of technologies (SAP, Microsoft, Oracle), industries (private and public sector), processes and methodologies (PMI, Agile, SDLC), and roles (functional and technical). In addition to serving as a thought leader and subject matter expert in the field of financial advisory, EPM, BI, and Information Management disciplines, he has been integral in project delivery, practice development, business development, and industry and community outreach efforts. He has pioneered unique methodologies and go-to-market solutions using an integrated EPM (EPM-BPC) and BI paradigm. The aforementioned have been featured in key industry events and publications, including SAP Sapphire and SAPinsider’s Financials and Reporting & Analytics conferences. He holds an MBA in strategy/marketing from Rice University, an MS in management information systems, and a Bachelor’s in finance. He is the author of 100 Things You Should Know about SAP NetWeaver BW, full of time-saving tips and tricks and step-by-step instruction.
You may contact the author at ajoo@us.ibm.com.
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