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5 Best Practices for SAP Master Data Governance By David Loshin President, Knowledge Integrity, Inc. Sponsored by Winshuttle, LLC Executive Summary Successful deployment of ERP solutions can revolutionize
5 Best Practices for SAP Master Data Governance By David Loshin President, Knowledge Integrity, Inc. Sponsored by Winshuttle, LLC Executive Summary Successful deployment of ERP solutions can revolutionize company operations in terms of increased opportunities, decreased costs, improved data integrity, and stronger productivity. To reap these benefits, however, organizations must take care to properly manage master data shared across multiple business functions. Errors and omissions in master data cause a plethora of operational problems, including difficult planning cycles, delayed production, incorrect billing, and failed deliveries. Poor master data also limits sales insights and analysis, produces inferior customer experiences, and leads to suboptimal procurement decisions, costing companies millions of dollars in lost opportunities and mistake rectification efforts. Master Data Governance can empower organizations to enhance data quality and streamline operations. Effective Master Data Governance does not strangle corporate agility. On the contrary, it acts as an evolving and adaptable process that integrates policy review and procedural updates to promote maximum business performance. The Master Data Governance best practices described in this paper offer proven strategies for driving data integrity and productivity improvements across a business enterprise. With innovative technology solutions like Winshuttle for Master Data, companies can easily automate processes and integrate governance-related procedures into existing business workflows, gaining further rewards in performance and cost-savings. Introduction In most organizations, business application development is aligned by line of business or corporate function, such as sales or finance. However, many key activities, such as order-to-cash or procure-to-pay, cut across functional and organizational boundaries and thus require a different approach. Applications that automate cross-functional activities must be able to share and exchange data among multiple domains. When siloed application development and cross-functional business process do not match, however, significant complications arise. Most notably, variations in meaning and structure can lead to difficulties in data integration and sharing across functions. Master Data Management enables organizations to effectively share relevant data among multiple crossfunctional processes, particularly those employing ERP applications. However, coordinating among different participants across different steps of workflow processes requires some oversight. A centralized Master Data Governance program can provide the oversight necessary to make crossfunctional coordination a success. Master Data Governance is not solely, or even primarily, a technical process, but a system of roles, rules, and rights that determine how people interact with and share data. This paper discusses Master Data Governance best practices that effectively facilitate: Collecting business requirements for master data oversight Capturing and managing master data business rules Managing the processes for creating and updating master data entities Motivating Factors While Master Data Management intends to support high-quality, shared data repositories, solving infrastructure problems without addressing policies and processes can often lead to delays or even project failures. Introducing Master Data Governance can help organizations address common challenges associated with shared data operations, such as: Alignment of Data Expectations: In a siloed environment, each data system is engineered to meet the quality and usability needs of the business function it supports. When a master data system is deployed, however, shared records must meet the needs of all users within its scope. This requires well-defined processes for identifying who the users are, how they plan to use the data, and what their quality requirements are. Furthermore, business rules must be centrally managed to ensure that everyone s needs are met. Effective Data Sharing: There is a big difference between utilizing a master repository for consolidating data dumped from multiple sources and managing a master repository for effective data sharing to support end-to-end processes. The former approach is prone to inconsistencies and errors, whereas focusing on the usage characteristics of a shared data asset can enable organizations to improve productivity, reduce errors, and speed up time to value. Oversight and Governance: By necessity, effective data sharing requires oversight and governance. In scenarios that have many data touch points performed by different users, there is a need to oversee the sharing of the data associated with workflow processes across functional lines. 5 Best Practices for SAP Master Data Governance Whitepaper 2 Five Best Practices for Master Data Governance Master Data Governance establishes policies and processes for the creation and maintenance of master data. Observing these policies establishes a level of trust in the quality and usability of master data. The best practices listed below outline successful strategies for implementing and maintaining effective Master Data Governance for maximum collaborative benefit and streamlined operations across an organization. 1: Establish a Master Data Governance Team and Operating Model Most business processes cross functional boundaries, requiring multiple individuals to be involved in creating master data and executing business processes. However, because organizations are typically organized around function, a question arises as to who owns cross-functional master data processes. To ensure that these processes are not impeded by data errors or process failures, the first best practice calls for creation of a central data governance team. This data governance team should be composed of representatives from each area of the business and be vested with the authority to: Define and approve policies governing the master data life cycle to ensure data quality and usability Oversee the process workflows that touch master data Define and manage business rules for master data Inspect and monitor compliance with defined master data policies Notify individuals when data errors or process faults are putting the quality or usability of the data at risk 2: Identify and Map the Production and Consumption of Master Data The quality and usability of the master data must be suitable for all defined purposes. Therefore, it is necessary to not only identify the requirements of all master data users, but also ensure that these requirements are met throughout the data lifecycles associated with cross-functional processes. In order to properly assess users requirements and expectations for data availability, usability, and quality, one must have a horizontal view of the workflow processes and understand who the data producers and data consumers are. To achieve this, the second best practice involves documenting business process workflows and determining master data production and consumption touch points. Whether looking at the workflow processes for creating shared master data or reviewing activities that consume such data, one should link the assurance of consistency, accuracy, and usability to specific master data touch points. Documenting process flow maps enables one to consider all master data production and consumption touch points in each workflow activity. As a result, this practice empowers organizations to identify opportunities for implementing governance directives and ensuring compliance with enterprise data policies. Any point of data creation or updating may be monitored or inspected for adherence to established policies and data quality rules. Reviewing usage scenarios can also expose data dependencies that must be carefully monitored to ensure consistency in definition, aggregation, and reporting. 3: Govern Shared Metadata: Concepts, Data Elements, Reference Data, and Rules Discrepancies and inconsistencies associated with shared data are among the key factors driving the need for Master Data Governance. Inconsistencies in reference data sets such as product or customer category codes are often especially abundant. When no centralized authority is available to define and/ or manage reference standards, duplication and overlap of user-defined code sets can cause significant problems within an ERP environment. 5 Best Practices for SAP Master Data Governance Whitepaper 3 To reduce the potential for negative impact due to inconsistencies, the third best practice advocates centralizing the oversight and management of shared metadata, with particular focus on entity concepts (such as customer or supplier), reference data, and corresponding code sets, as well as data quality standards and rules for validation at different touch points in the data life cycle. Governance and management of shared metadata involves defining and observing policies for resolving variance across similarly named data elements. Since each business function may have its own interpretation of the terms used to refer to master data concepts, a collaborative process should be implemented to compare definitions and resolve differences to pave the way for a standard set of master concept definitions. Shared metadata management also requires establishing procedures for normalizing reference data code sets, values, and associated mappings. Normalizing shared master reference data can alleviate a lot of pain in failed processes and repeated reconciliations when reporting does not match operational systems. Data quality rules used for validation can be centralized and governed to ensure consistency in their application for inspection, monitoring, and notification of errors or invalid data. 4: Institute Policies for Ensuring Quality at Stages of the Data Life Cycle The fundamental value of instituting Master Data Governance is the reduction in data variance, inconsistency, and incompleteness. These types of data issues typically result from the absence of quality control processes designed to prevent errors. Cleansing or correcting data downstream is a reactive measure, although as long as corrections are shared among all of the data creators and consumers, this may be acceptable as a last resort. To ensure that shared data meets the needs of the organization, as specified by the collective data requirements, the fourth best practice promotes a directed approach of defining data policies and instituting governance processes for data discovery and quality assessment. By analyzing potential anomalies within the master data set, this practice helps to identify structural inconsistencies, code set issues, and semantic differences inherent in the data. Once potential issues are identified, many can be resolved with stop-gap controls. It is also advisable to define policies for instituting controls within the process workflows, as well as application programs that can be inserted into the workflows based on the analysis of the production maps (see the second best practice above); this will reduce the introduction of errors. 5: Implement Discrete Approval and Separation of Duties Workflow Since cross-functional processes (such as order-to-cash) are not owned by any particular line of business or functional area, the question of workflow process ownership becomes key in governing their successful execution. Drilling down on this question exposes different facets of the challenge. One facet involves navigating the relationship between business teams who define the workflow processes and IT teams who develop and implement the applications for these workflow processes. Another facet is that there are different types of processes some require complex IT solutions, while others can be easily deployed by business users without IT involvement. A third facet involves the oversight of decision-making within process workflows, such as reviewing items, approving new records, or signing off on completed tasks. Because process workflow ownership is effectively assigned across the areas of the business, there is a need for a set of policies and methods to centrally oversee the successful execution of all stages of the workflow. The fifth best practice advocates integrating discrete approval of tasks into the operational aspects of Master Data Governance. Separation of duties can be discretely integrated into the application environment through the proper delineation of oversight, as part of an approval process that focuses on the business use of the data and does not require IT intervention. 5 Best Practices for SAP Master Data Governance Whitepaper 4 Implementing this practice on a small scale might be managed manually. However, as the number of processes grows, the number of touch points increases, and the need for discrete separation of duties expands, a manual solution becomes inefficient. Furthermore, relying on IT for application development may introduce a bottleneck in getting the processes into production. To alleviate these pressures, organizations should explore how different tools could empower business users to develop and deploy workflow processes without IT involvement. Technology and Automation Innovative technology solutions can help organizations to implement and manage data policies and processes necessary for successful Master Data Governance. The best types of technologies would automate the management of these processes and related approval functions, allowing master governance to be directly integrated into the execution of the processes. Automating Master Data Governance practices relies on a variety of core competencies. When evaluating solutions, organizations should look for these types of capabilities: Broad access to master data repositories (such as those integrated within ERP systems) Centralized management of data element definitions, structures, reference data sets, and other relevant metadata Shared data quality business rules Flexibility in developing and implementing workflow processes that reduce the IT bottleneck Incorporation of inspection and monitoring of compliance with data quality, validation, and decisioning rules Winshuttle for Master Data In business since 2003 and with over 1,500 customers worldwide, Winshuttle is the leading provider of effective solutions that improve ERP usability and help organizations get the most out of their SAP investment. Winshuttle for Master Data provides the quickest path to better SAP master data, allowing business teams to make immediate improvements in relevant master data processes without any programming. Easy to use and deploy, this market-leading tool empowers organizations to quickly resolve critical data issues such as those related to customer, material, vendor, and other key master data objects as well as progressively enhance master data integrity. Organizations can also improve data validation, design and deploy workflows, and perform mass maintenance. Winshuttle for Master Data features the following key capabilities: Data Validation: Validate master data requests at the point of entry against rules inside and outside of SAP. Data Governance: Create role-based and traceable governance processes that route change requests to different stakeholders for input, review, and approval. Mass Maintenance: Perform mass maintenance directly from Microsoft Excel without programming. 5 Best Practices for SAP Master Data Governance Whitepaper 5 Conclusion As companies increasingly rely on ERP-based workflow processes that cross corporate functions and lines of business, they must take measures to safeguard the integrity of the shared data assets. A centralized Master Data Governance program can fulfill this objective, bringing the necessary oversight across multiple business functions. Master data errors cause costly production delays, project failures, and missed deliverables. By ensuring master data quality, organizations can reduce business interruptions and speed up productivity, generating significant cost-savings and increasing business opportunities in the process. The best practices outlined in this paper offer strategic guidelines for implementing effective Master Data Governance within a business enterprise. Smart technology tools like Winshuttle for Master Data can also benefit this effort. Winshuttle for Master Data allows for easy integration of governance policies and procedures within the SAP environment. With robust process automation and no required programming, it not only streamlines data validation, maintenance, and accountability, but also empowers business users to manage SAP-related workflow processes without IT involvement. For more information on Winshuttle for Master Data, visit or Theresa Ciacchi Senior Account Executive - Business Solutions Group Adaptive Corporation, Inc. 118 W. Streetsboro Rd. Suite 221, Providing Innovation by Design Hudson, OH Best p. (440) Cleveland Dayton San Francisco Whitepaper c. (440) Irvine Raleigh Dallas Toronto e.
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