2 Master data deployment and management in a global ERP implementation
3 Contents Master data management overview Master data maturity and ERP Master data governance Information management (IM) Business processes and data definition Financial benefits
4 Master data management (MDM) overview
5 Enterprise value impact of MDM MDM impacts multiple business drivers and shareholder value. A global ERP implementation can enhance the significance of these drivers due to its magnitude. Each of these drivers can be managed with a proper approach to MDM in an ERP implementation framework.
6 Barriers and opportunities of MDM in an ERP initiative Barrier Opportunity Barrier Opportunity Barrier Opportunity Barrier Opportunity IT technology investments to support leading MDM designs are necessary, but can become significant in a global approach if not assessed properly. Understand the current MDM maturity level and develop a target to enhance technology innovation and capability. Improper global master data management and governance result in inconsistent financial reporting and reduced supply chain efficiency. An MDM organization and governance model can enforce policies and procedures to align with regulatory and audit requirements. Lack of data consistency and integrity in item, customer, supplier, and financial data contribute to chronic process barriers and excessive costs between facilities within the organization. Data cleansing and consolidation across the enterprise can improve supply chain efficiency, reduce financial reporting effort, and make acquisitions easier to absorb. Inadequate master data business processes cause delays of product introduction and an increased cost of quality. Properly designed MDM processes can reduce errors and contribute to continuous improvement efforts.
7 Master data maturity and ERP
8 Enterprise architecture maturity level description* Level 1 Level 2 Level 3 Level 4 Level 5 Process Process awareness exists and followed on an ad hoc basis. Processes are repeatable for architecture development and governance. Processes are defined for core and support and exist for all layers. Process outlined for EA development, architectural standards, portfolio management and governance key performance indicators. Processes are proactively assessed and optimized in alignment with the business and IT. Engagement and organization Project-based engagement at solution level. Little or no input in organizational decision making with minimal coordination. Indirect engagement through IT. Indirect engagement based on technology portfolio. Engaged directly with business and is a trusted adviser. Engaged at all levels and plays a strategic role of a partner and advisor. People Practitioners are skilled in areas of solution architecture. Practitioners with skills in the area of architectural standards and solutions. Practitioners with developed skills in areas of domain architecture in all disciplines and layers. Have the business architecture skills augmented by core skills, such as communication, presentation, etc.. Have capabilities ranging from technology to the business domain. Governance Governance checkpoints limited to software development life cycle. Governance process is primarily established for solution gating and standardization. Process and architectural governance is defined and managed for variances. Governance also includes influence on IT investment decisions and use of emerging technologies. Governance is optimized and is focused on value, agility, and alignment with strategy. Target state falls in this range* *Source: EA Architecture Capability Maturity Assessment Nov 5th Assessment conducted using the Enterprise Architecture Assessment Framework (EAAF) created by the US Federal Government and adopted by The Open Group, and merged with the Deloitte Enterprise Architecture Framework
9 Domains Enterprise architecture maturity level description* Maturity level is not just technology. Maturity level defines the data culture of the organization. A global ERP footprint should have at least a Level 3 maturity level. Enterprise architecture maturity level description* Level 1 Level 2 Level 3 Level 4 Level 5 Total capabilities Developed capabilities to provide architecture and standards at solution level. Capabilities also include domain architecture. Capabilities involve enterprise-shared services and driving IT simplification. Capabilities include driving technology innovation, agility, and emerging technology direction. Capabilities are established at all domains, layers, and levels and are continuously improved and optimized. *Source: EA Architecture Capability Maturity Assessment Nov 5th Assessment conducted using the Enterprise Architecture Assessment Framework (EAAF) created by the US Federal Government and adopted by The Open Group, and merged with the Deloitte Enterprise Architecture Framework
10 MDM maturity model The model should be used to determine the current state and future target of the application architecture. Maturity level upgrade can run parallel or as a part of an ERP implementation. The higher the maturity level, the higher the technical complexity and cost. The desired maturity level should be based upon business strategy and operations. Definition Technical architecture example Level 5 Master Data Stored in Multiple Master Data Domains Service-oriented architecture, e.g., Web service portals Level 4 Master Data Stored in Single Master Data Domain Data-specific applications integrated to operational applications, e.g., CDI, PIM. Level 3 Master Data Stored in Multiple Software Applications Operational applications integrated together, e.g., CRM, ERP, SCM Level 2 Master Data Stored in Single Software Application All data stored in ERP application Level 1 Master Data Stored in Databases Data stored in data warehouses, network applications, etc.
11 Data domain Examples The data domain assessment will ultimately determine where the master data exists. The amount of integrations may increase as a result of specific master data application, but MDM and data security typically improve as a result. Management, control, and timing of updates or introduction of new master data is typically deployed more efficiently as the maturity level improves.
12 Current state technology assessment Maturity-level alignment requires a technology assessment. Data modeling, specifications, and testing have a high degree of dependency on the available technology. Global ERP capabilities can be limited or enhanced by the technology landscape available. Standards and policies define the scope of the ERP configuration that can be enabled. Assessment Applications landscape Infrastructure Data domains Information standards and policies Description Assess the applications that may affect the implementation in order to identify guiding technology principles and constraints Assess the capacity of the network hardware, connectivity, processing speeds, and redundancies Assess the data domains, i.e., customer master, item master, supplier master for the maturity level, and current data management challenges. Assess the enterprise-wide policies and information standards as it applies to definition, management, data maintenance, and data content standards.
13 Domain data modeling cycle Regardless of the level of maturity, data modeling must occur to initiate the data design and development. The data modeling clarifies the locations and interactions of the master data within the ERP system and supporting applications. Data transformation may be necessary between the source and the target systems, i.e., concatenation, justification, and formatting. The data model is also required as an element of the functional specifications.
14 Master data governance
15 Governance Global model Approach A common global implementation model is required to attain the majority of the supply chain and financial improvements from business case benefits associated with it. Application Program Integrations (API) define the technical scope of the global model as 80% fit of the global requirements. Result Standardized and compliant processes Common technology Enables regional and global growth strategies Knowledge sharing and transfer
16 Governance Global model (cont.) The global governance design is centered on a global model made up of: Repeatable methodology supported by the MDM organization Model activities begin at the strategy level A common technology strategy and application blueprint Standard data definitions to support consistent financial reporting and global material planning and sourcing strategies
17 Governance Business foundation Data business foundation of a global model incorporates the key business functions of an ERP implementation. Master data is one of the three platforms from which the business is transformed with an enterprise initiative. This typically results in sustainable and consistent business operations supported by organization sponsorship.
18 Governance Strategic model The governance model should direct and consolidate the data design, tools and management capabilities to facilitate the supply chain, and financial transformation initiatives of the enterprise. A properly designed strategy can enforce organizational process structure and discipline. A global data strategy can provide reduced operating costs and improved response to market demands. Enterprise content and data management is where the focus for ERP exists.
19 Governance Content The content governance model defines the amount and type of data required to operate each business function. Content management controls the master data life cycle, and country localization requirements. This results in providing the requirements for the prioritization, sequencing, and timing of data content throughout the enterprise.
20 Governance Data The data governance model defines the data requirements with the master data to run the business. It involves people throughout the organization. Master data management controls the ERP configuration and data delivery. This can result in well-defined data parameters for the business function to utilize and makes data transactions more efficient.
21 Data governance Organization The MDM organization includes global, regional, and local representatives from IT and business functions. The primary responsibility of the MDM organization is to manage, coordinate, support, and deliver global, regional, and local business data initiatives. Managing information geographically can enhance data globalization and transactional automation.
22 Information management (IM)
23 Information management IM capability includes tasks to plan, design, implement, and monitor data in applications. The objective of IM in an ERP initiative is to produce high-quality master data that can be distributed throughout the enterprise. Data quality manages the state of the data and migration manages the standards and control.
24 Data quality risk assessment The purpose of this assessment is to identify the risks associated with financial, compliance, governance, or operational standards. Assessment incorporates master data components and the data source included in the ERP initiative. The assessment criteria includes: Data volume Business audit requirements Regulatory requirements Data elements identified as high risk must be aligned with either the global or regional MDM organization.
25 Data quality business impact assessment The purpose of this assessment is to identify the risks associated with the business in the areas of geography, location, or departments. The assessment incorporates master data components, the data source, and the business functions associated with the ERP initiative. The assessment criteria includes: Level of automation Number of users Impacted processes Data elements identified as high risk must have very limited user modification accessibility.
26 Data quality process The data quality objective is to improve data accuracy. The five-phase methodology focuses on data cleansing in accordance to data standards. Each phase has a defined duration in coordination with the ERP implementation timeline. This process can be applied independently by region or location.
27 Data migration process The data migration objective is to align and synchronize data across the enterprise. The five-phase methodology focuses on specifications and data conversions in accordance to the global data model. Each phase has a defined duration in coordination with the ERP implementation timeline. This process must occur during the Deliver phase of the ERP implementation for the sites involved.
28 Unit test approach Data quality and migration The data quality and data migration processes require testing and validation. Unit testing provides the means to assess data accuracy and consistency across the enterprise. Testing should be aligned with the ERP methodology incorporating standard testing tools and processes across the enterprise. The testing begins the integration of the data into the ERP configuration.
29 Business processes and data definition
30 Master data business process summary Master data business processes are considered key business processes. Generally, consist of request, review, approval, and update tasks. Master data business processes are considered key business processes. The processes consist of four phases that provide the mechanism for controlled change. The processes will take different paths and have different owners depending on the global, regional, or local impact. Updating volume and frequency is dependent on the geographical impact of the change.
31 MDM processes Master data processes involve master data components typically included in ERP initiatives. Similar to other key business processes, master data processes are maintained across the enterprise. Processes require cross-functional business involvement. IT and MDM metrics and Service Level Agreements (SLA) are derived as a result.
32 ERP data master design Utilizing common design considerations can simplify the ERP configuration and maintenance.
33 Master data geographical attribute summary (i) Utilizing geographical attributes can help in limiting and controlling the data users have access to modify. Global data attributes contain what is required to structure the ERP system for the enterprise. Regional data attributes contain what is required to structure the ERP system for the country. Local data attributes contain what is required to structure the ERP system for the site.
34 Master data geographical attribute summary (ii) Fewer duplications, synchronization errors, and data quality situations may result. Monthly and annual reporting can become more consistent.
35 Financial data Chart of Account (CoA) hierarchy Level of detail Defining the CoA hierarchy properly can allow for more efficient financial reporting and consolidation. Once the CoA is implemented in the global model, redesign can become challenging. All businesses in all geographies with the ERP footprint should align and use the global CoA for ease of reporting.
36 Financial benefits
37 Product life cycle benefits Product innovation/life cycle management techniques survey A little more than 50% of the companies surveyed utilize Product Data Management software Source: Deloitte Consulting LLP
38 Product life cycle benefits (cont.) Benefits The vast majority of companies using product data management software see benefits The vast majority of companies using product lifecycle management software see benefits Source: Deloitte Consulting LLP
39 MDM/ERP financial impact examples Impacts can be directly lined to business drivers. Financial impact improvements can generate an ROI in as little as three years. It also has the capability to enhance customer and supplier relationships.
40 Summary The key components of an effective management initiative are: Domain Governance Information management Business processes When properly executed, investment returns can occur in approximately three years and positively impact customer and supplier relationships with improved productivity. In organizations where data is not recognized as a key business driver, order fill rates, revenue per employee, item costs, and the cost of quality may lag behind high-performing companies.
41 Presenters For more information, please contact: Rick Olson Specialist Leader Deloitte Consulting LLP 227 W. Trade Street, Suite 1100 Charlotte, NC Luke Tay Specialist Leader Deloitte Consulting LLP 191 Peachtree St, NE #2000 Atlanta, GA
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