SAP MDG Online Training

SAP Master Data Governance is arguably the first-to-market integrated and active data governance application via its governance capabilities for creation, maintenance and replication of master data upfront (upstream) of line-of-business applications.SAP MDG is a prebuilt solution that requires configuration rather than a toolset that requires coding. SAP MDG can also be configured to leverage consolidated master data from SAP NetWeaver MDM as well.

With Tysco Online Trainings the SAP MDG Training is coordinated by best industry experts and the SAP MDG tutorial is prepared with best industry updates for offering participants best professional insight over modules. The training is available for individual and corporate batches. To know more about this online SAP training course contact reach at helpdesk of Tysco Online Trainings today.

Prerequisties

  • Experience in Business Suite / ERP
  • Experience in NW MDM
  • Experience in Data Services, Data Quality, Information Steward

SAP MASTER DATA GOVERENCE ONLINE TRAINING COURSE CONTENT

TOPIC 1: MASTER DATA GOVERNANCE OVERVIEW
TOPIC 2: WHAT’S NEW WITH MASTER DATA GOVERNANCE 6.1
TOPIC 3: OVERVIEW OF MASTER DATA GOVERNANCE ARCHITECTURE
TOPIC 4: DATA QUALITY REMEDIATION
TOPIC 5: OVERVIEW MASTER DATA GOVERNANCE FOR MATERIAL
TOPIC 6: WHAT’S NEW WITH MASTER DATA GOVERNACE FOR MATERIAL IN MDG 6.1
TOPIC 7: OVERVIEW MASTER DATA GOVERNANCE FOR SUPPLIER & CUSTOMER PART I
TOPIC 8: OVERVIEW MASTER DATA GOVERNANCE FOR SUPPLIER & CUSTOMER PART II
TOPIC 9: HOW TO CREATE CUSTOMERS ON AN HUB IN MDG 6.1
TOPIC 10: HOW TO CREATE CUSTOMERS ON AN HUB IN MDG 6.1
TOPIC 11: THE NEW DATASET IN MDG 6.1 FOR CUSTOMER DATA
TOPIC 12: THE NEW DATASET IN MDG 6.1 FOR SUPPLIER DATA
TOPIC 13: WHAT’S NEW WITH MASTER DATA GOVERNANCE FOR BUSINESS PARTNER, CUSTOMER, SUPPLIER WITH MDG 6.1
TOPIC 14: PROCESS OVERVIEW OF MDG FOR SUPPLIER & CUSTOMER

Advantages of using SAP MDG:

  • SAP MDG Training manages data quality management.
  • Real time Analysis.
  • Validating rules and postal reference data.
  • SAP MDG Training architecture is based on the data service integration with SAP MDG, SAP ERP and SAP CRM.
  • Strong functional expertise in MM and thorough expertise in material master data.
  • Configure SAP MDG in accordance to the detailed document and business requirements.
  • Validate technical design documents and perform unit testing of development.

SAP MDG Online Training Course Curriculum:

Establish a Master Data Governance Team and Operating Model

  • Recognize that most business processes cross function boundaries. Multiple individuals are involved in the collaborative workflow processes for creating master data as well as executing the business processes.
  • But because organizations are typically organized around function, a question arises as to who owns cross-functional master data processes? To ensure that the processes are not impeded by data errors or process failures, the first best practice that the executive management mandate the creation of a central data governance team.

Identify and Map the Production and Consumption of Master Data

  • By virtue of the process of sharing data managed within a master data repository, we are able to benefit from reuse of the data even though it may only be stored and managed from a single central location.
  • The quality and usability characteristics of the master data must be suitable for all of the multiple purposes.
  • This means that you will need to ensure that cross-functional requirements are identified from all master data consumers and that those requirements can be observed throughout the data lifecycles associated with the cross-functional processes.

Govern Shared Metadata: Concepts, Data Elements, Reference Data, and Rules

  • One of the main drivers for SAP MDG 8.0 Training is discrepancies and inconsistencies associated with shared data use, particularly in terms of the characteristics and attributes of master data concepts such as customer or product.
  • And incredible as it may seem, inconsistencies in reference data sets seem to be rampant.
  • The duplication and overlap of user-defined code sets causes particular problems within an ERP environment when there is no centralized authority to define and manage standards for reference data.
  • Incorrect use of overlapping codes can lead to incorrect behavior within hard-coded applications; these bugs are not only insidious, they can be extremely hard to track down.
  • To reduce the potential negative impacts of inconsistencies, our third best practice for SAP MDG 8.0 Training is centralizing the oversight and management of shared metadata, focusing on entity concepts reference data and corresponding code sets, along with the data quality standards and rules used for validation at the different touch points in the data lifecycle.
  • Because each business function may have its own understanding of what is implied by the terms used to refer to master data concepts, use a collaborative process to compare definitions and resolve differences to provide a standard set of master concept definitions.
  • Governance and management of shared metadata involves the definition and observation of data policies for resolving variance across similarly-named data elements along with the procedures for normalizing reference data code sets, values, and associated mappings.
  • Normalizing shared master reference data can alleviate a significant amount of pain in failed processes and repeated reconciliations that must be done when the reporting does not match the operational systems.
  • Lastly, if we are creating a collected set of data quality requirements, the data quality rules that are used for validation can be centralized and governed to ensure consistency in the application of those rules for inspection, monitoring, and notification of errors or invalid data.

Institute Policies for Ensuring Quality at Stages of the Data Lifecycle

  • The fundamental value of instituting master data management is the reduction in data variance, inconsistency, and incompleteness.
  • Cleansing or correcting data downstream is a reactive measure, and as long as any corrections are shared among all the data creators and consumers, this may be an acceptable last resort.
  • But realize that these types of data issues are just symptomatic of the absence of data quality control processes intended to prevent errors from occurring in the first place.

Implement Discrete Approval and Separation of Duties Workflow

  • By virtue of the fact that cross-functional processes 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 the business team members who define the workflow process and the IT teams who develop and implement the applications of the workflow processes.
  • Another facet is that there are different types of processes: some that require complex IT solutions, and others that can be easily deployed by business people without IT involvement.
  • A third facet involves the operational aspects of oversight of the decisions that are necessary within process workflows, such as reviewing newly entered items, approving the new records, or signing off on the completion of a workflow, to name a few.
  • 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.
  • This leads to our fifth best practice of integrating discrete approval of tasks into the operational aspects of SAP MDG Training.
  • 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.