Describing Key Capabilities

Objective

After completing this lesson, you will be able to describe the key data management capabilities

Central Data Governance

The primary goal of SAP Master Data Governance is to ensure that master data is clean based on its consolidation, central governance, and data quality management capabilities. This video introduces the key capabilities and the different Master Data Governance solutions provided by SAP for this purpose.

SAP Master Data Governance, Central Governance allows domain-specific enterprise master data management to centrally govern the creation, change, and distribution of master data across the entire enterprise system landscape. This proactive approach ensures that master data complies with company standards when activated. This way, organizations can ensure flawless business processes and trustworthy analytics right from the beginning.

Central Governance enables you to:

  • Provide central ownership of master data in-line with a company’s business rules and processes

  • Work with out-of-the-box master domains or use the built-in framework to create customized master data domains

  • Put change request-based processing of master data in place in your enterprise with integrated workflows, approval, activation, and distribution

  • Deploy MDG as a separate hub system, or co-deploy it with SAP S/4HANA. In both cases, MDG can use SAP and company-specific business logic to create master data ready to be used in a company’s business processes

The following video explains what a typical SAP Master Data Governance Process Flow looks like.

Central Governance for SAP Master Data Governance on SAP S/4HANA and SAP S/4HANA Cloud Private Edition, master data governance, supports the following domains:

  • Business Partner including Supplier and Customer
  • Material Master
  • Financial
  • Custom Objects

Central Governance - What Does It Look Like?

The following figure Central Governance: Creating a new Business Partner shows the welcome screen when creating a new business partner in single object processing.

Create a new business partner in the single object processing.

Central Governance provides:

  • User interface for manual data entry to complete fields
  • Duplicate checks and validations during the creation process
  • Option to retrieve reference data from external sources for automated population of fields (Look Up and Enrich)

Master Data Consolidation

Master Data Consolidation is an effective master data management approach to identify duplicates and determine the single source of truth for the customer, supplier, and product data not maintained centrally.

SAP Master Data Governance, Consolidation delivers capabilities to load master data from different sources, standardize the master data, and identify duplicates. For each of the determined match groups, it calculates the best record from the duplicates in that group, based on the survivorship rules for the master data attributes. You can then use these best records in dedicated analytical or business scenarios.

Within MDG, you can combine consolidation and central governance to support various master data management scenarios. Typical scenarios include the initial load of master data as a starting point for central governance, consolidation of master data after mergers and acquisitions, or combinations where you maintain decentralized ownership of master data in some parts of the company and centralized master data ownership in other parts.

The figure below depicts the domains and processes supported by SAP Master Data Governance, Consolidations process.

Given here are the different domains and processes supported by the Consolidations process. Processes include, Order to Cash, Procure to Pay, Design to Order, Service and Maintain, and Acquire to Retire. Domains supported are - Customers, Finance, Vendors, Materials, Products, Assets, and Location

All Use Cases and Implementation Styles

  • Continuous consolidation for analytical purposes
  • Consolidation for initial load before central governance
  • Consolidation for Merger and Acquisitions
  • Central governance only
  • Coexistence hybrid approach

Domain Coverages for Consolidation Process

SAP Master Data Governance, Consolidation supports the following domains:

  • Business Partner, including customer and vendor
  • Business Partner Relationship
  • Material Master
  • Custom Objects

The figure SAP Master Data Governance, Consolidation Process Flow illustrates the process flow in master data consolidation of source records.

The order of the process steps and the behavior of each process step type can be adapted to your requirements.

A consolidation process consists of several process steps. The order of the process step is defined by the process template you select when you create the process. A typical consolidation process consists at least of the process steps listed below in the displayed order: Standardization, Matching, Best Record Calculation, Validation, and Activation.
Data Load
SAP Master Data Governance supports different file formats to upload source records
Initial Check
View loaded data and check data quality based on backend customizing or own Business Rules Framework plus (BRFplus) rules.
BRFplus provides a comprehensive API and user interface for defining and processing business rules. It allows you to model rules intuitively and reuse these rules in different applications.
Standardize
Validate and enrich data (for example, address validation)
Match
Find duplicates based on matching rules and review the result
Calculate Best Record
Create best records based on approved match group and review the calculation results
Validate
Validate the best records against backend customizing and Central Governance checks
Activate
Activate consolidated master data for analytical or operational use

Master Data Consolidation - What Does It Look Like?

The figure Master Data Consolidation - Best Record Calculation is a system view of the Consolidation process that shows the step Best Record Calculation in the process. You can drill down into user interface details to review data at the table and field level.

This figure shows the step Best Record Calculation of the consolidation process. You can drill down into details UI to review data on table and field level.

Data Quality Management

SAP Master Data Governance, Data Quality Management is a business user application that allows to manage master data quality by defining and applying quality validation rules across all points of master data entry. It also features data quality monitoring and seamless remediation.

Data Quality Management is responsible for maintaining the quality of your product and business partner master data.

With Data Quality Management, you can:

  • Define derivation scenarios to deduce master data based on derivation rules
  • Define validation rules and data quality Key Performance Indicators (KPIs)
  • Evaluate the quality of your master data according to these rules and monitor the current state of the data quality and its trend

Data Quality Management Process

The figure Data Quality Management Process shows the process steps for Data Quality Management in SAP Master Data Governance.

The process for data evaluation involves multiple apps with some optional steps. You need to create and execute data quality evaluation runs to check your data quality based on your predefined validation rules, and then you use the evaluation apps to display data quality evaluation results and navigate to additional apps to resolve data quality issues. The Data Quality Score and Data Quality Evaluation Overview apps enable you to monitor your data quality with meaningful KPIs. Optionally, you can also configure your data quality score display to better monitor your data quality. To use data quality evaluation apps, you should have created and approved validation rules in your system. You can create these rules manually or use the Rule Mining apps to assist you in the process.
Define Derivation Scenarios

You can use derivation scenarios to deduce master data based on rules and a defined scope. Derivation scenarios are used during master data processes to ensure data quality, for example, in mass processing, consolidation, and change requests.

These scenarios are provided to combine derivation rules into executable and manageable units. A derivation scenario consists of one or more derivation rules, which control how target values are determined from source values.

Define Validation Rules

You can define validation rules that can be used for data quality evaluations and for checking data in change requests, consolidation, and mass processing for products and business partners. The rules can be implemented with Business Rule Framework plus (BRFplus).

Perform Rule Mining

The purpose of the rule-mining process is to discover new validation rules based on the selected criteria. The process uses machine learning to choose potential rules for your evaluation.

Define Data Quality KPIs

Define data quality dimensions and dimension categories with assigned validation rules, and specify how scores are calculated to describe KPIs for data quality monitoring.

Evaluate Data Quality

Create and run an evaluation of your master data according to the validation rules. You can also schedule evaluations, for example in weekly intervals.

Monitor Data Quality

Display up-to-date status information about current data quality and drill down into the details for further analysis. You can also display the current score, its trend, and the comparison with the defined thresholds.

Analyze and Correct Data

You can analyze data quality evaluation results for products and business partners to find incorrect data, investigate potential reasons for incorrect data, and start the correction.

Data Quality Management - What Does It Look Like?

The figure Data Quality Evaluation Overview for Business Partners is a system view of the Data Quality Management process for a Business Partner.

This graphic shows a system view of the Data Quality Management for a Business Partner.

The Data Quality Evaluation Overview for Business Partners application displays up-to-date data quality score information for business partners. You can drill down into the details for further analysis. In the different areas of the screen, you can find the following information:

  • Latest Data Quality Score
  • Data Quality Trend
  • Incorrect Data
  • Latest Data Quality Evaluations

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