Automation in Master Data Governance is a critical foundation to ensure data integrity and to maintain a single source of truth for the organization. Organizational frustration can creep in with poor quality master data management, yet only 36% of respondents have MDM governance and policies in a SAPInsider
research on Data Management and Data Warehousing in the Cloud.
Further, data quality has a direct impact on how businesses can adopt technologies such as automation, artificial intelligence (AI), and machine learning (ML). Matthew Phu, CEO of SimpleMDG, a master data governance vendor, says, “Data quality means cost savings, business growth and, ultimately, profitability of the company. Further, there's a lot of key innovation that are emerging related to artificial intelligence (AI), machine learning (ML), etc. However, for those technology innovations to create impact, companies need strong data quality. And that data quality starts with proper data governance.”
Simplistically, SAP master data spans across four domains—materials, vendors, customers, and finance. But it is the attention to granular details within these four domains that makes master data valuable and key to avoiding process errors and help the business be nimble.
SAP Master Data Governance (SAP MDG) solution includes standard, domain-specific solutions for common areas like materials, articles, customers, vendors, and technical assets. It is possible to extend SAP MDG to manage other master data. Several non-SAP solutions also provide such standard and extensible solutions.
Automation Use Cases for Master Data Governance
Automation comes into play in many areas in master data governance
- Master data creation – Intelligent automation can cascade creation of domain objects based on certain parameters in a process like the predictive form-filling we see in forms today.
- Automated Workflows - Rules-based notification and hand-offs for master data approvals can easily be configured to support any business scenarios
- Automated Mass Data Validation and Migration–With the move to SAP HANA and to the cloud, we can expect large volumes of data to be moved to newer storage. Data tiering strategies are also causing data to be housed in hybrid locations. Automation can help categorize data, migrate it to specific sources, and validate it post-migration.
- Further, more sophisticated models for bigger workflows or projects can be automated that are customized for a business use case. For example, a new product launch will need master data across all domains such as materials, finance, vendors and even customers. Automation can set up workflows to collect all requisite master data into one seamless view. We can also set it up to validate both basic master data governance rules and specific business rules for a particular product. We can easily improve such an approach as the business grows and adapts to market changes.
Several vendors have creative solutions that provide above automation, either with SAP MDG or independently.
What Does This Mean for SAPinsiders?
For SAPinsiders in any stage of digital transformation or migration to SAP HANA or to other cloud databases, automating master data governance can provide significant value. These are some ways to get started:
- Include automation as an area of evaluation within master data strategy.
- Identify use cases for automation with master data governance that provide impact and reduce downstream risk
- Evaluate vendor solutions that provide specialized automation functionality for your industry. Also consider if integration with SAP MDG is critical for such automation.
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