Master data governance - Accurate Master Data as an Engine for Innovation

All too often, projects and organizations fail to give master data governance the attention it deserves. In the hype surrounding AI, machine learning and automation visions, the basics are often forgotten - BUT: accurate master data is an essential prerequisite for the implementation of future-oriented innovations. Admittedly, data quality and compliance criteria are one of the biggest challenges in a rapidly growing data jungle. However, by taking a structured approach and embedding master data governance into organizational structures and corporate culture, it is possible to turn the sword of Damocles into an opportunity and develop a data-driven business for an efficient, sustainable future.

Data management and compliance are the core areas of Master Data Governance. The definition of relevant metrics and a consistent strategy for identifying and implementing their improvement potential is an elementary component of the master data governance cycle. In an analysis phase ("get clear"), it is therefore necessary to individually define quality criteria and security-relevant requirements in order to be able to subsequently implement cleansing and automation.

Merging data records into golden records is a difficult task in many organizations, depending on the information content, previous data quality, and definition of data owners. Undetected duplicates, incomplete records, and invalid information lead to inefficient processes and an accumulation of errors. Data quality is the foundation for a successful future, whether it is S/4HANA transformation, process automation, or the use of AI. Confidence in a solid database also strengthens decision security.

The "get clean" phase ensures that every data entry is checked for validity, timeliness, completeness, and security & compliance. With the right tools, it is possible to perform cleansing, harmonization, and validation.

I want to conclude:

Ideally, an initial cleansing will result in the findings and quality criteria being incorporated into future data generation. In this way, the creation of new data can be improved through defined rules and responsibilities, up to and including full automation. Recurring tasks and checks can be performed by software robots using a combination of human and artificial intelligence, requiring manual intervention only in exceptional cases.

Author
Dr. Julia Alexandra Lindorfer
julia.lindorfer@akquinet.at