Data governance has been at the forefront of business corporate oversight since the dawn of the digital age, and never has the subject been more important than now.
Many issues surrounding data protection and assurance concern inconsistent data management procedures. A unified set of policies to manage data is important, and it must meet regulatory demand.
One such demand, the EU Data Governance Act, is starting now to have wide-ranging impacts, both in Europe and further abroad since its 2022 implementation. Indeed, a recent analysis published by the Digital Society Journal has noted a range of challenges already being posed to American businesses, chiefly in how private data is used and then reused in the future.
With the specific nature of these data regulations, and the wider requirement for businesses to treat data carefully, there is a need and opportunity here to put artificial intelligence (AI) and machine learning (ML) to good use within SAP Master Data Governance (SAP MDG), both in securing data safety and in reducing overheads.
Data privacy concerns are high on the minds of American consumers. According to Forbes, 422 million individuals were impacted by data breaches in 2022. It’s little surprise, then, that 86% of Americans say they don’t trust companies with their data. With so many data breaches, there's a good reason for that. However, using AI and ML can be a demonstrably safe first step in the data governance picture. In doing this, businesses can build and restore trust, a crucial factor in both B2B and consumer relationships.
How is this enabled with an MDG framework? Firstly, using AI rules defined through regtech will enable the business to apply strict data gathering and quality rules. An algorithm with clear instructions, and the space to continue to develop, will create a strict framework around which data can be gathered. The next step, applying machine learning to that data collection, can help the business to establish what it can be doing better with the data and how it can ensure collection efforts react positively towards changes in legislation and standards.
SAP MDG can operate at the absolute core of this framework. At the heart of the SAP MDG program is a central trust view of the business through which to assess and monitor data; this being in place will enable the AI to do its work while providing absolute oversight.
The main use of AI and ML in this field is through automation, and that is replicated in consumer trust. Research conducted by MITRE Corporation shows that 64% of consumers have inherent trust in AI, believing it is there to assist and enhance customer interactions. Harnessing this is crucial. AI and ML can take away human error, or at least the perception of it.
The SAP MDG framework places smart working at the heart of its operation; in particular, master data integration on cloud-based services provides the opportunity to bring together a network of different services, automatically applying data management rules to each.
Moving away from individual administrators unevenly applying data policies and giving that responsibility to a machine learning unit is the key, and will help to show consumers that their data is being collected and handled in an impassive and impartial manner.
Applying AI and ML to data governance makes a lot of sense, but it has to be done with one eye on the specific data risks posed by AI itself. As the Harvard Business Review notes, regulation is on the way for AI, in terms of how it gathers data, how it is operated, and how it is allowed to continue to expand within its ecosystem. AI and ML, after all, are iterative technologies that benefit and indeed work best when they are given the space to roam. That said, there are already numerous ethical questions posed over the nature of AI that need to be addressed.
Companies can manage this risk internally. Developing MDG frameworks in a way that has strict boundaries, but boundaries that are nevertheless defined as broadly as they can be, will help to constrain the activity of the AI and ensure it meets compliance standards. SAP MDG has a clear focus on consistent security and ID management, with the fundamentals underpinning all of the work of the system, focusing on assurance. This will in turn help AI and ML to provide the valuable services it can do for data governance while also hedging against future threats.
Data governance is crucial, and SAP MDG provides a framework around which to develop a structure. Enhancing that is completed through the introduction of AI and ML, which can help to streamline data gathering processes. Consumer reports have shown that your average customer does, in fact, see AI as a force for good, and this can be used effectively within the way the business structures data governance. As with all fields of AI development, there is an inherent risk posed by the advancement of the technology itself. Business owners will need to be vigilant to hedge this risk and ensure AI stays on track.