The impact of artificial intelligence (AI) goes beyond just being a popular buzzword. It is a powerful tool to change how organizations think, analyze, decide, and act. Although AI is still in its early application stages, it will expand and encompass all aspects of enterprise operations, including data management and analysis. Data consists of every tenet of information that helps run companies and various functions, from finance to sales to purchasing, planning, and human resources (HR). Master data is a subsection of data that relates to the very nucleus of the organization and is relatively stable. Master data forms the substratum on which enterprise architecture is created. AI is rapidly changing the paradigm and is building the case for how enterprises leverage master data governance (MDG) for supply chain visibility, transparency, and optimization. The integrity and reliability of MDG are influenced by human intelligence and expertise, creating a complementary relationship between AI and humans.
Master Data Governance
According to Gartner, master data is “the consistent and uniform set of identifiers and extended attributes that describes the core entities of the enterprise including customers, prospects, citizens, suppliers, sites, hierarchies and chart of accounts.” Gartner’s definition specifies that master data is consistent and uniform. This can be an exceptionally challenging task for supply chain management (SCM) business processes when data types proliferate, and intricate technology solutions are applied at scale. MDG provides an ongoing set of practices and protocols within master data management (MDM) to establish and maintain the consistency and uniformity of master data through data ownership, role definition, and source of truth. The data integrity from MDM and the monitoring practices established by MDG produce confidence in master data. This trust transfers to faith in transactional data and data-informed decisions. Through reliable business processes based in MDM, MDG opens doors to leverage technology advances, adapt to change, optimize opportunity, and maximize resilience for SCM.
AI and the Supply Chain
SCM research has identified several key AI capabilities in sourcing information and responding to risks in the supply chain, including predictive analytics, forecasting, self-learning, resilience, traceability, and data mining. These AI applications rely on multiple data types from a variety of sources to inform techniques and algorithms like artificial neural networks (ANN), random forests, genetic algorithms (GA), convolutional neural networks (CNNs), and recurrent neural networks (RNNs) that underpin AI SCM solutions. Each of these is affected by data integrity and quality, and the consistency and uniformity of the data directly affect the reliability and usability of outputs from AI and algorithm processes. According to an IEEE conference proceeding, “AI still needs control and regular inputs to run flawlessly as it’s not a self-sufficient system…it’s very rare to generate a usable result from the AI system if the used data are not accurate.”
A 2022 McKinsey industry report found that those with high-quality master data were 1.5 times more likely than peers to report no challenges from 2021 supply chain impacts. McKinsey identifies master data as a key pillar of SCM, yet only 53 percent of top-performing respondents reported achieving high-quality master data, while 74 percent reported they planned to invest in increased IT resources and infrastructure. This ongoing asymmetry of data quality lagging digitization and IT resource expansion outpacing data integrity is why MDG is a critical step toward using AI tools in SCM. MDG introduces protocols and practices to establish and maintain the pillar of consistent and uniform high-quality master data. Without this data integrity, optimization is short-lived for today’s complex supply chains, and organizations cannot take reliable advantage of AI tools. The rate-limiting factor of performance for AI tools is no longer the transmission of data or hardware capacity. Now the challenge is establishing and maintaining data integrity and traceability.
MDG with AI
MDG is best practiced as an ongoing set of protocols, standards, and policies within MDM to continually inform data quality through monitoring data ingestion, master data ownership, and master data publication. Recent empirical evidence presented at the 2024 IEEE/ACM International Conference on AI Engineering (CAIN) summarized emergent data quality issues with AI: “While different tools and practices are available to support feature engineering and data transformation for managing AI pipelines, the need to improve the practices related to quality assurance is continuously increasing.” AI demands consistent data integrity, which necessitates the implementation of MDG processes so that data ownership, ingestion, and publishing are accountable and traceable in the immediate and long term. AI-enabled systems are still developing, with embedded issues such as technical debt, code debt, and data smells taking shape as research advances with industry implementations. According to the same research addressed at CAIN 2024, AI engineering does not yet have a standardized tool for monitoring data quality issues. Given the ongoing development in AI-enabled systems, establishing MDG enables professionals to be prepared to synergistically leverage IT and AI developments while benefiting from data integrity and data quality.
AI with Guardrails
The COVID-19 pandemic brought global attention to supply chain vulnerabilities and areas for improvement. In recent years, AI has illuminated the importance of data integrity, security, and reliability across the supply chain. AI insights may not be accurate or aligned with stakeholder interests without traceability and clear master data ownership. MDG and collaborative data governance frameworks provide protocols to adapt solutions to the contexts at hand, establishing traceability and reliability in AI as an advisor. Using AI tools with MDG guardrails gives the community a mission toward a collective definition of data governance standards, data sharing protocols, and standards for supply chain resilience that benefit all stakeholders and establish a more resilient and responsive global economy.
The Essential Partnership of Technology and Human Intelligence
The potential of AI is massive. The scale and scope of today’s data require strategic approaches, reliable frameworks, standards for collaboration, and protocols for navigating changes. MDG has proven its importance for strategic data-informed decisions. A complementary partnership between AI and human intelligence can be brokered using MDG. The relationship between master data integrity and AI transparency efforts is how AI complements rather than displaces humans. This paves the way for supply chain expertise to act decisively on data-informed insights with confidence. Through data integrity and collaborative governance, SCM professionals can convert today’s challenges into tomorrow’s resilient futures.
About the Author:
Abhishek Chaudhuri is a principal program manager of data governance for a multinational technology company. He has 18 years of experience in data management, data governance, product management, business management, materials management, and supply chain operations. Abhishek is an SAP Certified Associate and holds an MBA in marketing. Connect with him on LinkedIn.
Disclaimer: The author is completely responsible for the content of this article. The opinions expressed are their own and do not represent IEEE’s position nor that of the Computer Society nor its Leadership.
