Manufacturers have always struggled to balance cost optimization with robust service quality for manufacturing assets. Increasingly, they are turning to data-driven solutions to advance asset efficiency and reliability. These solutions also help to provision asset data for regulatory compliance, health and safety, and integration with other service providers.

To achieve optimal data-driven outcomes, manufacturers will need adaptive data governance. Adaptive data governance is an approach to data governance that focuses on flexibility and responsiveness to changing business needs and data landscapes. It recognizes that data governance is not a one-size-fits-all approach. Business requirements, technology, and data sources are constantly evolving. An adaptive data governance framework allows companies to rapidly integrate new data sources and modify their definitions and policies. Employees can leverage an adaptive governance framework to address emerging risks and compliance requirements while enabling proactive and collaborative decision-making.

GenAI can accelerate the ROI of adaptive data governance, particularly as all teams within the organization, not just data teams, adopt a data-driven mindset. GenAI analysis and recommendations create a high-value feedback loop between humans and analytics that improves the underlying data ecosystem. GenAI can identify data gaps/breakages and surface opportunities for improvement across different departments and locations, and enable all users to seamlessly harness the value of integrated asset data for decisions and asset triaging.

Applying GenAI to Data Governance

GenAI automates data discovery, profiling, labeling, and annotation, providing a contextual data quality engine powered by distributed processing engines for speed and scale. GenAI doesn’t just analyze data—it creates a feedback loop for employees to improve the data ecosystem. This processing power delivers advanced analytical insights to business users and data scientists. 

Companies should begin by addressing the real-time challenges of siloed asset data operations. Teams can leverage GenAI to define the parameters of adaptive data governance and use the technology to add adaptive capabilities and lineage tracking to ensure the fidelity of the asset data, giving companies the reproducibility and traceability needed to audit GenAI output. GenAI-assisted adaptive data governance leverages ML and AI to find critical asset data across structured and unstructured sources. It onboards new data automatically with oversight and control and automatically tags data with business context to help users assess relevance. Using company-defined parameters, it produces recommendations on downtime triggers and equipment failures, provides visibility to underperforming equipment, addresses safety/environmental incidents, and gives teams valuable analytical insights for various functioning assets to aid proactive maintenance.

Using GenAI-infused adaptive data governance, businesses can automate and streamline asset data, asset cataloging, data provisioning, data literacy, data control, and data quality.

Leveraging a Machine Learning Data Catalog

The core of a GenAI-driven adaptive data governance program is a machine learning data catalog (MLDC). MLDCs advance asset health by exploiting machine learning to improve data management, governance, and consumption. Every data catalog function learns from data patterns, user queries, data searches, and annotations. A MLDC delivers:

  • Automated Asset Data Discovery: Data discovery and search are crucial catalog components. MLDCs learn from human behavior to serve the most relevant asset search results, increasing efficiency. MLDCs leverage AI to learn from user interactions, reducing turnaround time and helping users get more accurate and relevant asset data faster.
  • Simplified Data Accessibility: As the asset data volume grows, manual data catalog tagging methods can no longer keep pace. MLDCs can detect suboptimal human interventions and alter data stewards. This process is transparent, and asset health is triaged and maintained. This automation allows asset data to be more accessible without compromising governance, creating a system of data democratization
  • Quality Asset Data: The MLDC tracks data consumption and information used to automate the discovery process. Automation entails rooting out duplicates and irrelevant data, standardizing data semantics, and prioritizing the data to meet the searcher’s needs. In this way, the data catalog continuously refines its ability to provide relevant data to the right person, evidenced by features like real-time quality alerts to the analysts as they work.
  • Scaled Data Intelligence: Once asset data is centralized, an intelligent catalog can collect information about internal asset data usage. This process allows the catalog to learn from human interaction and improve governance functions: data management, search, discovery, and quality. An MLDC boasts a bird’s eye view of all asset data across the manufacturing enterprise. If automation is effective in one data set, the MLDC can find other relevant datasets and apply the automation, adding further benefits to the target catalog.

GenAI Helps Companies Becoming Data-Driven

With the growth of sensors and smart assets, manufacturers face astronomical asset data growth. The challenge for the industry is implementing data governance that helps them manage their data efficiently and responsibly to drive better decision-making. However, data governance is not a one solution approach. Adaptive data governance infused with GenAI helps organizations respond quickly to changing needs, regulations, and goals. It creates a high-value feedback loop between humans and analytics that addresses unique business requirements as data sources constantly evolve.

About the Author

Sayantan Banerjee
Cluster Delivery Head, Data Analytics and Intelligence, Wipro Limited