ML Journal

Unlocking value from data offers manufacturers new opportunities in a shifting economic landscape. 

 

TAKEAWAYS:
● To improve supply chain visibility and overall operations, manufacturers must be intentional in how they harness and use all their data sources.
● Business strategy alignment, data literacy, and the right technology platforms are key to developing a data strategy that supports data governance.
● Manufacturers need a robust data foundation in order to use AI effectively.  

 

 

From raw material sourcing to product delivery, virtually every manufacturing process generates vast amounts of data. Yet for many organizations, these data remain siloed, underused, or poorly governed, hindering their ability to make informed decisions and fully leverage artificial intelligence (AI) and Internet of Things (IoT)-enabled devices.

Economic uncertainty and a shifting trade landscape make tapping into available data even more critical for driving better decision-making. In multi-layered manufacturing supply networks, organizations that harness information intentionally can use both their internal operational data and data generated from vendors, customers, and other partners to improve supply chain visibility and overall operations.

To seize this opportunity, manufacturers must embrace a clear data strategy that empowers a robust data governance framework. This approach ensures data are trustworthy and accessible, positions organizations to use data as a true strategic asset, and allows them to leverage more advanced technologies such as machine learning, automation, and AI.

Four Pillars of Data Governance

A successful data governance framework is based on four foundational pillars: trusting data, making data available, ensuring data compliance, and understanding the data (see Figure 1). These pillars help organizations cultivate a mindset to operationalize and analyze information at scale.

1. Trusting data: Data quality is the bedrock. Manufacturers must be confident that the data they use for supply chain optimization, process improvements, or customer insights is accurate, complete, and reliable. Without trust in data, decision-making becomes risky, and outcomes are more unpredictable.
2. Making data available: Accessibility is critical. Valuable data that sit in isolated systems or are confined to one department cannot drive enterprise-wide value. Governed data must be accessible to those who need them, when they need them, while maintaining appropriate access controls.
3. Ensuring data compliance: Regulatory requirements, both local and international, are growing in complexity. Manufacturers operate across borders, interacting with myriad vendors and customers. Data governance must ensure compliance with policies, processes, and ever-evolving regulations, protecting both the organization and its stakeholders.
4. Understanding the data: Data are only as useful as they are understandable. Clear definitions, standardized formats, and robust metadata enable teams throughout the organization to interpret and apply data appropriately, fueling both operational and analytical initiatives. Organizations also need to understand the systems from which their data originate and the overall lineage of that data. For instance, were there any areas where data may have changed as various partners within the supply chain transmitted that data? Teams need to understand all points along that data journey.

Figure 1: Data governance pillars

The Importance of a Clear Data Strategy

Data governance frameworks cannot exist in isolation or as reactive, one-off projects. Instead, they must be embedded as a program through a deliberate, forward-looking data strategy that aligns with organizational goals and evolving market landscapes. A clear data strategy provides the vision, structure, and prioritization necessary to make governance actionable and effective.

For manufacturers, the need for such a strategy is acute. The industry is characterized by complex supply chains, a diverse range of vendors and customers, and rapidly advancing technologies such as enterprise resource planning systems and fleets of IoT-enabled machinery. Every innovation and new data source increases both the challenge and the potential reward of effective data governance.

A robust data strategy helps determine which data are most important and why. This allows manufacturers to contextualize governance framework needs and focus resources on areas that drive the most value. A clear data strategy defines the organization’s roadmap for the next several years and should be closely tied with business goals and broader business strategy. The data strategy and other digital transformation initiatives within the company should also be intentionally aligned and inform each other on an ongoing basis.

Data as a Strategic Asset

Treating data as an asset can shift the organizational mindset from passive record-keeping to active value creation. In this context, data become a foundational resource akin to capital, talent, and technology.

This perspective can have profound implications. The rise of IoT devices in manufacturing plants, for instance, provides companies with new sources of data that can yield insights into operations. When governed and analyzed effectively, that data can unlock new efficiencies, reveal actionable insights, and enable predictive maintenance or smart product innovation. The difference between success and failure rests on whether manufacturers have the right frameworks to trust, access, and interpret their data.

A strong data foundation is also key for companies to implement AI effectively. “Garbage in, garbage out” is especially true here: without clean, well-governed data, even the most sophisticated AI tools will struggle to deliver meaningful results.

Three Key Data Strategy Considerations

Building a data strategy that supports effective governance requires manufacturers to address three critical considerations: align business strategy with long-term goals, cultivate data literacy and assign roles, and select and enable the right technology platforms.

1. Align business strategy and long-term goals: A data strategy must flow from a clear understanding of organizational objectives. Consider these questions: Where is the business headed in the next one, three, or five years? What are the focus areas for growth, efficiency, or innovation? The answers to these questions should inform which data products are prioritized, how they are governed, and how value is measured.
   If supply chain visibility is a strategic priority, for instance, then data governance efforts should focus on ensuring the quality, availability, and compliance of supply chain-related information.
2. Cultivate data literacy and assign roles: Data governance initiatives can only succeed if teams are empowered and accountable in their use of data. This means investing in data literacy across the organization and ensuring that the right roles and responsibilities exist to execute the strategy.
   From data stewards responsible for quality and compliance to analysts and business users interpreting insights, everyone must understand both the importance of data and how to use that data effectively. Training, clear communication, and a culture of curiosity and improvement are essential as new technologies and data sources enter the manufacturing environment. Having a change management plan in place can help with these efforts and overall adoption.
3. Select and enable the right technology platforms: Technology is the enabler that binds data strategy and governance together. The first step in governance is not governance itself but building toward a centralized source of truth with a modern data platform that supports integration, automation, and scalability while iteratively applying data governance processes and accountability along the way.

Manufacturers should invest in platforms that not only collect and store data but also facilitate sharing, security, and advanced analytics. Centralizing data reduces duplication of effort, minimizes errors, and ensures consistency in how governance policies are applied. Platforms must also be flexible and ready to accommodate new data streams from IoT devices, AI tools, and other digital transformation initiatives. The focus here should be on applying governance as the organization grows, as centralized data allow for better visibility and control of that data.

The Takeaway

For manufacturers, the journey from data chaos to data value starts with a clear, actionable strategy. By focusing on the four pillars of governance—trust, availability, compliance, and understanding—and embedding them into a strategy aligned with business goals, manufacturers can unlock their data’s potential.  M

About the authors:

 

Ravi Bodla is a principal at RSM US LLP.

 

 

 

Liz Rizzi is a manager at RSM US LLP.