In the manufacturing space, digital transformation has made data the oil in many machines – and much like oil, it needs refinement and enrichment. This can be carried out via Data Governance. Good Data Governance translates to smarter, quicker, data-driven decisions, as well as more efficient resource management, reduced security risks, greater trust from customers and suppliers, and stronger regulatory compliance for manufacturers. And yet, many players don’t know how to instill internal policies and processes that ensure their data is secure, accurate, and available. 

Despite the rapid uptake of smart data tools, some manufacturers have ineffective monitoring systems and inefficient workflows that result in misused data and missed opportunities. For example, less than 40% of manufacturing executives say that they have successfully scaled data-driven use cases beyond the production process of a single product.

But with artificial intelligence and machine learning becoming commonplace in manufacturing, organizations that don’t have a sound Data Governance structure risk powering their tech with poor-quality data and proliferating poor-quality insights or outcomes. In 2023, Data Governance must be a priority for manufacturers to centralize operations, improve data efficacy, and manage and mitigate business risks. Here’s how.

Applying Data Governance Throughout the Supply Chain

Data Governance should be an open conversation with employees at all stages of the supply chain in manufacturing. Leaders need to communicate what the data priorities and principles of the organization are, as well as why they need to be carried out. This communication should tie back to business goals and provide space for people to ask questions, clarify steps, and propose alternative routes to govern data. 

At the same time, manufacturers should double down on training new employees about Data Governance, to make practices second nature as soon as possible. That means offering informative, easily accessible resources around Data Governance, and building data literacy through workshops, webinars, and mentoring. It’s also worthwhile to embrace techniques like data visualization and encourage feedback from people to actively identify gaps in data flows that the maturing Data Governance strategy can cover.

Within the training, manufacturers need to take care to break down data availability, integrity, quality, security, and usability. They should express how data qualities are retained across different platforms, how data is cleaned, what safeguards are in place for data, and how data is structured and tagged. Likewise, legal requirements around Data Governance should be highlighted so workers recognize the penalties for not practicing good Data Governance. Export Administration Regulations (EAR), Good Manufacturing Processes (GMP), and System and Organization Controls (SOC) Certification are all notable starting points.

Whether a person collecting raw materials, an employee in the factory, or a driver transporting goods, every party in the supply chain should understand what good Data Governance is and how to enact it. 

Data Quantity and Product Quality

Manufacturers can’t approach Data Governance as a blanket strategy; it works best as part of an agile product framework where organizations react to shifting data priorities. That’s why organizations can implement small teams to oversee Data Governance rather than trying to integrate an abstract waterfall approach from the top down. The result is much smarter products. 

These teams are typically composed of data scientists, data experts, and engineers who are responsible for tracking product data movement, quality assurance, and Data Architecture. Their focus is not only data in specific product areas but also the wider data threads throughout the company. The team additionally can review and adhere to the latest industry Data Governance standards by analyzing competitors’ product Data Governance, complying with legal requirements, and reporting on Data Governance (both internally and externally). 

Small assigned teams ensure that there is accountability for Data Governance in product development too. Ultimately all employees are responsible for good data, but decision-making and protocols should be based on the findings of people who are acutely dedicated to data. By working more closely with the data, and having a deeper understanding of its history, teams can determine which tools are most appropriate for product iterations and how to automate product features where possible.

Efficient Data Use Equals Efficient Factories

AI is at the heart of manufacturing factories, and at the heart of AI is data. Factories that practice Data Governance, therefore, ensure that their AI and its outcomes are fully optimized. 

For instance, AI plays a big role in predictive maintenance, flagging hardware that needs to be serviced or even identifying possible breaks ahead of time. With efficient Data Governance, this AI can make more accurate predictions and help companies extend their equipment’s life cycle as well as produce less waste. Similarly, with digital twins – i.e., AI visualizations of factory floors – Data Governance allows for more realistic depictions and hence more realistic repercussions when manufacturers experiment with changing processes or layout.

Not to mention, AI has huge potential to boost sustainability efforts in manufacturing. Particularly in logistics and supply chains, AI is streamlining transport routes, supporting demand forecasting, and facilitating better inventory management. Manufacturers that tie Data Governance into their AI strategy can subsequently see the biggest sustainability returns, including lower carbon emissions and more efficient resource application and allocation.

Like any form of governance, Data Governance requires organizations to set up steps that have real-world advantages. Data Governance is about maintaining the highest standards of data to best serve all stakeholders in manufacturing. With these three tips as a guiding force, manufacturers not only build better products; they also construct a stronger supply chain and more efficient factories.