The challenges of data governance in global supply chains

Antoine Roex, OAKland Group

Faced with the growing complexity of international supply chains, data governance is becoming an essential strategic lever. Between information silos, interoperability and intergenerational issues, companies need to rethink the way they collect, structure and share data to remain competitive on a global scale.

Data governance: a necessity in an interconnected world

The rapid evolution of globalization has profoundly transformed supply chain management. Each link in the chain now produces a colossal amount of data which, if poorly governed, can lead to critical failures. Effective governance requires clear policies on the quality, integrity, accessibility and security of information at every level of the chain. Companies also need to harmonize their information systems with those of their international partners, which are often disparate, to avoid disruptions in flows. Otherwise, errors or duplications can slow down deliveries, increase costs and impact customer satisfaction. This need for rigorous management is accentuated by regulatory pressure, particularly around traceability and compliance. Centralized, agile governance is therefore becoming an indispensable condition for maintaining operational fluidity and meeting global economic and environmental challenges.

The technical challenges of data management in global supply chains

Integrating and structuring data across a multi-continental supply chain is a real technical headache. The systems used range from the most rudimentary to the most advanced, with technological heterogeneity complicating interoperability. Data is not always standardized, and updating it in real time remains a major challenge. What’s more, data security becomes critical when it circulates between multiple entities, often without a common infrastructure or shared cloud. This increases the risk of cyber-attacks and transmission errors. In addition, predictive analysis and artificial intelligence, while promising, struggle to deliver their full potential due to poorly qualified or non-contextualized data. Investing in middleware solutions or data mesh platforms is an innovative way of overcoming these limitations, provided you have a unified vision and a robust data architecture.

Intergenerational learning: an often underestimated strategic lever

At the heart of these technical and organizational challenges lies the human dimension. The transfer of knowledge between generations can offer a definite competitive advantage, especially when it comes to data governance. Young professionals, very comfortable with digital tools, master automation, algorithms and query languages. Conversely, the more experienced generations understand the operational logic of chains, know how to deal with the unexpected and often have a holistic vision of business issues. By fostering a structured dialogue between these profiles, companies can get the best of both worlds: technological innovation on the one hand, logistics expertise on the other. Reverse mentoring programs, communities of practice and intergenerational pairings all help to smooth the flow of skills. This human capital becomes essential to evolve governance standards, make data more intelligent and align tools with realities on the ground.

From raw data to value: how to create integrated management

Data is worthless unless it is translated into useful information, and then into strategic decisions. To achieve this, companies need to establish a data-driven management culture. This means going beyond traditional reporting by integrating dynamic dashboards, adjustable KPIs and clear visualization of flows. Integrated management also relies on collaborative tools, where data is shared in real time between the various internal and external players. The use of tools such as machine learning and predictive modelling not only enables us to anticipate stock shortages, but also to modulate decisions according to the vagaries of the market. However, such intelligent management presupposes that data is reliable, up-to-date and well governed upstream. Only then can supply chains truly become resilient, sustainable and competitive in an uncertain international context.

Conclusion

In a world where supply chains are global, complex and exposed to increasing risks, data governance is becoming a strategic priority. It requires a holistic approach that combines technology, shared standards, cybersecurity, cross-generational collaboration and intelligent steering. Investing in robust governance systems is no longer an option, but a sine qua non for surviving and thriving in today’s global economy.

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