Messy operational data emerges as a game-changer in supply chain innovation

The Power of Messy Data: Why Operational Complexity Is the Key to Supply Chain Innovation

In a provocative assertion that challenges conventional wisdom, Sheldon Mydat, CEO of Suppeco, argues that the prevailing focus on “clean data” in procurement and supply chain operations is misguided. Instead, he emphasises that the real opportunity lies in understanding and leveraging what he calls “messy operational data”, the unstructured, semi-structured, and o...

For years, organisations have fixated on refining their data quality, striving for immaculate datasets they believed were prerequisites for effective artificial intelligence (AI) and machine learning applications. Mydat contends that this obsession misses the bigger picture. According to his analysis, a staggering 90% of company data is operational in nature, encompassing day-to-day transactions, logistical movements, and real-time workforce activities. The pristine 10% of data that is “clean” is often a marginal quantity; the maths, he says, simply doesn’t add up.

The Myth of Clean Data

The real world of supply chain and procurement operations is inherently noisy. Data generated in these environments is often unstructured, spread across multiple systems, embedded within unstandardised formats, and deeply contextualised by specific organisational workflows. To dismiss this “messy” data as worthless is a fundamental mistake, Mydat argues. Instead, the key to value extraction lies in the context-rich information that resides within this chaos.

AI’s Evolving Role

Two years ago, Mydat admits he wouldn’t have been so optimistic about AI’s potential in operational settings. Today, advancements in large language models (LLMs) and natural language interfaces are transforming the landscape. Modern AI tools are now capable of extracting insights, and recognising patterns that previously remained hidden and inaccessible.

Specifically, this technology enables organisations to interpret vast quantities of operational data that are inherently messy. It turns the traditional blind spot of “dirty” data into a goldmine of intelligence, providing real-time foresight and decision-making capabilities that surpass the limitations of curated datasets.

Suppeco’s Approach: Working with Reality, Not Idealised Data

At the heart of Suppeco’s philosophy is a refusal to wait for “perfect” datasets, those pristine, sanitised collections that rarely materialise. Instead, their SuppEQ platform operates at scale within the real, often unstructured world of operational data. By extracting actionable insights from the chaos, Suppeco offers organisations a competitive advantage: understanding what is truly happening on the ground, in real-time.

This approach reflects a fundamental truth: successful business operations hinge on actual, real-world data, not idealised or cleaned versions. Embracing messiness and leveraging AI to interpret it is emerging as a pivotal strategy for supply chain resilience and procurement agility.

Conclusion: Embrace the Mess to Unlock Hidden Value

The message is clear: organisations that cling to the myth of clean, perfect data risk missing the true potential of their operations. In an era characterised by rapid change, disruption, and complexity, the real treasure lies beneath the messy surface, hidden in the context, scattered across systems, and waiting to be uncovered by sophisticated AI.

As Sheldon Mydat succinctly puts it, “See for yourself”, the real game changer is understanding and exploiting the reality of operational data, messy though it may be. In the future of supply chains and procurement, this mindset shift could be the decisive factor that separates the leaders from the laggards.