“You don’t rise to the level of your technology—you fall to the level of your data.”
– Cassie Kozyrkov, Chief Decision Scientist at Google
Back in 2022, when McKinsey predicted that the most successful enterprises by 2025 would be those that evolved their understanding of data, companies didn’t anticipate the quick shift in viewing data as a by-product of operations to treating it as a product in its own right. Today, global supply chains are more interconnected—and more vulnerable; industries are generating more data than they know what to do with, but sheer volume no longer equates to value. The data storage market alone is forecasted to grow by nearly 18% annually, reaching $778 billion by 2030 but supply chains are yet to overcome the tag of being built around static reports, siloed systems, and reactive decision-making
In this in-depth exploration, we look beyond the outdated silos and fragmented systems of the past. We’ll examine why traditional data strategies have failed to meet today’s supply chain demands, how data products offer a scalable, resilient alternative, and what a modern supply chain data product ecosystem looks like. Finally, we’ll explore how forward-thinking organizations are already putting this new paradigm into practice and reshaping the future in the process
A Decade of Data… but Where’s the Visibility?
For years, organizations were told the path to clarity was centralization—bring together every ERP, data lake, and master dataset, and you’ll unlock a “single source of truth.” That promise is rarely delivered. According to Gartner, only 23% of supply chain leaders feel they have real end-to-end visibility. What we’ve built, in many cases, are elegant silos—well-governed, but fundamentally disconnected from the rhythms of the business. Data moved slowly through bureaucratic layers. Insights often arrived too late to influence operational decisions. Desperate for agility, business users created their own local solutions, leading to the proliferation of “shadow IT” systems, conflicting KPIs, and fragmented realities
The problem wasn’t a lack of data. It was how data was framed—as a technical artifact, not a business asset. IT owned it, governed it, and measured success by how many dashboards were delivered. But business teams needed more than visibility—they needed insight, in context, and on time. When that failed, they built their own workarounds, creating shadow systems, Excel empires, and parallel truths. And that’s where trust in data eroded.
A Shift in Philosophy: Data as a Product
Imagine instead of handing over a set of shipment logs, you deliver a product that tracks container delays, predicts impact on service levels, and offers rerouting suggestions—all embedded within the planner’s workflow. That’s the leap: from passive information to active decision support.
Data products are built with the end user in mind. They have a defined lifecycle, evolve with business needs, and are continuously monitored for performance. This model ensures that data is not only accessible, but trustworthy, timely, and actionable. The future isn’t about dumping more data into better lakes. It’s about designing data with intent—as a living asset, not just a technical artifact.
Domains That Make Sense to the Business
Traditional data teams organized themselves around systems - SAP, Snowflake, or Hadoop. But a planner doesn’t think in systems; they think in problems. What’s driving my forecast error? Where is inventory accumulating? When will that shipment arrive?
A data-product-led model reframes domains in business terms—Planning, Logistics, Finance, Sales—and builds products aligned to real needs. Think of a Demand Sensing product, or an Inventory Health Scorecard. These are tools designed to solve specific problems, with context that the user recognizes and trusts. The result is adoption. Not because users were trained, but because the product makes their job easier.
Why This Matters More Than Ever
We’re living through an era of cascading disruptions—port closures, regulatory shifts, climate volatility and geopolitical instability. Traditional reporting lags. What’s needed is responsiveness, scenario agility, and operational foresight. That only happens when data is embedded, not just visualized.
The beauty of data products is their composability. They can be snapped together to support new decisions without rebuilding the system from scratch. A network optimization use case might combine demand forecasts, supplier capacity, and logistics risk—each product, maintained and trusted, stitched together in near real-time.
What a Supply Chain Data Product Ecosystem Entails
A mature supply chain data product ecosystem typically includes:
- Master Data Products: Clean, trusted entities like Supplier Master, SKU Master, and Customer Master that serve as foundational building blocks. These act as the single source of truth across systems and functions.
- Operational Data Products: Real-time visibility of assets tracking inventory positions, in-transit shipments, production schedules, and supplier performance. These products power day-to-day decisions at the execution level.
- Predictive Data Products: Forecasting engines that anticipate demand, lead times, supply risks, and disruptions. These products help teams shift from reactive firefighting to proactive planning.
- Optimization Data Products: Scenario planning tools for sourcing, production planning, capacity balancing, network design, and transportation routing. These are used for strategic and tactical decisions.
Importantly, leading companies are redefining how data domains are structured, organizing them not by abstract technical categories, but by real business teams. Rather than siloed models like “order” or “transaction,” data products are increasingly aligned to how the organization functions—by Finance, Logistics, Planning, Sales, and Customer Service. This shift enhances ownership, agility, and relevance.
For example:
- The Planning team may own a suite of products like “Demand Sensing,” “Inventory Health Score,” and “Capacity Forecast.”
- The Finance team may manage data products such as “Landed Cost Calculator,” “Working Capital Tracker,” or “Forecast Variance Analysis.”
- The logistics team could be responsible for “Shipment ETA Prediction,” “Carrier Scorecard,” or “Dock Capacity Optimizer.”
- The Sales team may drive “Revenue Recovery Watchlists,” “Customer Fill Rate Dashboards,” and “Lost Order Root Cause Analytics.”
Each of these products is independently owned but designed to interoperate through standard APIs and Metadata catalogs. Governance, compliance, lineage, and security are embedded at the product level—not bolted on as an afterthought. It’s not about solving one problem. It’s about creating a fabric of intelligence that can scale, flex, and evolve.
This shift is as much about culture as it is about architecture. Data teams can no longer be off to the side; they must sit inside the business—planners, engineers, analysts, and product managers co-creating together.
Products have owners, roadmaps, and SLAs. They are versioned, improved, sometimes even retired. Compliance, lineage, and governance aren’t separate workstreams—they’re embedded. This is the maturity curve: from a reactive, report-driven world to one where data behaves like software and delivers like product.
What’s Next: A Smarter, More Adaptive Future
We’re moving toward a world where data pipelines reconfigure themselves based on risk, where AI copilots answer complex planning questions in plain language, and where ecosystems respond dynamically to changes in weather, demand, or global policy.
This isn’t about replacing people; it’s about giving them tools that move at the speed of their decisions. That’s what data products promise. Not just more data, but better data. Useful, timely, embedded.
Agentic AI: Elevating Data Quality to an Autonomous Discipline
As organizations embrace data products, a new frontier emerges: agentic AI—the rise of autonomous systems that proactively manage data quality without human intervention.
Historically, data quality management has been reactive and manual. Data stewards created rules, monitored exceptions, and initiated investigations when anomalies arose. This model struggles to scale in real-time, high-velocity environments like modern supply chains.
Agentic AI changes the game fundamentally. These systems act autonomously across four critical dimensions:
- Continuous Monitoring: Scanning incoming data streams in real time to detect anomalies such as missing shipment updates, unexpected inventory levels, or outlier lead times.
- Intelligent Diagnosis: Distinguishing between systemic errors (e.g., integration failures, supplier data corruption) versus isolated noise, and classifying issues based on severity and impact.
- Auto-Remediation: Automatically initiating corrective actions where feasible—such as imputing missing values using predictive models, pulling reference data from external sources, or suggesting targeted manual interventions.
- Learning Over Time: Using machine learning to continually improve detection sensitivity, minimize false positives, and optimize monitoring configurations based on observed patterns.
Consider a Transportation Visibility Data Product enhanced by agentic AI. It could detect missing GPS signals from trucks in real time, assess the risk of late delivery, trigger alternative routing workflows, and even notify customers proactively without manual intervention.
This evolution transforms data quality from a reactive firefighting exercise to a proactive resilience capability—a critical leap as supply chains move toward autonomous execution models.
- Event-driven architectures to process and react to streaming data
- Embedding machine learning models into data pipelines
- Integrating with orchestration engines for automated workflows
- Building feedback loops into data products for continuous improvement
Functionally, it demands:
- Redefining quality SLAs in terms of decision impact
- Establishing trust hierarchies between automated vs. manual interventions
- Building explainability mechanisms to ensure business users understand and trust AI-driven actions
Agentic AI doesn’t just elevate data quality. It makes it invisible and seamless, allowing human operators to focus on strategic decisions rather than low-level monitoring.
Reframing the Question and moving from Infrastructure to Intelligence
Cassie Kozyrkov’s quote nails it—we don’t rise to our tools; we fall to our foundations. And if our foundation is fragmented, stale, or hard to trust, no amount of AI or cloud investment will fix it.
The organizations winning today aren’t those with the biggest platforms. They’re the ones treating data as a product, building trust, reducing time to insight, and empowering business teams to act with clarity. The shift is underway. True competitive advantage comes from building the operating model to turn that technology into sustained value.
Supply chain data products represent the next leap forward:
- From fragmented reports to contextual decision aids
- From passive dashboards to proactive intelligence
- From siloed systems to smart, self-optimizing ecosystems
The journey demands leadership, investment, and resilience. Organizations must unlearn old habits and embrace data as a dynamic, living product. For those who act decisively, the rewards are transformational: faster decisions, greater resilience, happier customers, and smarter, more agile supply chains.
The future of the supply chain isn't just digital. It’s intelligent, agentic and above all, product-driven.
AUTHOR - FOLLOW
Madhumita Banerjee
Associate Manager,<br>Supply Chain
