I have spent years watching supply chains stumble under the weight of their own data. Not because the data is missing, but because it cannot be accessed or understood for effective decision-making. Every warehouse, every carrier, every ERP system, every port terminal hoards its information behind different formats, different standards, and different assumptions about what the data even means. The result is a supply chain that moves physical goods across continents in days but cannot move a single data point across two departments in under a week. That gap between the speed of your shipments and the speed of your information is where margin leaks, disruptions hide, and competitors pull ahead. Data interoperability is how you close that gap.

It is not a single tool or a one-time project. It is a discipline, and in this digital world, data interoperability is a vital competency that every supply chain needs to survive and thrive. I have helped supply chain organizations integrate thousands of data interfaces, and I can tell you there is not just one way to achieve it. In this article I lay out twelve areas you must address to turn fragmented data into actionable visibility with real digital velocity.

1. Data Readiness: Treating Your Data as an Asset to Make Integration Easy

The Supply Chain Data Readiness Problem: How to Stop Operating in the Blind

I’ve spent enough years in the supply chain trenches to recognize a very expensive irony: we pour millions into advanced systems like AI expecting supply chain nirvana, yet we fuel these state-of-the-art engines with fragmented, low-quality data. For example, a freight carrier wanted to transition several of its legacy dispatch systems into one to use a new route optimization application. However, the integration team discovered that these overlapping dispatch systems logged the same lanes using different location codes and inconsistent time formats. The optimizer could not make sense of the data until the carrier standardized its location identifiers and timestamps first. This is one of countless examples of enterprise data treated as an application by-product, not a valuable asset. When your data is ready, integration stops being a fight and starts being a flow.

I believe the only way to stop operating in the blind is to achieve Data Readiness. This is the strategic discipline where businesses actively structure and prioritize critical data so it is immediately accessible, high-quality, and actionable. If you are tired of watching your ROI vanish while your teams struggle to get basic insights from your data, read my article, The Supply Chain Data Readiness Problem: How to Stop Operating in the Blind. In it, I lay out seven guiding principles to help you shift from digital chaos to rapid, informed decision-making, plus a concrete, 5-step data readiness strategy to finally make data integration easy.

“… we pour millions into advanced systems like AI expecting supply chain nirvana, yet we fuel these state-of-the-art engines with fragmented, low-quality data.”

2. Data Integration: Unlocking Your Data Silos

The Best Ways To Access Data – Tech Solutions To Unlock Your Data Silos

Data silos are the default state of most supply chains. Each system was built to solve a specific problem, and sharing its data was secondary. By implementing APIs and other data exchange methods, companies can integrate their supply chain management systems with both internal systems and external partners’ systems, enabling seamless data exchange and synchronization. With data integration, critical supply chain processes are no longer slowed by manual exports, email attachments, or spreadsheet reconciliation. 

For example, an ecommerce company can integrate its order management system with a logistics provider’s system using APIs to automatically update shipment status. Unlocking data silos is less about buying new software and more about refusing to let your data stay trapped. See my article, The Best Ways To Access Data – Tech Solutions To Unlock Your Data Silos, for a breakdown of different data integration types.

“Data silos are the default state of most supply chains. Each system was built to solve a specific problem, and sharing its data was secondary.”

3. Semantic Interoperability: Ending Incoherent Data Integrations to Ensure What Was Sent Is Understood

You can connect two systems and still fail at interoperability, because integration moves data while semantic interoperability moves meaning. For example, if one system labels a data field “weight” in kilograms and another interprets it as pounds, the data arrived but the understanding did not. I watched a logistics company integrate with a new partner and discover its shipment weights were off by a factor of 2.2 across every record. The systems were technically connected. The data was technically flowing. But nobody had agreed on what the numbers actually meant. This is the danger of home-grown and proprietary integrations, where each side defines data fields on its own terms with no shared agreement. Semantic interoperability solves this by establishing shared definitions, common data models, and agreed-upon context so that what was sent is what is understood. Without it, you are not interoperable. You are just connected and confused. Semantic interoperability usually comes about through standards development. For example:

Standards Bodies Driving Supply Chain Interoperability

Semantic Digital Interoperability: This Is The Ultimate Way To Make Supply Chains Seamless

• GS1: This SDO led the development of the GS1 General Specifications Standard for bar codes and the Global Shipment Identification Number (GSIN) for shipment visibility. These interoperability standards allow different supply chain systems to exchange product information seamlessly, regardless of the software they use.
• ASTM International: This SDO’s technical committee F49 focuses on standardizing digital information in the supply chain. An associated initiative, the Transport Unit IDentifier (TUID) Working Group, has developed a standardized identifier for tracking goods movement across global logistics processes.

For a primer on semantic interoperability, see my article, Semantic Digital Interoperability: The Ultimate Way To Make Supply Chains Seamless.

“You can connect two systems and still fail at interoperability, because integration moves data while semantic interoperability moves meaning.”

4. Enterprise Automation: Building on Technology to Integrate and Digitalize Business Processes

What Is Enterprise Automation? From Replicating Legacy Processes to the Rise of AI

Enterprise automation dictates the terms of success for any data integration project. It shapes which data elements move, the quality of what gets transmitted, and how often. At the same time, the data interface itself is what gives enterprise automation the digital velocity and actionable visibility that actually drives business value. Without true data interoperability, companies fall back on manual hand-offs and human errors, and automation stalls. For example, a manufacturer can automate purchase order approvals across three integrated systems using RPA, cutting average approval time from two days to under an hour. But results like these only happen when true data interoperability is achieved.

The rise of AI only raises the stakes. Enterprise automation is expanding rapidly and getting far more complex. The era of simply replicating legacy processes with rigid, rules-based software is over. With this transformation, businesses must treat true data interoperability across their supply chains as urgent, or they will be overrun by their AI-powered competitors. For more on where enterprise automation is going, see my article, What Is Enterprise Automation? From Replicating Legacy Processes to the Rise of AI.

“Enterprise automation dictates the terms of success for any data integration project. It shapes which data elements move, the quality of what gets transmitted, and how often.”

5. Knowledge Graph Technology: Contextualizing Data to Make Supply Chain Integrations Smarter

Knowledge Graph Tech: Enabling A More Discerning Perspective For AI

Most supply chain data is flat. A table of shipments, a table of suppliers, a table of products. Each is useful on its own but limited, because none of them can show how everything relates. Knowledge graph technology changes that by mapping the relationships between entities, so a product is no longer just a row in a database. It becomes a node connected to its suppliers, its components, its shipping routes, and its compliance requirements. For example, a food company can integrate their data using knowledge graphs  to trace the origin of a contaminated ingredient across six tiers of suppliers in minutes instead of weeks. The data was the same data they always had. The graph simply made the connections visible and queryable. When your integrations are connected to knowledge graphs, they stop moving isolated records and start moving context.

What’s more, in this age of AI, knowledge graph technology complements AI by shoring up some of its biggest weaknesses. AI struggles with data that is incomplete, ambiguous, or simply not available. Knowledge graphs give AI a form of common sense, unifying disjointed data across supply chain functional silos. For more on this topic, see my article, Knowledge Graph Tech: Enabling A More Discerning Perspective For AI.

“Knowledge graph technology … mapping the relationships between entities, so a product is no longer just a row in a database. It becomes a node connected to its suppliers, its components, its shipping routes, and its compliance requirements.”

6. AI Integration: Accelerating Implementations, Standards Development, and Intelligent Data Gathering

Artificial intelligence does not replace interoperability. It accelerates it. AI can speed up integration projects by automating data mapping, suggesting field correspondences, and learning patterns across large datasets that no human team could process manually. It can also help develop and refine data standards by identifying common structures across partners and industries. On the data gathering side, AI tools can prioritize which data to collect, clean, and integrate first based on downstream value. By leveraging these innovations, businesses can achieve more efficient, secure, and high-velocity data integrations. See examples below on how AI improves data interoperability.

Ways AI Improves Data Interoperability

Achieving Supply Chain Interoperability: How To Make Data Right With AI And Triumph Over Digital Disconnects

• Streamline Data Integration Tasks: AI and AI agents can automate data discovery, mapping, data quality improvements, and metadata management, to name a few.
• Machine Learning (ML) to Accelerate Data Standard Development: ML can accelerate the task of classifying and predicting events. AI can also analyze large datasets to help identify new additions to data models and standards.
• Advanced Analytics that Streamlines Information Gathering and Synthesis: Instead of massive data dumps and duplication of data that clog up pipelines, AI and traditional analytics can help us share only the targeted, relevant information.

For more on this subject, see my article, Achieving Supply Chain Interoperability: How To Make Data Right With AI And Triumph Over Digital Disconnects.

“AI can speed up integration projects by automating data mapping, suggesting field correspondences, and learning patterns across large datasets that no human team could process manually.”

7. Data Sharing Platforms: Shifting from Application-Centric to Data-Centric

For decades, supply chains organized themselves around applications. You bought a TMS, a WMS, an ERP, and each of these systems owned its data. The problem is that data does not belong to applications. It belongs to the business, and it needs to flow to wherever it adds value. Data sharing platforms flip the model by making data the center and applications just one of many sources and consumers of that data. When you shift from application-centric to data-centric, you stop arguing about whose numbers are right and start acting on numbers everyone trusts. It is time to stop treating data as a by-product of software applications and start recognizing it as the precious business asset it truly is. To illustrate, here are three ways businesses can share data using these platforms.

Three Ways Businesses Can Share Data Using These Platforms

Being A Data-Centric Business: It’s Going Beyond The Frenzy Of More Big Apps And High Tech

• Collaborative Work Tools: Businesses can use content and work collaboration tools like Google Drive to share files across teams and semi-automate the sharing of documents. See Gartner’s coverage on Collaborative Work Management this category for more information.
• Enterprise-Level Data Collaboration Platforms: These platforms are designed to simplify, automate, and centralize the collaboration and sharing of data assets. See Slashdot’s listing for more information on this topic.
• Build Your Own Collaborative Data Sharing Platform: A business could use existing databases or data lakes to build a collaborative data sharing platform, supplemented by APIs and other third-party tools to extract needed source data. For example, Shiplab provides carrier billing data pipelines for shippers to share parcel invoice data.

For more information on how to transition to a data-centric business, see my article, Being A Data-Centric Business: It’s Going Beyond The Frenzy Of More Big Apps And High Tech.

“When you shift from application-centric to data-centric, you stop arguing about whose numbers are right and start acting on numbers everyone trusts.”

8. Partner Collaboration Platforms: Strengthening Interoperability through Shared Information and Relationship Management

Your supply chain does not end at your company’s walls, and neither does your data interoperability. Partners, suppliers, carriers, and customers all hold pieces of the picture, and if you cannot collaborate and share information with them seamlessly, your visibility stops at your own border. Partner collaboration platforms provide a shared environment where information moves between organizations and relationships are managed over time rather than transaction by transaction. This is true interoperability. For example, I have seen large retailers use these platforms to onboard their top suppliers, giving both sides shared access to inventory levels and demand signals that significantly reduce stockout incidents. Interoperability with partners is not just technical. It is relational, and the right platform supports both. For more on supplier management, see my article, Supplier Management: Optimize, Make Compliant, Assure Quality, Mitigate Where Risky.

Supplier Management Data Analytics: Beyond Just Better Compliance, But To Strengthen Relationships, Assure The Best Outcomes, And Mitigate the Unexpected

” Partner collaboration platforms provide a shared environment where information moves between organizations and relationships are managed over time rather than transaction by transaction. This is true interoperability.”

9. Cloud Infrastructure: Breaking Physical Data Silos to Scale Interoperability and Elasticity

The AI Value Multiplier: The Winning Traits of IT Vendors for Scaling AI

Physical data silos are often literally physical: servers in different facilities, different regions, and different organizational units that cannot reach each other without slow, brittle connections. Cloud infrastructure dissolves those boundaries by giving your data a shared, elastic foundation that can scale up during peak demand and scale down when the pressure eases. For supply chains, this elasticity matters enormously. For example, a logistics provider can use the integration layer of its cloud provider to spin up new partner connections in days instead of the months it used to take with on-premise infrastructure. The cloud is not just a hosting decision. It is an interoperability enabler, because it removes the physical and technical limits that keep data trapped in place.

In this age of AI, cloud computing is even more critical for both interoperability and as an economic driver. Businesses must ask: does my cloud provider lower cost barriers and have the built-in integration capabilities to make AI economically viable, or does it become an expensive bottleneck that quietly drains the business case? AI at scale is ravenous. It demands elastic compute, high throughput, and predictable cost curves that do not punish success. For more on cloud vendors as efficient AI accelerators, see my article, The AI Value Multiplier: The Winning Traits of IT Vendors for Scaling AI.

“Cloud infrastructure dissolves those boundaries by giving your data a shared, elastic foundation that can scale up during peak demand and scale down when the pressure eases.”

10. Digital Identity Solutions: Enabling Trusted Interoperability for Secure Data Exchange

Interoperability without trust is a liability. If you cannot verify who is accessing your data and confirm that the data you receive comes from a legitimate source, then every connection you build is a security gap. Digital identity solutions solve this by giving every system, partner, user, and AI agent a verifiable identity and a controlled set of permissions. I have worked with pharmaceutical supply chains that need to share shipment data with dozens of logistics partners while meeting strict regulatory requirements. For example, a digital identity management system lets them grant and revoke access precisely, audit every data exchange, and prove compliance without slowing the flow of information. Trusted interoperability is not optional when the stakes are high. It is the foundation that makes secure data exchange possible. For more on this topic, see my article, Best Use Of Digital Identity Technology In The Supply Chain,

Best Use Of Digital Identity Technology In The Supply Chain

“Interoperability without trust is a liability. If you cannot verify who is accessing your data and confirm that the data you receive comes from a legitimate source, then every connection you build is a security gap.”

11. Third-Party Providers: Outsourcing to a 3PL or IT Integrator

The Digital Supply Chain Challenge: Is a High Tech 3PL Integrator the Best Way?

Not every organization has the resources or the expertise to build interoperability in-house, and you do not have to. Third-party logistics providers and IT integrators specialize in exactly the kind of integration, data sharing, and system coordination that supply chains need. Outsourcing to the right partner can accelerate your interoperability journey by months and bring capabilities you would struggle to build alone. For example, I have seen mid-sized businesses, such as a regional retailer, outsource their transportation management and data integration to a 3PL and gain end-to-end shipment visibility they could never have achieved on their own. For many companies, neither logistics nor data integration is a competency that offers a competitive advantage, and building it internally is rarely cost effective. The right third party does not just fill a gap. It raises your whole standard. For more on the advantages and risks of outsourcing, see my articles, The Digital Supply Chain Challenge: Is a High Tech 3PL Integrator the Best Way?, and Outsourcing IT Services.

“Outsourcing to the right partner can accelerate your interoperability journey by months and bring capabilities you would struggle to build alone.”

12. Computer Vision AI: Streamlining Data Interoperability with Picture-Perfect Supply Chain Visibility

Some of the most valuable data in a supply chain was never written down at all. It was visible in the physical world, in the form of damaged pallets, mislabeled packages, yard congestion, and loading dock bottlenecks. Computer vision AI captures that visual data and converts it into structured data your systems can use. For example, I have seen a warehouse deploy cameras at its receiving docks to automatically flag damaged shipments, mismatched labels, and unsafe stacking, all without manual inspection. That visual data integrated directly with the WMS and visibility systems. Computer vision AI is a genuine interoperability breakthrough as it replaces error-prone data translation. It gives your supply chain a new source of data that no other integration method can capture picture-perfect. For more information on this topic, see my article, Picture-Perfect Supply Chain Visibility: How Computer Vision AI Closes the Data Integration Gap

Picture-Perfect Supply Chain Visibility: How Computer Vision AI Closes the Data Integration Gap

“Computer vision AI is a genuine interoperability breakthrough as it replaces error-prone data translation. It gives your supply chain a new source of data that no other integration method can capture picture-perfect.”

Conclusion

Data interoperability is not a destination you reach once and then forget. It is an ongoing discipline that evolves as your supply chain grows, as your partners change, and as the technology advances. The twelve areas I have laid out here are not a checklist to complete but a framework to guide your priorities. Some will be more urgent for your organization than others, but none can be ignored indefinitely. The supply chains that thrive in this digital world will be the ones that master interoperability, treating their data as an asset, connecting it without losing its meaning, and putting it to work where it creates value. The ones that do not will keep moving goods across continents while their information stays stuck across the hall. If you want to go deeper, I have additional references on data interoperability below to help you take the next step.

“The supply chains that thrive in this digital world will be the ones that master interoperability, treating their data as an asset, connecting it without losing its meaning, and putting it to work where it creates value.”

More References.

• Supply Chain Visibility: Supply Chain Visibility Technology: Empowering Businesses to See, Track, and Act
• Tracking Status Interoperability: The Way To Better Supply Chain Analytics: Overcome Data Interoperability With Intelligent Tracking Status
• Data Readiness: The Supply Chain Data Readiness Problem: How to Stop Operating in the Blind
• Root Problems Hobbling Data Interoperability: The Data Interoperability Challenge For Supply Chains: 12 Reasons For It And Why Tech Will Never Overcome It Alone
• Data Interoperability’s Three Components: Logistics Data Interoperability: Advice To Make It Understandable, Usable, Secure

Logistics Data Interoperability: Advice To Make It Understandable, Usable, Secure

Need help with an innovative solution to make your supply chain data ready? I’m Randy McClure, and I’ve spent many years solving data readiness challenges to help decision-makers gain better, faster insights and for organizations to leverage data-intensive technologies. As a supply chain tech advisor, I’ve implemented hundreds of successful projects across all transportation modes, working with the data of thousands of shippers, carriers, and 3rd party logistics (3PL) providers. I specialize in pilot projects and program management for emerging technologies. If you’re ready to modernize your data infrastructure or if you are a solution provider, let’s talk. To reach me, click here to access my contact form or you can find me on LinkedIn.

For more articles from Supply Chain Tech Insights, see latest posts on supply chains, information technology, and data readiness.