In an era defined by rapid disruption and razor-thin margins, many organizations believe they have a supply chain visibility problem. But, the reality is much more systemic: they have a Data Readiness Gap. Despite investing millions in sophisticated ERPs and logistics platforms, leaders remain “blind” to the critical shifts in their own networks because their data is trapped, inconsistent, or too out-of-date to matter. AI, by itself, will not solve this predicament. Without a doubt, this is more than a tech problem, supply chains have a data readiness problem. 

In this article, I’ll share with you the five primary reasons why we struggle to achieve total supply chain visibility. Everyone of these gaps trace back to our lack of data readiness. These data challenges include limited interoperability, data treated as a software byproduct, disjointed shipping data, isolated analytics, and slow-moving insights. By knowing our data readiness gaps, we can now go about fixing them.

1. Breaking the Data Silos: Achieving Interoperability Across the Fragmented Supply Chain.

The first hurdle to true supply chain visibility isn’t a lack of data; it’s the sheer volume of disconnected sources—from disparate carrier portals to legacy internal systems—that refuse to speak the same language. These silos create a fragmented landscape where “the truth” depends entirely on which department you ask. Achieving interoperability requires moving beyond simple API connections to a robust digital framework that harmonizes these conflicting data streams. To list, below are the five major data Interoperability obstacles today preventing total supply chain visibility.

Five Data Interoperability Constraints

The Data Interoperability Challenge: It’s The Need For Tech Standards, Compliance, Security, Massive Resources, And Be Understandable

• Inadequate Tech Standards: The lack of standards frustrates data interoperability.
• Regulation Compliance: Obeying data protection laws and corporate policies.
• Data Security: The perplexing challenge to verify, authorize, and authenticate data users.
• Massive Tech Costs: The high costs of IT data interoperability and integration projects.
• Making the Data Sent Understood: The absence of shared knowledge across systems and organizations to correctly interpret data.

For a more detailed examination of these constraints and how you can fix them, read my article, The Data Interoperability Challenge: It’s The Need For Tech Standards, Compliance, Security, Massive Resources, And Be Understandable.

“Achieving interoperability requires moving beyond simple API connections to a robust digital framework that harmonizes these conflicting data streams.”

2. From Byproduct to Asset: A Business Shift to a Data-Centric Mindset.

A Data-Centric Business: The Best Way To Agility, One Truth, Simplicity, Technology Innovation

For too long, supply chain have treated their data as a mere byproduct of business applications, especially Enterprise Software.  As a result, most businesses remain mired in traditional operational structures, with departments working in isolation, using disparate functional systems that are not designed to synergize. To bridge the data readiness gap, organizations must first undergo a fundamental mindset shift to be data-centric. This means viewing their data as a high-value corporate-wide asset that requires deliberate investment and a data-centric strategy. From a technical perspective, this means that a business’ most critical data must live independently of the software applications that created it.

For a more detailed discussion of what a data-centric mindset means, its benefits, and how to get started, see my article, A Data Centric Business: The Best Way To Agility, One Truth, Simplicity, Technology Innovation.

“… organizations must first undergo a fundamental mindset shift … viewing their data as a high-value corporate-wide asset that requires deliberate investment and a data-centric strategy.”

3. Unifying Logistics Visibility: Integrating Finance, Planning, and Operational Partners.

Better Shipping Data Analytics Results: Use Of Load IDs To Achieve The Best Efficiency, Visibility, And Financials

Another major problem with logistics visibility is shipping data. The fragmentation of shipping data across order fulfillment, TMS, and financial systems creates a significant “linking” challenge for analysts and AI-powered systems. As a result, different organizational departments only achieve partial, disjointed visibility when linking their shipping data to disconnected reference numbers like tracking IDs, invoices, and POs. To bridge this gap, organizations must integrate finance and planning directly into the operational flow. This horizontal alignment ensures that a shipping delay is immediately recognized for its financial impact and its ripple effect on future planning. As a result, this transforms reactive troubleshooting into proactive, data-driven adjustments.

One solution to this complicated shipping data structure is for shippers to start using a shipper reference number or a Load ID for each of their shipments. Indeed with the use of a shipper-originated load ID, shippers can unify their shipment data starting with planning through execution to payment to post-diagnostics, gaining both horizontal and end-to-end supply chain visibility. For a detailed discussion of this shipper-generated Load ID concept, see my article, Better Shipping Data Analytics Results: Use Of Load IDs To Achieve The Best Efficiency, Visibility, And Financials.

“… different organizational departments only achieve partial, disjointed visibility when linking their shipping data to disconnected reference numbers like tracking IDs, invoices, and POs.”

4. Actionable Intelligence: Converting Data Chaos into Decision-Ready Insights.

Again, in most organizations, supply chain data is chaotic—fragmented across dozens of global systems including order fulfillment, TMS, 3PL tracking, and IoT networks to name a few. This fragmentation also forces data analytics into functional silos where BI dashboards merely report “what happened” in isolation. Then using different data sets, planners guess “what is likely to happen”. At the same time, supply chain data itself is complex. For example, a single shipment can generate hundreds of data elements across scores of disparate systems. 

Meet Ralph Whose The Best At Leveraging Awesome Data Analytics Technology To Empower His Supply Chain

As supply chain data is so chaotic, it’s challenging for decision-makers to get the full benefits of advanced analytics. Instead of isolated pockets of analytics, decision-makers need data that is ready to support the full range of data analytics. This enables decision-ready insights across the supply chain. The full range of data analytics types are as follows:

6 Types of Data Analytics

• Descriptive Data Analytics: What Happened?
• Diagnostic Data Analytics: Why Did This Happen?
• Predictive Data Analytics: What Is Most Likely To Happen?
• Prescriptive Data Analytics: What Action Should We Take?
• Real-Time, On-Demand Analytics: What Do I Do Now?
• AI-Powered Analytics: What Questions Did I Not Know to Ask?

For a more detailed discussion on these distinct types of data analytics, see my article, Meet Ralph Whose The Best At Leveraging Awesome Data Analytics Technology To Empower His Supply Chain.

“Instead of isolated pockets of analytics, decision-makers need data that is ready to support the full range of data analytics. This enables decision-ready insights across the supply chain.”

5. High-Velocity Information: Powering On-Demand, AI-Driven, Adaptive Insights.

If you’ve ever sat in a boardroom waiting for an “urgent” report that’s already three days late and two days irrelevant, you know the frustration of traditional decision-making. We’ve been taught to rely on a mix of gut instinct and slow-motion analysis, but in a world moving at the speed of an algorithm, “slow and steady” just gets you left behind. The reality is that our intuition is a powerful tool, but it’s being starved of the rapid insights it needs to actually make timely decisions. The final frontier of closing the data readiness gap is the move toward high-velocity information: insights that are not only on-demand but are also AI-driven and inherently adaptive.

Below are the three high-velocity elements needed to support today’s rapid decision-making processes.

High-Velocity Information – 3 Elements Needed

High-Velocity Decision Systems for Executives: The Three Ways To Best Exploit AI Tech And Data Analytics

• On-Demand Analytics: In conjunction with traditional analytics (descriptive, diagnostic, predictive, prescriptive), uses a “Data-Ready” framework that is prepared to provide immediate insights.
• AI-Powered Analytics: Autonomous and extraordinary computing capabilities, accessing massive data sets, revealing new insights, and answering unforeseen questions.
• Continuous Feedback Loop:  Enables Decision Systems to learn and adapt. It closes the gap between prescribed insights and actual outcomes. 

For more details on how these high-velocity components fit together, see my article, High-Velocity Decision Systems for Executives: The Three Ways To Best Exploit AI Tech And Data Analytics.

“… move toward high-velocity information: insights that are not only on-demand but are also AI-driven and inherently adaptive.”

More References.

For more discussion and references on supply chain visibility, see:

• Anvyl’s article, How to Improve Supply Chain Visibility With Real-Time Data
• Tibco’s article, What Is Supply Chain Visibility
• Lora Cecere’s article, Supply Chain Visibility. If It Seems Simple, Look More Closely
• SC Tech Insights’ articles, Reactive Shipment Tracking Vs Proactive Operational Visibility: Here Is How To Best Overcome Supply Chain Exceptions and Supply Chain Visibility Of Cargo: Know Your Need And The Surprising Challenge To Secure It.

Supply Chain Visibility Of Cargo: Know Your Need And The Surprising Challenge To Secure It

For more from SC Tech Insights, see the latest articles on Data Analysis, Supply Chain, Interoperability, Decision Science.

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.