In the lightning-fast world of digital commerce, data integrity and its seamless flow between systems is essential for making informed decisions and driving business success. Yet, beneath the surface, a silent saboteur is at work: “dumb integrations.” These aren’t just technical glitches; they’re disjointed digital exchanges where despite perfectly transmitted data, it loses its integrity because it’s fundamentally misunderstood between business systems. It’s like having a perfect postal service for letters written in a language no one receiving them comprehends. The result? Ambiguity, misinterpretation, and ultimately, decisions based on digital guesswork. So, why do we continue to build these data bridges to nowhere?

In this article, I’ll look at examples of “dumb integrations,” dissecting the core reasons why data integrity consistently loses its value. Without a doubt, these examples will illustrate how it is fundamental misunderstandings between our systems that create obstacles that cripple interoperability in our digitally integrated world. More importantly, I’ll offer you my insights and proven strategies for overcoming these challenges, transforming your data exchanges from digital guesswork into genuine understanding. Indeed, it’s time to stop building bridges to nowhere and start building intelligent connections. Read on.

“…  why do businesses and their systems exchange data that is not understood?”

1. What Does Data Integrity, Integration, and Interoperability Have to Do With Each Other?

In today’s interconnected digital landscape, data integrity, integration, and interoperability are the cornerstones of efficient and effective business operations. Because these specialized methodologies are so critical to modern supply chains, it is crucial that we know how each of these processes work. Without optimizing these technical processes, organizations will struggle to fully leverage business insights from their data. In particular, if businesses do not master these data-intensive methodologies, their data will remain ambiguous and disconnected across business systems, yielding few actionable insights. To better explain these digital disconnects, let’s start with some definitions.

Definitions for Data Integrity, Integration and Interoperability

“Data integrity is the assurance that an organization’s data is accurate, complete and consistent at any point in its lifecycle. Maintaining data integrity involves safeguarding an organization’s data against loss, leaks and corrupting influences.”

IBM

“Data integration is the process of achieving consistent access and delivery for all types of data in the enterprise.”

AWS

“Data (Semantic) interoperability is ensuring what is sent is what is understood”

EIF

Without a doubt, when setting up data transfer interfaces between systems, businesses quickly become frustrated with their IT departments. The root cause: massive deficiencies in data integrity, integration, and interoperability. To break it down, businesses need their data to be accurate, complete, and consistent (data integrity). Moreover, when they share data it needs to be understood by the receiving system (interoperable). However, what businesses often get is data that is integrated but not properly understood by the receiving system. As a result, routinely the value of data transferred between systems is not actionable. For more on this topic, see my article, This Is What Semantic Interoperability Is: It’s The Best Last Chance For Seamless Supply Chains.

This Is What Semantic Interoperability Is: It’s The Best Last Chance For Seamless Supply Chains

“… businesses need their data to be accurate, complete, and consistent (data integrity). Moreover, when they share data it needs to be understood by the receiving system (interoperable).”

2. The Reasons Why Data Integrity Suffers Due to Poor IT Integrations.

Undoubtedly, poor IT integrations will severely undermine data integrity, leading to a cascade of problems that can ripple through an organization. When we do not properly integrate systems, data becomes fragmented, inconsistent, and unreliable. This results in incorrect reports, flawed analytics, and misguided business decisions. The root cause often lies in the assumption that simply connecting systems will ensure seamless data exchange, which is far from the truth. Next, let’s look in more detail how poor IT integrations adversely affect data integrity.

a. The Enterprise Integration Myth that Data Exchanged is Data Understood.

First, one of the most pervasive myths in enterprise integration is that data exchanged is data understood. More specifically, this myth assumes that once a system transmits data “successfully”, the receiving system will interpret it correctly. So in many cases, the crux of the issue lies not in the transmission of data, but in its interpretation. For example, a date field may represent a purchase date in the source system. However, when this transmitting system transfers the data to the target system, this receiving system misinterprets the date field as a ship date. As a result, this causes errors in calculations and reports in the receiving system.

b. Examples of Data Integration Where Data Loses Its Meaning.

As discussed previously, a data integrator can successfully establish a data interface between two systems, but that does not necessarily mean data interoperability will occur. Here are some examples:

The Ways Poor Operational Definitions Compromise Supply Chain Interoperability

First, let’s take an example from supply chains. In many cases, different organizations will have varying definitions of basic business terms such as “shipped” and “delivered”. As a result, the sender will transmit ambiguous data and the receiver will misinterpret it upon receipt. For instance, a warehouse system may transmit a “shipped” status message for a package when in reality the shipper’s operation has only printed a bar code label for the package. As a result, the customer reasonably expects that the package is on its way upon receiving the “shipped” status message versus it still sitting on the shipping dock.

Here’s another example of failed data integrations – a financial system interfacing with a marketing system. In this case, the financial system uses a different definition for “revenue recognition” than the marketing system. As a result, the data will be misinterpreted. This can lead to inaccurate financial reports and misguided marketing strategies. For more examples of data disconnects due poor business definitions, see my article, The Ways Poor Operational Definitions Compromise Supply Chain Interoperability

c. Data Integrity Lost When Context and Clear Definitions Are Not Shared Between Systems.

So, there’s a gap between organizations actually integrating their systems and truly achieving true data interoperability. In particular, a knowledge gap often exists regarding the data transferred between the two organizations and their systems. While the receiving system will usually import the data successfully, it will lack the knowledgeable context and clear business definitions needed to make it understandable. Indeed, semantic interoperability is essential. This is what empowers organizations and their systems to exchange meaningful data with their partners’ systems. Hence, semantic interoperability closes the knowledge gap and enables the achievement of true data integrity between systems. For more on achieving semantic interoperability, see my article, Achieving Logistics Interoperability: The Best Way to Breakthrough The Tangle Of Dumb Data Integrations.

Achieving Logistics Interoperability: The Best Way to Breakthrough The Tangle Of Dumb Data Integrations

“… semantic interoperability closes the knowledge gap and enables the achievement of true data integrity between systems.”

More References.

• Semantic Interoperability Solutions – Achieving Logistics Interoperability: The Best Way to Breakthrough The Tangle Of Dumb Data Integrations.
• Ambiguous Business Glossaries – Feeble Business Glossaries: Know It’s The Greatest Killer Of Supply Chain Collaboration And Data Interoperability.
• Steps to Achieve Data Interoperability – Semantic Digital Interoperability: This Is The Ultimate Way To Make Supply Chains Seamless. 
• Semantic Interoperability Success Stories – Semantic Interoperability Examples That Turn The Tables From Having Dumb Data Communications To Being Really Informative.
• Data Interoperability Needs – The Data Interoperability Challenge: It’s The Need For Tech Standards, Compliance, Security, Massive Resources, And Be Understandable.

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

Need help with an innovative solution to make your supply chain systems work together? I’m Randy McClure, and I’ve spent many years solving data interoperability and visibility problems. 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 ?proof-of-concept and operational pilot projects 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 from SC Tech Insights, see the latest articles on Interoperability and Information Technology.