Nowadays, logistics organizations know that their data holds the key to incredible insights and a competitive edge. However, many struggle to harness this power due their data being scattered across many systems, linked by a labyrinth of data integrations. Worse, even if this data gets transmitted to another system, the meaning of the data often gets lost in translation, resulting in “dumb” data that is practically useless. As supply chains move forward with digitalization, there is indeed a better data integration approach than just continuing to expand this tangled mess of data connections. This better data integration approach is called semantic interoperability.

In this article, I’ll explain what semantic interoperability is and why it is the best approach for businesses to exchange meaningful data across their supply chains. Further, I’ll detail the benefits of semantic interoperability and how other industries and disciplines are advancing it. Also, I have four recommendations on how best for logistics and standards organizations to move forward with achieving semantic interoperability. This includes the importance of knowledgeable business leaders leading these interoperability efforts. Further, I’ll highlight how we can leverage emerging tech such as AI and knowledge graphs to accelerate the implementation of semantic interoperability in supply chains.

1. Semantic Interoperability May Be the Answer, But What Is It?

Most supply chain leaders may not be familiar with the term “semantic interoperability,” but they are likely acquainted with “data integration.” Both terms relate to the transfer of data between different systems, yet semantic interoperability emphasizes the understandability of data once received. It is crucial that both sender and receiver share a common understanding of the transmitted data. The discussion below highlights the importance of achieving semantic interoperability in supply chains, as opposed to merely transferring data between systems.

a. Semantic Interoperability Is Similar to Data Integration. 

Semantic interoperability is closely akin to data integration that currently consumes a lot of IT resources for most businesses. In fact, according to a survey on IT budgets from Digibee, more than half of IT organizations are spending as much as 25% of their time on data integration initiatives. In other words, these organizations are spending a significant amount of their budget attempting to achieve data interoperability between business systems. Even though IT data integration is resource intensive, many business leaders do not know what it entails. For instance, what is the difference between uploading a data file via Dropbox and what IT is doing on all these costly data integration projects?

b. Semantic Interoperability Defined. 

So, the question is why does “data interoperability” cost so much? Why does the IT department take so long and expend so many resources doing data integration? Indeed, the reasons for these skyrocketing costs are much more than just the expense of transmitting the data. In addition to facilitating data transfer, it is implicit that IT integrators are also creating solutions that support meaningful data interchange between systems. The key word is “meaningful”, and specifically, meaningful data. Another term for this is semantics. So, the whole purpose of IT data Integration is to achieve semantic interoperability, not just exchanging data between systems. Below is a definition for semantic interoperability:

“ability of computer systems and organizations to exchange data with unambiguous, shared meaning”

ICAO

c. More Trade and Government Organizations Are Recognizing that Semantic Interoperability  Is the Way for Seamless Data Flows.

Positively, many trade and government member organizations such as ICAO and the EU are keenly promoting semantic interoperability. This is because they acknowledge the importance of helping their members succeed by fostering collaboration and cooperation amongst their systems. Indeed, with the ever increasing number of enterprise business systems, organizations are seeking more efficient ways for their IT systems to exchange meaningful data between each other. For example, the EU’s European Interoperability Framework (EIF) is conducting extensive work to enable semantic interoperability. Click here to see what the EIF is doing to advance data interoperability within the Europe Union’s public administrations.

Also, see Wikipedia for a more detailed discussion on semantic interoperability.

2. What Would Happen If We Achieved 100% Semantic Interoperability in Our Supply Chains?

Imagine a world where all supply chain systems acted in concert, seamlessly exchanging meaningful data. Indeed, each link in the supply chain would have near-perfect information (total visibility) to act with optimal efficiency. Better yet all logistics planners and operators could fully leverage data-driven applications such as AI and data analytics. In fact, there would be no more miscommunications, less guessing, and minimum exceptions that today ripple across supply chains causing disruption, mayhem, and ultimately dissatisfied customers. To list, below is just a sampling of the many benefits of achieving semantic interoperability within supply chains.

The Many Ways Supply Chains Benefit From Semantic Interoperability

The bottom line is supply chain data interoperability enables supply chain staff and systems to take action with quality supply chain data that is highly accurate, complete, timely, and meaningful. Indeed, semantic interoperability results in supply chain excellence and unleashes innovation. For a more detailed discussion on the challenges and benefits of data interoperability, see my article, Let’s Breakthrough The Data Interoperability Nightmare: It Is The Best Way To Unlock Supply Chain Innovation.

Let’s Breakthrough The Data Interoperability Nightmare: It Is The Best Way To Unlock Supply Chain Innovation

3. Examples of Where Other Disciplines Like Healthcare, IoT, and Language Translation Are Advancing Toward Semantic Interoperability.

It is not just supply chains that are grappling with semantic interoperability. In fact, interoperability is critical for most industries and disciplines to include healthcare, defense, language translation, and Internet of Things (IoT) to name a few. What’s more, many of these disciplines are well on their way in this transformative process toward semantic interoperability. The question is, do these disciplines hold a blueprint that the logistics industry can emulate to drive innovation within supply chains? To help answer this question, below are some examples of semantic interoperability initiatives on-going in other industries and disciplines.

“And here lies the core semantic interoperability problem: How can the inherent meaning of a piece of data be preserved across different domains, without needing a human understand and ensure correct translation?”

Ericsson

a. Healthcare. 

Healthcare semantic interoperability allows healthcare systems to share patient data that is both secure and meaningful. As a result, this improves diagnosis and treatment outcomes. In the areas of healthcare there are many ongoing semantic interoperability initiatives. For instance, the US National Institutes of Health is leveraging ISO/IEC 11179 Metadata Registry to build a common semantic model. Also, the U.S. Office of the National Coordinator for Health Information Technology (ONC) is building what they call The United States Core Data for Interoperability (USCDI). This is a standardized set of health data classes and data elements for nationwide, interoperable health information exchange.

b. Department of Defense.

DoD has a program called Department of Defense Architecture Framework (DODAF). Within this program, DoD is building logical data models to achieve semantic interoperability. As DODAF describes it “… the emphasis is on the identification and description of the information in a semantic form (what it means) and why it is of interest (who uses it).”

c. Language Translation Software. 

Traditionally, language translation has relied on statistical machine translation (SMT) approaches. Basically, these approaches automatically map sentences in one human language (for example, French) into another human language (such as English). Now, starting with Google Translate efforts back in 2016, the focus is now on neural machine translation (NMT). This AI-based tool provides improved quality, fluency, and context preservation. See Adriano Raiano’s article, Modern Technology And The Future Of Language Translation, for an excellent overview of the evolution of language translation software.

d. Internet of Things (IoT).  

IoT vendors and standards groups are advancing semantic interoperability to enable devices to communicate and work together intelligently. For instance, see Erricsson’s Niklas Widell blog post, What is semantic interoperability in IoT and why is it important?. According to Circutor, IoT interoperability is a top trend in the IoT industry.

So, there are many industries and disciplines that are both committed and having success with achieving semantic interoperability. Now, what about supply chains?

4. Four Things Need to Happen For Supply Chains to Achieve Semantic Interoperability.

For the supply chain industry to truly succeed with achieving semantic interoperability, several things need to happen. First, business leaders need to get involved. This includes both supporting semantic interoperability efforts and helping to provide clarity to supply chain data terms we use today. Indeed, data is now a strategic asset and tool for businesses to leverage to achieve both efficiency and improve service levels.

“… we need to stop building a “Tower of Babel” with the proliferation of more proprietary data interfaces.”

Further, it is critical that business leadership help cut the ambiguity out of logistics data. This will go a long way in advancing the meaningful exchange of data within the supply chains. Positively, this is a business-led effort that brings clarity and mutual understanding to supply chain data terms. This is not something that IT or technology does well. 

Additionally, businesses need to make it a priority on their next data integration project to first seek to use existing data standards versus establishing another proprietary data interface. Indeed, we need to stop building a “Tower of Babel” with the proliferation of more proprietary data interfaces. Lastly, there are opportunities to streamline the process of achieving semantic interoperability using emerging tech such as AI and knowledge graphs.

See below for the specifics on my four recommendations on what the supply chain industry need to achieve semantic interoperability.

a. First, Businesses Need to Adopt a Data-Centric Mindset.

1) Supply Chain Data Is Fragmented With No Single Source of Truth (SSOT).

Indeed, most businesses are challenged today to leverage the vast amount of data within each of their business systems. This is because their data is fragmented, locked in individual software application silos. Thus, most supply chains only have rudimentary data interoperability between their systems and external systems. Worse, their data is duplicated, inaccurate, incomplete and out-of-date with no data repository offering a Single Source of Truth (SSOT). 

2) Data Is Either Not Shared or Loses Meaning During Data Transmission.

Further, the data that does get exchanged between systems in many cases loses much of its meaning during the data exchange. For example, a data transmission may contain a date field, but what type of date does it represent? – shipped, delivered, created, transmitted, loaded on the truck, etc. Indeed, this is not so much a technical problem, but a problem where a business person needs to dig into the data and provide direction to eliminate ambiguity.

3) Need To Shift Our Mindset From Software-Centric to Data-Centric.

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

A major cause of our data interoperability problem is that we have gotten too software centric where we think that just another software update will solve all our problems. As a result, we continue to think that data is just a by-product of software applications. 

This application-centric mindset needs to change. Indeed, what business leaders need to do is adopt a data-centric mindset where data is treated as a most valuable and permanent asset. Adopting a data-centric mindset enables businesses to start thinking of data as something to nurture and leverage as a competitive advantage. Hence, a data-centric approach leads to businesses investing in data structure and networks, training personnel to become data-savvy, and fostering a culture where data is shared and leveraged across departments. 

By shifting the focus to the intrinsic value of data, businesses can build a strong foundation that supports true semantic interoperability. For a more detailed discussion on developing a data-centric mindset, see my article, A Data Centric Business: The Best Way To Agility, One Truth, Simplicity, Technology Innovation.

b. Second, Need Logistics Organizations to Partner With Standards Development Organizations (SDO) to Advanced Semantic Interoperability.

At its essence, supply chains are focused on the movement of goods and the financing that supports logistics. Thus, the primary components of supply chain data consist of shipments (moment) and products (goods) as well as the FinTech-driven transactions that support logistics financing. 

1) Data Gets Transmitted, But Loses Meaning In Translation.

Now over the years, businesses have found many ways to transfer data. For more details on this subject, see my article, The Best Ways To Access Data – Tech Solutions To Unlock Your Data Silos.

So, the real problem is data ambiguity. Indeed, the challenge is with semantic interoperability – transmitting data that retains its shared meaning. Take for example a carrier that provides an electronic invoice in either a comma-separated values (csv) or electronic data interchange (EDI) format for convenient download. If the shipper’s IT department then fails to properly translate the invoice data into their system, issues arise. This miscommunication might stem from inadequate data transfer documentation from the carrier. On the other hand, the shipper may have misunderstood the documentation. Additionally, it might result from poor collaboration between the shipper’s IT and business groups while mapping the data to their system. Consequently, semantic interoperability remains limited or non-existent.

2) Standards Development Organizations (SDO) Help Provide Shared Meaning to Data.

So, while most businesses can transfer data, how can we achieve true data interoperability where we can consistently transfer meaningful data? One effective approach is for businesses to better support Standards Development Organizations (SDOs). Consider how standardization enabled U.S. railroads to use uniform track gauges, allowing them to transport railroad cars seamlessly. Similarly, with supply chain semantic interoperability, we need to support the advancement of data standards. These standards provide a metadata framework that describes and informs us about supply chain data. Below are examples of metadata frameworks that provide meaning to supply chain data and related financial data terms.

Examples of Standards Development Organizations (SDO) and Trends

14 New EFT Payment Technology Trends In The FinTech Industry

• GS1. GS1 offers many interoperability standards such as GS1 General Specifications Standard for bar codes and Global Shipment Identification Number (GS1 GSIN) for shipment visibility.
• ASTM F49. ASTM F49 is defining a non-proprietary, intelligent tracking data framework to achieve true data interoperability. This interoperability framework will enable supply chain stakeholders to share intelligent shipment status with their partners. 
• FinTech Trends. For examples of emerging financial technology (FinTech) trends, see my articles, 14 New EFT Payment Technology Trends In The FinTech Industry and Freight Payment Terms: A Painful Money Game, Its Purpose, Is There A Better Way?

Also, SDOs are more than just developing data standards such as data dictionaries. They are also working on defining business glossaries which are critical for achieving data interoperability. In fact, the need for comprehensive business glossaries is often overlooked, but is in fact a major reason that we lack operational clarity among business organizations and their systems. So, business glossaries are critical. Indeed, business glossaries are needed to provide clear definitions to enable both business systems and their organizations to successfully communicate and be understood. For more on the critical role that business glossaries have on achieving semantic interoperability, see my article, Poor Operational Definitions Impede Supply Chain Tech Adoption: Now Is the Time For A Big Change.

Poor Operational Definitions Impede Supply Chain Tech Adoption: Now Is the Time For A Big Change

3) The Need for Logistics Organizations to Partner With Standards Development Organizations.

In summary, I recommend that supply chain leaders get involved with supporting standards development organizations (SDO) that help advance semantic interoperability in the logistics industry. This is not just something left to IT. Data is a business asset. Developing and adopting a supply chain metadata framework requires collaboration across the industry. It is supply chain leaders who need to agree on common terms, definitions, and taxonomies to ensure that data retains its significance regardless of where it travels. For example of the benefits of logistics organizations benefiting by partnering with standards development organizations, see my article, Unifying Shipping Data Using Transport Load IDs: Here Are 10 Ways It Can Unlock Analytics And Empower Logistics Tech.

Unifying Shipping Data Using Transport Load IDs: Here Are 10 Ways It Can Unlock Analytics And Empower Logistics Tech

c. Third, Need to Move Away From Proprietary Data Interfaces.

Proprietary data interfaces are an expedient method to transfer data, but usually the benefits are short-lived. In many cases with a proprietary interface, the data will lose meaning in translation and it is difficult to reuse for analytics. Worse, proprietary data formats overtime lock data into silos. Further, to make updates to these custom data interfaces, requires expensive IT resources with long lead times. See below for more details and the compelling reasons to move away from proprietary data interfaces.

1) Proprietary Data Interfaces Is Currently the Norm for Exchanging Supply Chain Data.

The Digital Supply Chain Challenge: Is A High Tech 3PL The Best Way?

For decades, logistics organizations have implemented proprietary system-to-system data Interfaces. Especially for supply chains, this has created a tangled web of information flows and data silos. Worse, over the years many large logistics organizations and electronic marketplace have encouraged their customers to adopt their proprietary data interfaces and APIs to create a seamless online community. At best, this has created short-term benefits, but in the long-term these proprietary data networks become obsolete, very expensive, or go out of business. The end result – more proprietary data interfaces and data silos.

At the same time 100% semantic interoperability is not going to happen overnight. See my article, The Digital Supply Chain Challenge: Is A High Tech 3PL The Best Way? on using a 3rd party logistics provider (3PL) for digitalizing your supply chain versus doing it yourself.

2) Complex Network of Supply Chain Systems, Technology, and Data Requirements.

Further, supply chains are complex, having a diverse range of technologies, hardware, and software platforms used by different stakeholders. What’s more, few industries have to deal with so many systems, numerous stakeholders, and massive amounts of data. Worse, shipment and inventory status updates in supply chain operations are by their nature fleeting adding even more challenges for decision-makers.  Moreover, integrating these disparate systems is a monstrous task that requires considerable time, effort, and resources. 

3) We Have Created a Data Interoperability Nightmare and It Is Time to Untangle It.

Let’s Breakthrough The Data Interoperability Nightmare: It Is The Best Way To Unlock Supply Chain Innovation

The end result is that we have created a data interoperability nightmare compounded by our continued use of proprietary data interfaces. Undeniably, this untenable situation does not make for a common framework for supply chain communications. Indeed, this is what we need to exchange meaningful, actionable data between our supply chain systems.

Consequently, if supply chains continue this practice of adding more proprietary interfaces, we will just continue this data interoperability nightmare. This quagmire needs to stop. It is preventing supply chains from making innovative transformations to increase efficiencies and improve services. To list, below are examples of the challenges we create every time we add a new proprietary interface to this data interoperability nightmare.

Examples Of Poor Supply Chain Data Interoperability

For a more detailed discussion of these data interoperability challenges, see my article, Why Is Data Interoperability Such A Nightmare For Supply Chains?

d. Forth, Leverage AI, Knowledge-Centric, and Digital Identity Tech to Empower Data Interoperability.

To maintain a dynamic metadata standards framework, supply chains can harness powerful emerging tech such as knowledge graphs, artificial intelligence (AI) and digital identity tech. The old ways of data silos where critical information is locked within proprietary enterprise systems simply won’t cut it. Further, cobbling together these systems with more custom-built data interfaces is not the answer.

Indeed, traditional data integration efforts are a slow-moving technical process that demands significant know-how. Now in the past, standards development organizations (SDO) like GS1 have helped ease integration tasks such as inventory tracking with standardized barcodes. Further, SDOs do offer some hope today facilitating data interoperability. However, integrating data is still a slow and excruciating process. The question is can emerging technology help us to achieve true data interoperability?

For a detailed discussion on how emerging technologies can transform traditional data Integrations to be more secure, understandable, and of higher velocity, see my article, Data Interoperability Tech: It Is More Than Integrations – It’s Understandable, Secure, And High Velocity Technology.

Conclusion.

In the past, our supply chains were transformed by standardizing bar codes and ocean containers to achieve better interoperability. Now to fully leverage data-driven tech like AI, we need to achieve interoperability with our supply chain data. In this article I offer a blueprint with four recommendations on how best for logistics and standards organizations to move forward with achieving semantic interoperability. This included the importance of knowledgeable business leaders working with standards development organizations (SDO) to lead these interoperability efforts. Additionally, it is time to move away from proprietary data interfaces. Further, I highlighted how we can leverage emerging tech such as AI and knowledge graphs to accelerate the implementation of semantic interoperability in supply chains.

OK, semantic interoperability will not happen overnight, but it will happen over time to transform logistics operations. Just think what the standardization of bar codes and ocean containers did for the industry in the past.

Supply Chain Business Communications Need Clarity: This Is What is Hobbling New Tech Innovations

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