Digital supply chains are the new frontier. But let’s ask ourselves, “How is our digital transformation really going?” Maybe not as well as we’d hope? One major stumbling block for many digitization efforts is the inability of systems and technologies to communicate with one another. This data integration challenge, also called data interoperability, is crucial for achieving a seamless digital supply chain and maximizing our tech investments. For example, automating internal processes to reduce paperwork can increase efficiency, yet it might also just create another isolated data silo. Indeed, without smart data exchange between systems and devices, your digitalization goals will remain out of reach.
In this article, I’ll offer you some solutions to help your organization achieve data interoperability and get your systems talking to each other. First, I’ll highlight the current disjointed state of supply chain data. Next, I’ll detail for you the three key components needed to achieve data interoperability in today’s digital world. Namely, these three interoperability components are 1) a data transfer capability, 2) common terminology for intelligent data sharing, and 3) a method to achieve mutual trust between systems, users, and devices. So, let’s get started.
Current State of Supply Chain Data: Fragmented, Functional Silos, Poor Structure, Few Insights.
Supply chain data is a nightmare to work with. Indeed, it is often characterized by disjointed systems that inhibit visibility and collaboration. What’s worse is that most logistics data remains trapped within departmental silos, each with its own data structure and format. Consequently, most of us are drowning in a sea of data that’s hard to fathom, and even harder to act upon. Moreover, within this “big data” are precious insights buried under layers of incompatibility and ambiguity.
For a more detailed discussion on supply chains’ data interoperability nightmare, see my article, Let’s Breakthrough The Data Interoperability Nightmare: It Is The Best Way To Unlock Supply Chain Innovation.
“The single biggest problem in communication is the illusion that it has taken place.”
George Bernard Shaw
The Three Ingredients Needed to Achieve Data Interoperability: Data Communications Channels, Shared Meaning, Mutual Trust.
Most of us will agree that to remain competitive in today’s world, our organizations must continue to digitalize. Indeed, with digital transformation, businesses must also achieve data interoperability. For instance, a supply chain cannot capitalize on its supply chain digitization efforts unless it can share data with its many internal systems as well as its partner systems. Further, to do this, an organization must do more than just have the capability to transfer data back and forth. In fact, there are three ingredients to achieving true data interoperability. These three components include:
The Three Components to Achieve Data Interoperability
- Data Communications Channels. Capability to exchange data between systems and devices.
- Common Data Terminology to Share Meaningful Information. Must have standard terms and methods to effectively assure that the data transmitted is understood.
- Method to Achieve Mutual Trust. Need to leverage digital identity technologies to enable us to trust digital network partners, software agents, and devices. As a result, we gain confidence in the data.
Next, I’ll detail each of these three critical ingredients needed to achieve data interoperability.
1. Data-Level Interoperability: A Communications Channel That Enables Data Transfer Between Systems.
First, the baseline component of data interoperability is the technical capability to both transfer and access data. This includes capabilities like using an API (Application Programming Interfaces) or a data transformation process such as Extract, Transform, Load (ETL) to integrate data. Nowadays, most organizations have some level of capability for data-level interoperability.
Also, for a given data integration project, the integrator will need to decide on which type of data transfer method to use. This will depend on various factors. For instance, considerations include things like data set size, in-house IT expertise, data transfer types available from data sources, and budget limitations. While many businesses can handle the more simple projects internally, some integration projects may demand advanced cloud integration tools or skilled third-party vendors for success. For a more detailed discussion on types of data transfer solutions, see my article, The Best Ways To Access Data – Tech Solutions To Unlock Your Data Silos.
2. Semantic-Level Interoperability: Business-Led, Tech-Enabled To Assure the Data Sent Is Understood.
The second component of data interoperability is semantic interoperability. This critical ingredient provides data meaning and understandability when data is transferred from one system to another. Basically, semantic interoperability is achieved when data is successfully transferred AND the data that was sent was understood by the receiving organization. Indeed, the ultimate goal of data interoperability is the ability for the receiver of the data to take intelligent action on the data received. For a concise definition of semantic interoperability, see below:
“Ensuring what is sent is what is understood”
European Commission – EIF
Today, semantic interoperability is the biggest stumbling block for digital supply chains. First, we have more systems generating more data that we do not understand. Further, we keep trying to improve our data fluency problems with tech solutions, when it is really a business problem. See discussion below.
a. The Digital Transformation Challenge: More Complex Systems Generating More Data That Gets Lost in Translation.
Today, a major problem we have is that we are generating and transferring increasingly more data for more complex business uses. As a result, our systems are now even more challenged to capture the real meaning of the data transferred. In many cases, the meaning of the data gets lost in translation, and therefore, we and our systems cannot take intelligent action on the data received. For more on tackling the problems with digital transformations, see my article, The Way of Digital Transformation: A Business First, High Tech Reinvention 0f Processes and Culture.
b. Semantic Interoperability is a Problem for Business to Solve.
Also as supply chains organizations gain experience in data integration, they begin to realize that issues with data interoperability is not just an IT problem, but rather a business problem. While IT can transfer data from one system to another, the meaning of the data is unclear for the receiving organization. Let’s take the example of a transportation carrier transmitting data to a shipper. In this case, one of the data elements is a date field. The question for the shipper is what does this date field represent. Is it a ship date, manifested created date, data transmission date, or just a random date? Indeed, this is a semantic interoperability problem!
Ultimately, business must clearly define all these data elements and assign meaning to them. Based on my years of experience working with thousands of shippers and third-party logistics (3PL) providers, I recommend that businesses take the following four key actions.
Business-Led Improvement Areas to Achieve Semantic Interoperability
- Adopt a Data-Centric Mindset; Stop Being Application-Centric, treating data as a byproduct.
- Partner With Standards Development Organizations (SDO) to Advance Semantic Interoperability.
- Avoid Proprietary Data Interfaces.
- Leverage AI and Knowledge-Centric Tech to Enable Data Standards to Learn, Evolve, and Expand.
For a detailed discussion on semantic interoperability and how to move forward with achieving it in your business, see my article, Achieving Logistics Interoperability: The Best Way to Breakthrough The Tangle Of Dumb Data Integrations.
3. Trusted Interoperability: Leveraging Digital Identity Tech to Achieve Confidence in the Data Exchanged by Partners and Entities.
An increasingly critical component of data interoperability is trusting in who or what is the source of data. In fact in terms of magnitude and breadth, data security is a new phenomenon. This is because data owners need to share and receive their data with more and more systems. What’s more, non-traditional systems such as AI agents and Internet of Things (IoT) devices are beginning to share and consume an astronomical amount of data. So first, businesses now have more security compliance challenges. To gain a better understanding of the importance of digital identity tech, below is a comprehensive definition of what a digital identity is and what it encompasses.
Definition of Digital Identity
“A digital identity is an online presence that represents and acts on behalf of an external actor in an ecosystem. An identity could belong to a legal entity, a financial intermediary, or a physical object, for example. Ideally, a digital identity is verified by a trust anchor, or something confirming the legitimacy of an actor, so that those interacting with that actor’s digital identity have confidence the actor is who and what it claims to be.”
World Economic Forum
Now, in the early days of data transfer, digital identity was not much of an issue. First, there were not many connections to deal with. Second, IT staff could easily handle new connections by just issuing a password to authorized users. Also, for a data integration project, IT could just work with the other IT staffs to set up the initial data connection. That was it! Now in our increasingly digital world, keeping our data network secure includes innumerable challenges. This includes an astronomical number of connections are needed, an increase in bad actors, more data security compliance requirements, high levels of automation, and the dynamic need for new data connections.
Thus, digital identity technology and methodologies, more than ever, are critical to achieve data interoperability. For a detailed discussion of digital identity technology, especially for the supply chain industry, see my article, Digital Identity In Logistics And What To Know – The Best Security, Scary Risks. Key topics discussed include:
Conclusion.
In summary, for logistics leaders to achieve the benefits of a digital supply chain, they will need to conquer data interoperability. Only through harnessing the power of data interoperability can businesses stay competitive and realize the full benefits of digitalization. Indeed, data interoperability is a business-led, tech-enabled pursuit. Only through intelligent data exchange between systems and devices, will you achieve your digitalization objectives. As discussed, there are three key ingredients needed to achieve data interoperability in today’s digital world. Namely, these three interoperability components are 1) a data transfer capability, 2) common terminology for intelligent data sharing, and 3) a secure method to achieve mutual trust between systems, users, and devices.
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.
Also, for more from SC Tech Insights, see the latest articles on Information Technology and Interoperability.
Greetings! As a supply chain tech advisor with 30+ years of hands-on experience, I take great pleasure in providing actionable insights and solutions to logistics leaders. My focus is to drive transformation within the logistics industry by leveraging emerging LogTech, applying data-centric solutions, and increasing interoperability within supply chains. I have a wide range of experience to include successfully leading the development of 100s of innovative software solutions across supply chains and delivering business intelligence (BI) solutions to 1,000s of shippers. Click here for more info.