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

Attention, supply chain leaders! How is your supply chain’s digital transformation going? Maybe not so good? A major reason that many digitization effort get stalled is because systems can’t communicate with each other. Indeed, this problem, known as data integration or data interoperability, is the linchpin for achieving a digital supply chain and recouping your tech investment. For instance, using less paperwork and automating internal processes only goes so far. Positively, without intelligent data exchange between systems and devices, you will not achieve your digitalization objectives.

In this article, I’ll offer you 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 most logistics data remains trapped within departmental silos, each with its own data structure and format. Consequently, most of us are drowning in seas of data that’s hard to fathom, and even harder to act upon. This is because its precious insights are 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. Also, one type of logistics data that is a major interoperability problem is shipment data. For a better insight on shipping data interoperability challenges, see my article, Poor Shipping Data Analytics – Here Are The 4 Reasons Impeding High Tech Visibility And Optimization.

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 all of its 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 of 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 achieve both confidence in the data exchanged and to trust digital network partners, software agents, and devices.  

All three of these components are needed to achieve data interoperability. Let’s break this down why each of these 3 data interoperability components are needed. First, without mutual trust data becomes suspect and bad actors such as hackers can compromise a supply chain network. Second, without shared meaning data is not actionable, or worse, is misinterpreted. Lastly, of course, without effective data transfer capabilities, all we have are data silos that yield limited insights within a fragmented supply chain. Below, 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 process such as Extract, Transform, Load (ETL) to integrate data.  Nowaday, most organizations have some level of capability for data-level interoperability.

Now for a given data integration project, the decision on which type of data transfer method to use will depend on various factors. This includes things like data set size, in-house IT expertise, data transfer types available from data source, 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

a. More Complex Systems Generating More Data That Gets Lost in Translation.

Today, the challenge 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.

b. Semantic Interoperability is a Problem for Business to Solve.

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. Specifically, we need to ensure that the meaning of the data is clear to any receiving business or system. Hence, a business person should lead data interoperability initiatives, while technology is the enabler for businesses to achieve seamless information exchanges. Thus, businesses are then faced with the question: “How do we achieve semantic-level interoperability?” Based on my years of experience working with thousands of shippers and third-party logistics (3PL) providers, it’s essential that four key actions take place. These are:

Business-Led Improvement Areas to Achieve Semantic Interoperability
  • Adopt a Data-Centric Mindset; Stop Being Application-Centric.
  • 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 to have trust in who or what is the source of data. Further, for data owners it is increasingly key that they know who and what is accessing their data. Of course, one reason for this need for trusted interoperability is to comply with regulations and to protect organizations from bad actors such as hackers. 

However, another reason for trusted interoperability requirements is that there is an increasing need for businesses to share and receive 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. Thus, there is an increasing need to leverage digital identity technologies. 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: 

Trusted Interoperability – What Supply Chain Leaders Need to Know
  1. Why Is Digital Identity Important To Supply Chains’ Security, Operations, And Financials?
  2. The Need to Verify, Authorize, and Authenticate the Growing Number of Supply Chain Entities.
  3. Digital Identity Risks In The Supply Chain: Mitigating The Unique Dangers.
  4. Digital Identity Systems And Standards In The Supply Chain.
Conclusion.

In summary, for logistics leaders to achieve 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 method to achieve mutual trust between systems, users, and devices.

For more from SC Tech Insights, see the latest articles on Information Technology and Interoperability.

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