Money isn’t buying success in the digital world. Companies sink billions into data integration projects. In fact, businesses now commit up to 25% of their IT budgets toward data interoperability efforts. What do they get? Often, not much. The dream of seamless data sharing keeps hitting roadblocks. For one, organizations face a maze of competing standards, each claiming to be “the one.”  Also, data security adds another layer of frustration. Moreover, teams must navigate an ever-growing list of data protection laws while keeping information both accessible and secure. 

Surprisingly, the biggest obstacle for data exchange and interoperability is not a technical or a financial challenge. On the contrary, nowadays data interoperability is primarily a business problem. Specifically in many cases, businesses do not provide enough definition and context to the data they transfer between systems. As a result, the data is not understood on the receiving end. Hence, too many business systems incorrectly interpret data when it is received from other systems and crosses organizational boundaries. Indeed, these digital messages often get lost in translation. So, let’s cut through this complexity. In this article, I’ll break down the five biggest data interoperability hurdles businesses face today and show you what’s really going on behind the scenes.

What is Data Interoperability and Is It Different from Data Integration?

Before looking at data interoperability challenges, let’s start with two definitions, data interoperability and data integration.

“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

For many years, my perspective as an IT integrator was that these terms were synonymous. Now, I think there is a difference. First, the term “data integration” is more technically focused on the process and the specifications of data formats. 

On the other hand, “data interoperability” takes a more holistic perspective, focused on the end result, “to ensure what is sent is understood”. This is an important distinction because the term, data interoperability, has a measure of success for data exchange. Namely, the success factor for data exchange is when what was sent is correctly interpreted by the receiver. On reflection, I successfully integrated thousands of data interfaces, but I am not sure how many of these interfaces actually achieved data interoperability.

The Five Major Constraints for Organizations Achieving Data Interoperability.

For businesses to successfully compete in a digital world, they need to achieve data interoperability between their partners’ systems and with their internal systems. Consequently, they first have to have a complete understanding of data interoperability challenges in order to mitigate them. Indeed, this is the first step to overcome these constraints. For the remainder of this article, I’ll examine the constraints involved in achieving data interoperability. These include having to adhere to tech standards, compliance with data protection laws, secure access to data, the high costs of integration, and the capability to correctly interpreted data.

1. Adhering to Tech Standards: The Lack Of Standards Frustrates Data Interoperability.

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

The lack of widely adopted data standards is a major obstacle to seamless data exchange. Hence, organizations struggle with incompatible systems that use different data structures, measurements, and protocols. Indeed, this dysfunctional digital landscape leads to costly workarounds, manual data entry, ambiguity, and numerous errors. While industry groups push for standardization, the rapid evolution of technology often outpaces these efforts. As a result, this leaves businesses of all sizes to navigate a maze of competing standards and protocols.

For more discussion on addressing the challenges with data interoperability, see my article, Let’s Breakthrough The Data Interoperability Nightmare: It Is The Best Way To Unlock Supply Chain Innovation. Also, for a detail technical perspective of data interoperability, see acceldata’s article, Data Interoperability: Key Principles, Challenges, and Best Practices

2. Complying with Regulations: Obeying Data Protection Laws and Corporate Policies.

Data Sensitivity: What You Need to Know For Your Business

The regulatory landscape adds another layer of complexity to data interoperability. Organizations must balance the need for seamless data exchange with strict compliance requirements, from GDPR to industry-specific regulations. Corporate policies, designed to protect sensitive information and maintain privacy, often create additional barriers to smooth data flows. Indeed, this constraining regulatory environment causes hesitancy among companies to share sensitive data. At the same time, this data is key information for organizations to obtain interoperability. Worse, zealous data security departments will implement sophisticated governance frameworks that slow down data sharing initiatives. For more information on data protection, see my article, Data Sensitivity: What You Need to Know For Your Business.

3. Securing Access to Data: The Puzzling Challenge to Verify, Authorize, and Authenticate.

Digital Identity In Logistics And What To Know – The Best Security, Scary Risks

Security remains a paramount concern that businesses must address as part of achieving data interoperability. Indeed, modern authentication systems must verify users, authorize access, and protect against breaches while maintaining the smooth flow of data. So, all organizations face the challenge of implementing robust security measures without creating bottlenecks in data access. Further, the rise of sophisticated cyber threats adds urgency to this challenge, requiring constant updates to security protocols and authentication mechanisms. 

For more discussion on balancing security with interoperability, see Strata Identity’s article, Solving the identity puzzle: How interoperability unlocks cloud security potential. Also, for more on authorizing and verifying access within the logistics industry, see my article, Digital Identity In Logistics And What To Know – The Best Security, Scary Risks.

4. Massive Tech Costs: The High Costs of IT Data Interoperability and Integration Projects.

It is truly amazing that data integration projects and services can consume up to 25% of IT budgets. Indeed, the IT costs for businesses to implement each new data integration project is staggering. This is because these types of projects need specialized expertise and custom solutions that drive up expenses. Worse, the complexity of these integration projects often leads to budget overruns. Further, organizations face ongoing costs related to upgrading infrastructure, system maintenance, updates, and staff training. So, businesses must weigh making substantial investments against the uncertainty of actually achieving data interoperability. For more specifics on the factors behind high costs of data integration projects, see StarfishETL’s article, How Much Does Data Integration Cost?.

5. Make the Data Sent Understood: The Absence of Shared Knowledge Across Systems and Organizations to Correctly Interpret Data.

In the lightning-fast world of digital commerce, the seamless flow of data is essential for making informed decisions and driving business success. However, one of the biggest hurdles in data interoperability is ensuring that the data shared is correctly understood when it is exchanged between systems and organizations. So, why do businesses and their systems exchange data that is not understood? See two primary reasons below:

Primary Reasons Data Exchanged Is Not Understood

• Data Integrity Lost When Context and Clear Definitions Are Not Shared Between Systems. This leads to ambiguity and misinterpretation that severely undermines data integrity, leading to a cascade of problems that can ripple through an organization.
• The Enterprise Integration Myth that Data Exchanged is Data Understood. Indeed, the lack of seamless data exchange often lies in the assumption that simply connecting systems is all that is needed.

For a more detailed discussion on this topic, see my article, The Reasons that Data Integrity Loses Value From Dumb Integrations

More References.

• Semantic Interoperability Solutions – Achieving Logistics Interoperability: The Best Way to Breakthrough The Tangle Of Dumb Data Integrations, In this article, topics include semantic interoperability benefits and recommended solutions to improve interoperability by exchanging meaningful data. 
• Steps to Achieve Data Interoperability – Semantic Digital Interoperability: This Is The Ultimate Way To Make Supply Chains Seamless. Here, I detail a 4-step process to enable meaningful, actionable data exchange.

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.

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