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What Is Data Interoperability?

Data interoperability is the ability to exchange and process data among different systems and business processes. Key to understanding data interoperability is that people can discover, access, and process data when, where, and how they need it. For different stakeholders and systems to use interoperable data in different ways without changing the data needs to be trusted data — meaning unified, cleansed, standardized, and enriched to support different use cases.

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How Data Interoperability Is Different From The Single Source Of Truth

A single source of truth focuses on the accuracy and consistency of data in a domain. Interoperable data is a broader concept, focused on giving different stakeholders the ability to access and use data. To make this possible, the data should be both accurate and consistent, yet rich enough—including attributes like interactions and transactions—to serve different stakeholders’ needs. In a way, a single source of truth could be considered a secondary but critical component of data interoperability.

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Benefits Of Using Interoperable Data

Data interoperability offers several advantages when implemented across industries:

  • Improved Efficiency:
    By seamlessly integrating data across different systems, organizations can eliminate manual processes, reduce data duplication, and streamline operations.
  • Enhanced Collaboration:
    Interoperable data fosters collaboration among different stakeholders, enabling them to access and share relevant information in real-time, leading to better decision-making and innovation.
  • Better Insights:
    Cross-industry data integration provides a holistic view of information, enabling organizations to gain actionable insights, identify trends, and make data-driven decisions.
  • Increased Agility:
    Interoperable data allows organizations to quickly adapt to changing market conditions, customer demands, and regulatory requirements, giving them a competitive edge.

By embracing interoperable data, organizations can unlock the full potential of their data assets and drive digital transformation across industries.

Importance of Interoperability In Data Management

Interoperability is vital for effective data management in today's interconnected business environment. It allows organizations to break down data silos, connect disparate systems, and achieve a unified view of their data. By enabling data sharing and collaboration, interoperability facilitates informed decision-making, enhances operational efficiency, and drives innovation.

Benefits of Achieving Interoperability:

The benefits of achieving interoperability extend beyond improved data management. Organizations that successfully implement interoperable data solutions experience:

  • Enhanced Data Quality: Interoperability ensures consistent and accurate data across systems, reducing errors, redundancies, and inconsistencies.
  • Efficient Data Integration: By seamlessly integrating data from various sources, interoperability eliminates the need for manual data entry and improves data processing speed.
  • Streamlined Workflows: Interoperable systems enable smooth data flow, automating workflows, and minimizing interruptions, leading to increased productivity.
  • Better Customer Experience: Interoperability empowers organizations to provide a holistic view of customer data, enabling personalized experiences and improved customer satisfaction.
  • Accelerated Innovation: Interoperable data fosters collaboration, allowing organizations to leverage data from diverse sources to uncover insights, drive innovation, and gain a competitive edge.
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Interoperable Data And Metadata

Metadata is information about data. Since machines require metadata to understand data, metadata is an inseparable part of interoperability. When data and metadata can be consistently exchanged and interpreted across different systems—again, not only data itself but its context—interoperability is possible.

Interoperability Of Data And Data Sets

Metadata is also information about data sets. Because data sets carry their own metadata, they, too, are interoperable elements. Like data, they are searchable and understandable by machines. And like data, they can be exchanged and integrated for analytics, reporting, and decision-making. One way to understand this is by treating data sets as data products, with their own life cycles, formats, markers of quality, lineages, owners, use rights, etc. Metadata lets data be exchanged, consumed, and understood just like any other product.

Interoperability Challenges

Interoperability challenges are a common hurdle faced by organizations when it comes to achieving data interoperability in different industries. These challenges arise due to various factors and can hinder the seamless exchange and integration of data between systems and applications. Let's explore some of the common obstacles and their impact.

Common Obstacles in Achieving Data Interoperability:

  1. Lack of standardized data formats: One of the major obstacles is the absence of standardized data formats across different systems and applications. Each industry often has its own unique data formats, making it difficult to establish a unified approach for data exchange.
  2. Disparate data sources: Organizations frequently deal with data from multiple sources, such as legacy systems, third-party vendors, and cloud-based platforms. These disparate data sources often have varying data structures and schemas, making data integration and interoperability complex.
  3. Data quality issues: Inconsistent data quality can also pose a significant challenge to achieving interoperability. Data inconsistencies, such as missing or incorrect values, duplicate records, or outdated information, can hinder the effective exchange and utilization of data.
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Impact of Data Format Inconsistencies:

Data format inconsistencies can have several negative consequences:

  1. Data integration delays: Incompatible data formats require additional time and effort to transform and map the data correctly, leading to delays in data integration projects.
  2. Increased risk of errors: Manual data transformation and mapping processes increase the risk of errors, such as data loss or data corruption, which can have serious consequences for businesses.
  3. Reduced data quality: Inconsistent data formats can lead to data quality issues, affecting data accuracy, completeness, and reliability.

Solutions For Addressing Interoperability Challenges:

  1. Adopt industry standards: Organizations should strive to adopt industry-standard data formats and schemas to facilitate seamless data exchange and integration.
  2. Implement data unification and management: Leveraging modern data unification and management solutions, such as Reltio, can help overcome interoperability challenges by providing data integration, standardization, cleansing, and enrichment capabilities.
  3. Data governance and quality management: Establishing robust data governance frameworks and implementing data quality management processes can help address data quality issues and ensure the accuracy and consistency of data.

By addressing these interoperability challenges, organizations can unlock the full potential of their data and enable smoother data exchange and collaboration across different industries.

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What Data Interoperability Requires

Generally, interoperable data systems and processes share four requirements:

  • Data standardization
    By adopting standard data formats, coding systems, and communication protocols, data can be easily understood by different systems.
  • Semantic interoperability
    Semantic interoperability means that different systems interpret data in the same way, which prevents errors during data exchange.
  • Interconnected systems
    Interoperable data requires linked systems that can easily connect and communicate with each other through APIs, or Application Programming Interfaces.
  • Data exchange
    Interoperability makes it possible to exchange data across different systems without losing meaning or structure. Data exchange can occur in real time or through batch processes.

The Levels Of Data Interoperability

From data transmission protocols up to policy and cultural behaviors, interoperability requires effort across an entire organization. By creating services and systems that are able—and understand how and why—to share data sets, true interoperability is as much a feature of organizational collaboration as it is a technical ability. From basic to advanced, the levels of interoperability are:

  • Foundational
    Foundational interoperability focuses on establishing protocols that allow systems to  exchange data. It’s more concerned with basic communication than how these systems interpret data.
  • Structural
    This level is about syntax, or standardizing the format and structure of how systems interpret and process exchanged data. Here, machines can recognize what different data sets are and how they structurally relate.
  • Semantic
    Semantic interoperability gives data common meaning and interpretability across systems. Standardized terminologies, ontologies, and vocabularies, along with standardized structures, give systems a way to interpret their shared data.
  • Organizational
    While the organizational level sits outside any technical definition, it’s worth including because an organization’s governance, culture, legal requirements, and other security considerations can integrate workflows, foster mutual trust, and enable interoperability.

Data Interoperability Testing, Step By Step

Interoperability testing is an important part of measuring how well multiple systems exchange and process data before launching them in the real world. Follow these general steps to test interoperability:

1. Define test objectives
Decide what you’re testing and why. Which systems are involved? What data will be exchanged? What are the expected outcomes of the test?

2. Create test cases
Develop full test cases that cover a range of conditions, including the different types of data, data formats, and communication protocols.

3. Execute
Conduct the test in a controlled environment. Monitor the data-exchange process, analyze the results, and identify any issues.

4. Iterate
Refine the test to resolve any issues that come up. This may mean refining test cases, adjusting system configurations, or enhancing data-mapping processes.

5. Document
Record the testing process, test cases, results, and any refinements for future reference.

Best Practices for Ensuring Interoperability

When it comes to achieving interoperability in different industries, there are several best practices that can help organizations streamline their data exchange processes. By implementing these practices, businesses can ensure seamless communication between systems and facilitate the sharing of data across various platforms. Let's explore some of the key strategies:

Data Standardization and Normalization Techniques: One of the fundamental aspects of achieving interoperability is establishing consistent data formats and structures. By standardizing and normalizing data, organizations can ensure that information is formatted in a uniform manner, making it easier to exchange and interpret across systems. This involves defining data models, establishing naming conventions, and mapping data fields to a common framework.

Implementing Interoperability Frameworks and Protocols: To enable smooth data exchange between different systems, it is crucial to adopt interoperability frameworks and protocols. These frameworks provide a set of guidelines and standards that facilitate the integration and sharing of data. By adhering to industry-standard frameworks such as HL7, FHIR, or EDI, organizations can ensure compatibility and seamless interoperability between their systems and those of their partners.

Strategies for Seamless Data Exchange Between Systems: Achieving interoperability involves more than just establishing data standards and adopting frameworks. It also requires implementing strategies to enable seamless data exchange between systems. This can be achieved through the use of application programming interfaces (APIs), data integration platforms, or middleware solutions. These technologies enable data to flow seamlessly between different applications, databases, and systems, ensuring that information is synchronized and up-to-date across the organization.

Interoperability Across Various Industries

Interoperable data is revolutionizing various industries, enabling seamless integration and collaboration among different organizations. Here are some examples of industries that are benefiting from data interoperability:

  1. Healthcare: In the healthcare industry, interoperable data allows medical professionals to access patient records, test results, and treatment plans across different healthcare systems. This enhances care coordination, reduces medical errors, and improves patient outcomes.
  2. Retail: Data interoperability in the retail industry enables a unified view of customer data, inventory management, and supply chain operations. Retailers can optimize their operations, personalize customer experiences, and make data-driven decisions to drive business growth.
  3. Finance: Interoperability in the finance industry facilitates seamless data sharing between banks, insurance companies, and financial institutions. This enables secure and efficient transactions, fraud detection, risk assessment, and compliance with regulatory requirements.
  4. Manufacturing: Data interoperability in manufacturing streamlines processes across the supply chain, from procurement to production and distribution. This enables real-time visibility, efficient resource allocation, and predictive maintenance, leading to improved productivity and cost savings.

These are just a few examples of how data interoperability is transforming industries. Let's explore some successful use cases of interoperability implementation:

  1. Health Information Exchanges (HIEs): HIEs enable the secure exchange of patient health information among healthcare providers, improving care coordination and reducing duplication of tests or procedures.
  2. Internet of Things (IoT) in Retail: Retailers leverage IoT devices and data interoperability to optimize inventory management, track product performance, and provide personalized shopping experiences.
  3. Open Banking: Open banking initiatives promote data interoperability among financial institutions, allowing customers to securely share their financial data with authorized third-party providers to access innovative financial services.

Why Data Interoperability Is Important In Healthcare

Data interoperability is like a universal language that lets different systems communicate equally among each other. This ability matters a great deal in healthcare, whose different systems share a common goal of healing people and saving lives. Given the complexity of healthcare systems, establishing a universal language among hospitals, clinics, labs, pharmacies and the like leads to fewer opportunities for mistakes and better patient care. Interoperable data in healthcare ensures that vital patient information can be easily shared, understood, and used across different healthcare settings, supporting:

  • Better patient care
    Most people visit different doctors or practitioners who know a lot about a specific medical condition or practice and much less about fields beyond their area of expertise. But patient conditions, behavior, and histories rarely fit into such silos. Interoperable data gives healthcare providers a broader view of people’s medical history, test results, medications, and treatment plans—all of which deliver better patient outcomes.
  • Efficiency and accuracy
    Interoperable data lowers the chances of errors and duplication. For example, when doctors spend less time reviewing old records or repeating tests thanks to interoperable data, they can work more efficiently without missing crucial information.
  • Faster responses
    In emergency situations, quick access to someone’s medical history and vital information can save their life. Interoperable data helps emergency responders and hospitals know crucial details, such as allergies or existing conditions, to deliver the right care fast.
  • Cost savings
    Interoperable systems can cut down on unnecessary healthcare costs by eliminating redundant procedures and minimizing the time it takes to retrieve and manage health data.
  • Research and public health
    Aggregated, interoperable data can be anonymized and used for research to improve treatments, understand diseases better, and even track public health trends. On a broader scale, it contributes to medical advancements and healthcare practices.

What Makes Data Interoperability So Difficult In Healthcare?

Establishing data interoperability in healthcare is particularly difficult, because it’s about adopting a universal language in an industry where different languages are spoken by default. Such challenges include:

  • Diverse systems
    Healthcare uses diverse software and systems that often don't, or can’t, talk to each other. One system might store data differently. Another uses proprietary codes and formats. It’s an operational and technical challenge to help systems understand each other's languages enough to smoothly share information.
  • Privacy and security concerns
    Patient data is incredibly sensitive, for good reason, and balancing data-sharing with patient privacy is inherently complex. When healthcare providers share information between systems, they must do so securely and within the bounds of privacy rules—HIPAA in the U.S., for example.
  • No universal standard
    As of today, there's no universal standard for structuring and sharing healthcare data, much less a national patient identifier that links patients to their data. Many hospitals or systems use their own standards, and no legislation yet establishes a single standard. This makes it hard to create a common language for all systems to understand and exchange information.
  • Time and budget
    Many healthcare organizations use outdated systems that can’t easily communicate with modern technology.

How Reltio Delivers Interoperable Data

Reltio enables overall business responsiveness through trusted real-time data. Reltio provides trusted core business data by unifying, cleansing, and enriching it so it can be used/reused across different systems and business processes. Reltio’s AI‑powered data unification and management capabilities, which include a cloud‑native master data management platform, help business leaders improve their market responsiveness by quickly giving them trusted, universally understood and useful data when, where, and how they need it.

By giving its clients the ability to integrate accurate, standardized data within a wide range of systems, and helping them ingest and share this data in real time across their entire business, Reltio helps organizations respond to their customers’ needs, make the most of market opportunities, manage risks, and grow. For IT and data leaders, Reltio offers a flexible solution giving them the ability to adapt to changing business needs and technology requirements. Customers can incrementally enhance their solutions and data products without disruption.

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