June 18, 2023
5 min read

What does data interoperability mean in 2023?

The metaphor 'data is the new oil' is more pertinent than ever.

With the explosion of data generation and consumption, understanding and leveraging data has become an absolute necessity. Yet, the true value of data is only realised when it is accessible and compatible across various platforms and systems – this is where data interoperability comes into play. As we traverse through 2023, it’s vital to grasp the evolving significance of data interoperability in our rapidly digitalising world.

At its core, data interoperability refers to the ability of different systems, technologies, and applications to seamlessly exchange, interpret, and utilize data. It dismantles the barriers of data silos, enabling data to flow freely between diverse systems. The advantages of this are extensive – it enhances collaboration, augments decision-making, boosts operational efficiency, and enables new insights that drive innovation.

Data Governance and Security in the Era of Data Interoperability

As we stand in the year 2023, data interoperability has evolved and become more critical due to several crucial factors. These include the rise of distributed work environments, the proliferation of data-driven technologies such as artificial intelligence (AI) and the Internet of Things (IoT), and the increasing necessity for robust privacy and security in data exchanges.

Firstly, the global shift towards remote and hybrid work models has highlighted the crucial role of data interoperability. Teams are scattered across different locations, often using a plethora of platforms and tools. In this scenario, data interoperability is no longer a luxury but a requirement to ensure data consistency and seamless collaboration. It allows the uninterrupted exchange of information, irrespective of the tools or platforms employed, thereby fostering effective team collaboration and improving productivity.

Secondly, the rise of AI and IoT technologies has led to an explosion of data from a multitude of sources. These data are often in different formats and structures, making interoperability essential to amalgamate them for meaningful analysis and utilization. Data interoperability allows AI models to learn from a diverse set of data, leading to more accurate and reliable predictions. Likewise, in the IoT space, interoperability aids in the integration of data from various devices, resulting in smarter, more interconnected systems that can communicate and function seamlessly.

Moreover, the significance of data privacy and security has taken center stage in 2023. With the imposition of stricter data regulations like the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), businesses are now obligated to ensure that their data exchange practices comply with these privacy laws. Data interoperability plays a crucial role in this context, enabling secure data exchange while ensuring adherence to privacy and compliance standards.

Interoperability Technologies and Protocols

To accomplish data interoperability in 2023, organizations are employing innovative tools and strategies. The adoption of open standards and Application Programming Interfaces (APIs) has become commonplace, enabling different systems to communicate in the same 'data language'. Pioneering companies like Togggle are spearheading this transformation, offering groundbreaking solutions to facilitate data interoperability.

But data interoperability in 2023 isn't just about technical compatibility. It calls for a cultural shift towards data sharing and collaboration. Organizations need to cultivate a 'data interoperable mindset', fostering an environment where data can flow freely, securely, and unobstructedly across systems, leading to organization-wide data democratisation.

Data interoperability in 2023 signifies the creation of a seamless, secure, and efficient data ecosystem. It's about leveraging advanced technology and fostering a culture that embraces data exchange. It's about creating a world where data doesn't just exist but communicates and interacts, thereby driving innovation, collaboration, and growth.

Data interoperability, the ability of systems and services to exchange data and interpret the shared data meaningfully, is a critical aspect of modern data infrastructure. In the context of healthcare, for instance, data interoperability facilitates the seamless exchange of patient data between different healthcare providers, resulting in improved patient care outcomes.

Future Outlook: Predictions and Opportunities in Data Interoperability

Gartner, a leading research and advisory company, has identified the top 10 data and analytics (D&A) trends for 2023 that provide insights into the current and future states of data interoperability**1**.

  1. Value Optimization: Most D&A leaders struggle to articulate the value they deliver for the organization in business terms. Value optimization requires an integrated set of value-management competencies, such as value storytelling, value stream analysis, ranking and prioritizing investments, and measuring business outcomes to ensure the expected value is realized.
  2. Managing AI Risk: The growing use of AI exposes companies to new risks, such as ethical risks, poisoning of training data, or fraud detection circumvention. Effective AI governance and responsible AI practices are critical for building trust among stakeholders and catalyzing AI adoption and use*
  3. Observability: Observability is a characteristic that allows the D&A system’s behaviour to be understood and allows questions about their behaviour to be answered. D&A leaders need to evaluate data observability tools to understand the needs of the primary users and determine how the tools fit into the overall enterprise ecosystem.
  4. Data Sharing Is Essential: Data sharing is becoming more critical as it promotes transparency, collaboration and aids in decision-making. It has the potential to create a more inclusive enterprise ecosystem.
  5. D&A Sustainability: D&A leaders are expected to optimize their processes for sustainability improvement. A variety of practices are emerging, such as the use of renewable energy by cloud data centres, the use of more energy-efficient hardware, and the usage of small data and other machine learning techniques
  6. Practical Data Fabric: Data fabric is a data management design pattern leveraging all types of metadata to observe, analyze and recommend data management solutions. It enables business users to consume data with confidence and facilitates less-skilled citizen developers to become more versatile in the integration and modelling process.
  7. Emergent AI: Emergent AI, represented by technologies like ChatGPT and generative AI, will change how most companies operate in terms of scalability, versatility, and adaptability. It will make AI ever more pervasive and valuable.
  8. Converged and Composable Ecosystems: Converged D&A ecosystems design and deploy the D&A platform to operate and function cohesively.
  9. Consumers Become Creators: The time users spend in predefined dashboards will be replaced by conversational, dynamic and embedded user experiences that address specific content consumers’ point-in-time needs. Organizations can expand the adoption and impact of analytics by giving content consumers easy-to-use automated and embedded insights and conversational experiences they need to become content creators.
  10. Humans Remain the Key Decision Makers: Not every decision can or should be automated. D&A groups are explicitly addressing decision support and the human role in automated and augmented decision-making. Efforts to drive decision automation without considering the human role in decisions will result in a data-driven organization without conscience or consistent purpose.

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