Data standardization in AI: from canonical forms to normalized models

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Introduction

Standardized representation of data is essential for developing and implementing effective artificial intelligence systems. This standardization, also called "canonical form" or "normalized model," creates uniform, simplified and optimized representations of data, algorithms and structures.

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Based on mathematical and computer science principles, this approach is crucial in the field of AI, especially considering the increasing complexity and integration of modern technologies.

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The concept of data standardization in AI

The term "canonical" is derived from the concept of "canon," which indicates a widely accepted rule or standard. In computer science, "canonicalization" is the process of converting data that has multiple possible representations into a "standard" or "normalized" form[^1]. As explained on Wikipedia, this process is essential when comparing different representations for equivalence, reducing repetitive calculations, or imposing meaningful order[^2].

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In 2025, with the expansion of AI into many areas, standard data models (or Canonical Data Models - CDMs) have become crucial tools for:

• Facilitate seamless integration of data from disparate sources
• Ensure interoperability between different systems and applications
• Simplify data processing and analysis within AI systems[^3].

A standard data model functions as an intermediary between different systems, offering a common format instead of relying on direct point-to-point communication between systems[^4].

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Practical applications in modern AI architectures

1. Data integration and interoperability

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In modern business systems, the integration of data from disparate sources is a significant challenge. Standard data models provide a framework for representing entities and relationships in their simplest form, facilitating communication between heterogeneous systems[^5].

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For example, an online learning application could integrate data from student registration, course registration and payment system subsystems, each with its own formats and structures. A standardized model can define common fields (student name, ID, email, etc.) in an agreed format such as XML, JSON or others, significantly reducing the number of data translations needed[^6].

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2. Optimization in machine learning

Standardized forms play a crucial role in optimization problems central to many machine learning algorithms. In 2025, the most advanced AI models use unified representations for:

• Structure constraints and objective functions in standardized formats
• Simplifying computational processes
• Improve efficiency in solving complex problems[^7]

3. Neural networks and advanced deep learning

By 2025, the evolution of AI architectures has led to significant advances in the reasoning capabilities and quality of "frontier" models[^8]. According to Microsoft, these developments are based on standardized forms applied to:

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• Optimized Neural Networks using weight normalization
• Models with advanced reasoning skills that solve complex problems through logical steps similar to human thinking
• Active inference systems that optimize model evidence by minimizing variational free energy[^9]

These standardized approaches make it possible to significantly reduce the number of parameters, improve computational efficiency, and better manage the increasing complexity of big data.

4. Feature representation and dimensionality reduction

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Standardized representations are also widely used for:

• Transforming feature representation problems into matrix proximity problems
• Apply minimization techniques for learning structured embedding
• Implement dimensionality reduction methods such as principal component analysis (PCA)

These approaches allow preservation of essential data features while reducing computational complexity[^10].

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Advantages of standardized representations in AI software

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The implementation of standardized models in AI offers numerous advantages:

1. Uniformity: Provides a consistent framework for representing and manipulating data and algorithms
2. Efficiency: Streamlines computational processes and optimizes resource utilization
3. Interoperability: Improves the ability of different systems and components to work together smoothly
4. Scalability: Facilitates the management of complex data structures and large-scale applications
5. Optimization: Enables more effective optimization of models and algorithms
6. Compression: Supports model compression techniques, crucial for implementing AI in resource-constrained environments[^11]

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Applications in 2025: Concrete cases of standardization in AI

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Advanced visual recognition

Companies in the fashion industry use standardized convolutional models to automatically classify garments. These models allow parameter reduction while maintaining high accuracy, enabling implementation on devices with limited resources[^12].

Multilingual natural language processing

Banking services implement standardized language models for sentiment analysis in customer reviews. These representations allow for effective handling of dialect and multilingual variants, significantly improving the accuracy of analysis[^13].

Optimization of supply chains

Automotive manufacturers use standardized optimization algorithms for supply chain management. This approach reduces computation time and enables real-time adjustments, improving overall operational efficiency[^14].

Advanced medical diagnostics

Hospitals implement decision support systems based on standardized representations for medical image interpretation. This standardization improves interoperability between different departments and increases diagnostic accuracy, leading to more timely and personalized treatments[^15].

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Future trends of standardization in AI

In 2025, we are seeing several emerging trends in data standardization for AI:

1. Agentic AI: According to MIT Sloan Management Review, agentic AI-systems that perform tasks independently-is considered one of the most important trends of 2025. These autonomous and collaborative systems require standardized representations to communicate effectively with each other[^16].
2. Increased focus on unstructured data: Interest in generative AI has led to an increased focus on unstructured data. According to a recent survey, 94 percent of AI and data leaders say interest in AI is leading to a greater focus on data, particularly unstructured data such as text, images, and video[^17].
3. Advanced reasoning models: Models with advanced reasoning capabilities, as highlighted by Microsoft and Morgan Stanley, use standardized representations to solve complex problems with logical steps similar to human thinking, making them particularly useful in fields such as science, programming, mathematics and medicine[^18][^19].
4. Regulatory Standardization: With the introduction of the EU AI Act and other regulations, standardization practices are taking on an increasingly important role in ensuring that AI development is ethical, transparent, and compliant with current regulations[^20].
5. Energy efficiency: Standardized models are helping to improve the energy efficiency of AI systems, a crucial aspect considering the growing concern about the environmental impact of AI[^21].

Conclusion

Standardized representations are a fundamental approach to optimizing various aspects of systems. From data models to neural network architectures, these forms provide a structured, efficient and interoperable framework essential for advancing AI technologies.

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The adoption of standardization practices in AI is driving innovation in key sectors such as manufacturing, finance, and healthcare, helping to position AI development and application at the forefront. The challenge for the future will be to balance rapid innovation with the need for standardization and regulation, ensuring that AI remains a tool in the service of humanity, guided by ethical principles and shared values[^22].

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As this field evolves, it will be critical for researchers, developers and policymakers to work closely together to shape a future in which standardized AI can realize its full potential while maintaining public trust and confidence.

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Sources

[^1]: "Canonicalization - Wikipedia," https://en.wikipedia.org/wiki/Canonicalization

[^2]: "Canonical form - Wikipedia", https://en.wikipedia.org/wiki/Canonical_form

[^3]: "What Is a Canonical Data Model? CDMs Explained - BMC Software | Blogs," https://www.bmc.com/blogs/canonical-data-model/

[^4]: "Canonical model - Wikipedia", https://en.wikipedia.org/wiki/Canonical_model

[^5]: "Canonical Models & Data Architecture: Definition, Benefits, Design," https://recordlinker.com/canonical-data-model/

[^6]: "Canonical Data Models (CDMs) Explained | Splunk," https://www.splunk.com/en_us/blog/learn/cdm-canonical-data-model.html

[^7]: "Data Normalization Explained: An In-Depth Guide | Splunk," https://www.splunk.com/en_us/blog/learn/data-normalization.html

[^8]: "What's next for AI in 2025 | MIT Technology Review," https://www.technologyreview.com/2025/01/08/1109188/whats-next-for-ai-in-2025/

[^9]: "6 AI trends you'll see more of in 2025", https://news.microsoft.com/source/features/ai/6-ai-trends-youll-see-more-of-in-2025/

[^10]: "Canonical Models: Standardizing Data Representation," https://elsevier.blog/canonical-models-data-representation/

[^11]: "Canonical Data Model - Definition & Overview," https://www.snaplogic.com/glossary/canonical-data-model

[^12]: "AI in 2025: Building Blocks Firmly in Place | Sequoia Capital," https://www.sequoiacap.com/article/ai-in-2025/

[^13]: "The State of AI 2025: 12 Eye-Opening Graphs - IEEE Spectrum," https://spectrum.ieee.org/ai-index-2025

[^14]: "AI's impact on healthcare is poised for exponential growth", https://stats.acsh.org/story/artificial-intelligence-in-2025-key-developments

[^15]: "AI in the workplace: A report for 2025 | McKinsey," https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/superagency-in-the-workplace-empowering-people-to-unlock-ais-full-potential-at-work

[^16]: "Five Trends in AI and Data Science for 2025 | MIT Sloan Management Review," https://sloanreview.mit.edu/article/five-trends-in-ai-and-data-science-for-2025/

[^17]: "2025 and the Next Chapter(s) of AI | Google Cloud Blog," https://cloud.google.com/transform/2025-and-the-next-chapters-of-ai

[^18]: "5 AI Trends Shaping Innovation and ROI in 2025 | Morgan Stanley," https://www.morganstanley.com/insights/articles/ai-trends-reasoning-frontier-models-2025-tmt

[^19]: "8 AI Trends To Look Out For in 2025," https://www.synthesia.io/post/ai-trends

[^20]: "January 2025 AI Developments - Transitioning to the Trump Administration | Inside Government Contracts," https://www.insidegovernmentcontracts.com/2025/02/january-2025-ai-developments-transitioning-to-the-trump-administration/

[^21]: "Request for Information on the Development of a 2025 National Artificial Intelligence (AI) Research and Development (R&D) Strategic Plan," https://www.federalregister.gov/documents/2025/04/29/2025-07332/request-for-information-on-the-development-of-a-2025-national-artificial-intelligence-ai-research

[^22]: "Request for Information on the Development of an Artificial Intelligence (AI) Action Plan," https://www.federalregister.gov/documents/2025/02/06/2025-02305/request-for-information-on-the-development-of-an-artificial-intelligence-ai-action-plan