*By Rochael Ribeiro Filho
Effective use of data is today the greatest competitive advantage for companies in various sectors. Whether in retail or healthcare, the ability to capture, process and use personal information allows for the creation of a more personalized and relevant experience. However, handling this data requires an approach that not only respects privacy and security, but is also efficient and accurate. This is where data encapsulation and Artificial Intelligence (AI) come in as partners in creating personalized service experiences.
Data encapsulation is a programming principle that allows sensitive information to be kept secure, accessible only by specific methods, and protected from unauthorized alteration. This model provides an extra layer of security by organizing data so that it can be accessed and analyzed by AI systems without compromising user privacy. For institutions such as clinics and hospitals where sensitive health information is handled, encapsulation ensures that data is organized and ready to be used in an appropriate and secure manner.
Turning data into personalized experiences
Artificial Intelligence adds a powerful layer to this framework, processing large volumes of encapsulated data and transforming it into actionable insights for personalized service. This process relies on a solid information foundation that organizes and ensures the quality and integrity of the data captured over time. It is built through careful collection, validation, and categorization, ensuring that the data is complete and ready for use in AI analytics.
In a practical context, such as e-commerce, this foundation allows AI to capture and analyze consumers’ past preferences and behaviors, offering more accurate and relevant product recommendations. In medical settings, the same framework supports analyses based on clinical histories, allowing AI to suggest preventive treatments, tests and even assist in diagnoses, which improves the patient experience and safety. The integration of AI systems into personalized care then becomes a structured and efficient process, capable of responding precisely to the needs of each user.
Successful implementation of AI in personalized care begins with building a solid foundation of information. With this framework in place, AI systems are integrated to accurately process, analyze, and respond to customer and patient data. By organizing the process into steps, we have a clear roadmap:
- Organization and structuring: The first step involves encapsulating and categorizing the data so that it is accessible in an organized and secure manner.
- AI Tools: Choosing AI solutions that can interpret and adapt to user interactions is essential. These tools analyze behavior patterns and context, enabling more personalized, agile and relevant responses to meet consumer expectations.
- Continuous AI training: With a continuous learning process, the system adjusts its recommendations and stays relevant based on new information. In some cases, this adjustment occurs without the need for new training, as in the RAG (Retrieval-Augmented Generation) technique. The model accesses a specific context of information to generate more accurate and updated responses, without changing the core structure of the system.
- Feedback and optimization: Implementing a feedback process is essential to ensure that personalized interactions are aligned with user expectations. The insights gained through this feedback not only identify areas for improvement, but also enable a fine-tuning process (fine-tuning), where the system relearns based on previous interactions.
The combination of data encapsulation and AI has the potential to revolutionize the way consumers and patients are served. With data protected and organized, AI can create a truly personalized experience that is both secure and efficient. Ultimately, companies and institutions that invest in technologies that promote the ethical and efficient use of data not only stand out, but also build trust with their consumers, generating loyalty and lasting value.
*By Rochael Ribeiro Filho, Sales Engineer Manager at InterSystems Brazil
Notice: The opinion presented in this article is the responsibility of its author and not of ABES - Brazilian Association of Software Companies
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