NCS Insights

Why is Generative AI Important?
Which Platforms Use AI in the Data?

Artificial Intelligence, often referred to as "AI," has proven to be a valuable resource that is reshaping the landscape in the digital age, influencing various market sectors. In recent years, significant advances in algorithms related to machine learning and deep learning have culminated in the development of generative AI, endowed with the ability to generate content and solutions based on pre-existing data.

The implementation of AI in data management platforms has gained considerable momentum due to the emergence of the big data concept, where the ability to process and interpret large volumes of data has proven to be an essential and crucial element.

Why is Generative AI Important?

• Automation and Efficiency: It allows for the automation of repetitive and complex tasks, freeing up human resources for strategic activities. Studies show that automation can significantly increase operational efficiency and reduce costs.
• Accuracy and Predictability: Capable of identifying hidden patterns and trends in data, generative AI offers more accurate predictions than traditional methods, essential in sectors like finance, healthcare, and marketing, where forecasting can lead to a significant competitive advantage.
• Service Personalization: It enables the creation of highly personalized solutions, adapting products and services to the needs of each customer. This is particularly relevant in areas such as e-commerce and customer service, where personalization can significantly improve the user experience and increase customer loyalty.
• Informed Decision-Making: The ability to analyze large volumes of data and generate insights allows companies to make more informed, data-driven decisions. This can lead to more effective business strategies and the identification of new market opportunities.

Which Platforms Use AI in the Data?

• Databricks: Uses generative AI to optimize data transformation and analysis. The platform integrates Machine Learning operations (MLOps) and support for large language models (LLMs) within SQL queries and Spark ETL pipelines. This enables companies to automate complex processes and perform advanced analyses more efficiently. Generative AI in Databricks facilitates the creation of new synthetic data, predictive modeling, and insight generation, enhancing the platform's analytical capabilities.
• IBM Watson: A platform with a variety of AI services, including virtual assistants, data analysis, and content generation. Watson integrates tools such as Watsonx.ai for model training and Watsonx.data for data management, helping companies create personalized experiences and monitor the accuracy and ethics of AI models.
• Azure Machine Learning: A comprehensive and integrated Microsoft platform that facilitates the building, training, deployment, and management of machine learning models. It offers a collaborative and scalable environment for data scientists and developers, enabling the creation of high-quality AI solutions. The platform supports a wide range of frameworks and languages, offering tools for automating machine learning processes (MLOps), which improves efficiency and reduces development time. Additionally, Azure Machine Learning provides robust features for model experimentation, monitoring, and governance, ensuring data compliance and integrity.
• Appen: Provides high-quality data for the development of AI applications. Its modular annotation tools support text, image, audio, video, and rigorous quality control mechanisms and flexible deployment options.
• AWS (Amazon Web Services): Offers a variety of AI services through the AWS AI/ML platform, including Amazon SageMaker, which facilitates the building, training, and deployment of machine learning models. It also offers services like Amazon Comprehend for natural language processing (NLP), Amazon Rekognition for image and video analysis, and Amazon Lex for creating chatbots and virtual assistants. These services enable companies to integrate AI functionalities into their applications in a scalable and efficient manner.
• Microsoft Fabric: A data platform designed to simplify the collection, transformation, and analysis of data in diverse environments, including hybrid and multicloud. Although not an AI itself, Fabric integrates AI and machine learning technologies to optimize its functionalities. The platform uses AI for tasks such as automating ETL (extraction, transformation, and loading) processes, integrating data from different sources, and predictive analysis, providing more efficient data management and faster insights.

Digital transformation is redefining the market, and the integration of generative AI is at the heart of this revolution. Contact us, as our specialized Data & Analytics team is ready to guide you in optimizing your data, increasing efficiency, accuracy, and discovering new opportunities for your company.

The integration of generative AI in data platforms is transforming the corporate market by optimizing the analysis and transformation of large volumes of data. This technology automates complex processes, improves predictive accuracy, and enables more informed decision-making and service personalization. In addition to increasing operational efficiency and reducing costs, AI facilitates the identification of new market opportunities, being essential for the innovation and competitiveness of organizations.