In the digital age we live in, Generative Artificial Intelligence and Data Analysis are completely revolutionizing content generation. This change affects multiple sectors and industries, from commerce to scientific research.

In this context, Generative AI is proving to be a revolutionary technology, promising to transform the way we approach Data Analysis.

Given the popularity of this topic, we at Drive2Data wanted to contribute by answering some popular questions on the subject. In recent projects we have dedicated a lot of time and resources to the development of new tools that “communicate” directly with a large amount of data.


Generative AI, a branch of Artificial Intelligence, focuses on creating original data or content instead of analysing or classifying existing data.

Traditional Machine Learning algorithms operate on pre-existing data to make predictions or classifications. Instead, generative models attempt to generate new data with specific statistical or semantic properties.

Advanced techniques like Generative Neural Networks (GANs) and Recurrent Neural Networks (RNNs) enable machines to learn from data and generate new information.


Generative Artificial Intelligence systems fall under the broad category of General Artificial Intelligence (AGI) and Machine Learning (ML).

They have the potential to change the way we approach content creation, including:

  • Data Augmentation. This is a technique in ML model training, as it can help improve model performance and prevent overfitting. Using generative AI, new data samples can be generated to increase the diversity of the training set. In this way the entire capacity of the model is improved.
  • Creation of personalized content. Generative AI can be used to create personalized content based on data collected about individual users.
  • Image and video analysis. Generative neural networks have proven to be extremely effective at generating realistic images and videos. This feature can be leveraged in image and video analysis. For example, for synthesizing medical images for training purposes or generating simulated videos for testing object detection algorithms.
  • Text analysis. In text and language analysis, generative AI can be used to generate new texts that maintain the characteristics of the training data. This can be useful for automatically generating descriptions, summaries and creating natural conversations in chatbot applications.

The increase in investments in Generative AI

The McKinsey Report “The state of AI in 2023: Generative AI’s breakout year” reveals that the usage of generative AI is increasingly prevalent and will continue to be so in the future.

North American respondents lead the world in terms of using generative AI at work, with 28% stating that they also utilize the technology in their daily tasks, compared to 24% of European respondents and 22% of Asia-Pacific respondents.

Another intriguing finding is that 40% of companies currently utilizing generative AI plan to increase their investment in AI, thanks to its impact.


Our latest research and developments focus precisely on this field, leveraging innovative tools that enable us to directly interact with large datasets. The objective is to streamline and support all Data Analysis procedures.

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