Efficient Data Preparation for Gen AI with Fivetran

Many organizations are currently using or will soon be using Generative AI (Gen AI). The success of Gen AI depends on how effectively, efficiently, and securely an organization can use its unique datasets with foundation models and Gen AI apps.

Two Key Elements to Prepare for Generative AI

The data readiness for generative AI depends on two key elements:


  • The ability to move and integrate data from databases, applications, and other sources in an automated, reliable, cost-effective, and secure manner.
  • Understanding, protecting, and accessing data through data governance.

This kind of data readiness continues to be overlooked and has historically derailed many attempts to harness the power of big data and data science. One metric indicates that up to 87% of data science projects never reach the production stage. This is often due to siloed and unmanaged data and undeveloped data infrastructure.

Challenges for Generative AI

Let’s take a closer look at what is needed to prepare for generative AI:

  • Data Complexity A diverse and vast amount of data is required for training to answer the various questions users may ask.
  • Data Integration: Scattered data from various sources must be seamlessly integrated.
  • Real-time Training: If there is data that is not updated, the generative AI will not be able to reflect the latest data when providing answers. Therefore, a continuous supply of data is necessary.
  • Data Quality: Data quality must be ensured to generate accurate and consistent content.
Data platform architecture for generative AI

Building generative AI from scratch is a colossal undertaking, with the potential to cost hundreds of millions of dollars and the equivalent of hundreds of years. Your organization is most likely to use a base or foundation model – a commercially available model already trained on huge volumes of public data.


In the initial stages, this architecture mirrors basic analytics use cases, requiring a data pipeline to extract, load and transform raw data into models for supporting reports, dashboards and other data assets.


What comes afterward is unique to generative AI. You can supplement an off-the-shelf generative AI model with your data in two ways:


  • Convert text into enumerations, store in a vector database for generative AI to integrate into long-term memory, enhancing results from initial training and unique organizational data..
  • Combine large language models with knowledge graphs, explicitly encoding semantic understanding into the model, not just statistical word associations.


Even with the help of an increasing number of off-the-shelf tools for managing data infrastructure with generative AI, it is likely that you will need to lean heavily on engineering, data science and AI expertise to make the parts function properly with each other and build usable applications on top of the architecture.


The potential of generative AI can only be fully realized when organizations recognize the pivotal role of their proprietary data. By prioritizing mastery over data through the implementation of advanced data operations technologies and cultivating a culture of responsible data use, organizations can unlock the true power of generative AI, ensuring its optimal performance and ethical deployment in a rapidly advancing technological landscape.

Efficient Data Preparation for Generative AI with Fivetran

Fivetran is a fully automated and fully managed data movement platform that helps deliver usable, reliable, high-quality data for your data workloads. Fivetran provides a fully automated and fully managed platform that centralizes your data, modernizes your data infrastructure, enables greater data self-service, and allows you to build differentiated data solutions like GenAI apps.

  • Fivetran’s 400+ connectors are zero-maintenance and zero-code.
  • Connectors can be set up in just 5 minutes.
  • The data extraction, transformation, and loading process is fully automated and managed with 99.9% uptime.

To learn more about the GenAI approach using Fivetran, BigQuery, and Vertex AI, check out the video below.

ClooConnect, Fivetran

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