What is a Data Flywheel?

A Data Flywheel in the context of AI is a self-reinforcing loop where data, AI models and product usage continuously feed and improve each other. This helps AI services, applications and agents gain momentum over time, just like a real flywheel, improving quality and business value while cutting costs.

For example, a data flywheel for a generative AI customer support chatbot can learn new ways users phrase questions, resulting in improved routing and resolution accuracy and reduced ticket escalations. This would make it more widely adopted across teams, generating even more data for continued improvement.

Why is a Data Flywheel Important?

The data flywheel effect creates a compounding advantage for AI applications. As more data comes in, the LLM improves, thus improving the AI application. This results in higher business value, with more attracted and retained users. Consequently, more users result in more data, further spinning the flywheel. 

Benefits of Implementing a Data Flywheel

• Continuous Model Improvement – As new data flows in from users or systems, AI models become smarter over time without manual re-training. This supports higher accuracy, better generalization and resistance to concept drift. This also allows for future-proofing the application, as it is always updated with relevant data.

• Faster Innovation Cycles – The data → model → product → new data loop enables rapid experimentation. Organizations can deploy updates faster, validate features with real-world feedback and then iterate AI products continuously. 
• Scalable Personalization – The flywheel supports hyper-personalized experiences that get better the more users engage. These could include smarter recommendations, adaptive UIs, and more relevant predictions.
• Lower Operational Costs – Once in motion, a flywheel reduces reliance on manual data labeling (via self-supervision or active learning), expensive rule-based updates and overly large teams to maintain model performance. It also makes infrastructure use more efficient, especially with shared feature stores or automated pipelines.
• User Growth and Engagement – A better product experience attracts and retains more users, generating more data, thus improving the product even further. This creates a moat around your AI solution.

Strategic Data Asset Creation – With the system constantly turning usage into valuable, proprietary training data. Organizations strengthen long-term IP, supporting future AI initiatives across the organization.

How Does the Data Flywheel Work?

A data flywheel is made up of multiple components, used to run the service or agent, gather feedback, feed it back and orchestrate the process. Here’s how it works:

Step 1: Data Generation – Business, inference and monitoring data are ingested by the app. For example, when AI application users interact with a product (e.g. an app, recommendation engine, chatbot). These interactions create valuable data, like clicks, preferences, behaviors, errors, etc., used as the flywheel database.

Step 2: Model Training & Improvement – The data is fed back into AI/ML models. The models are retrained or fine-tuned, improving predictions, personalization, or automation. An orchestrator like MLRun can run this entire process, capturing logs, orchestrating feedback and monitoring deployed models.

Step 3: Better Performance – Improved AI models provide more accurate, helpful, or engaging experiences. This could result in better recommendations, smarter chatbots, faster automation, lower use of compute resources, etc., i.e high business value. Then, users respond positively to the app’s better performance. This means even more usage, more trust and more interactions.

Step 4: More Data – The resulting higher engagement leads to even more data, and ideally higher-quality data, which restarts the loop.

Here’s an example of a data flywheel powered by MLRun and NVIDIA that trains small models based on large model performance:

How Do Data Flywheels Help Scale AI?

Data AI flywheels boost operational efficiency through automation and reusable infrastructure, lowering costs and complexity. As performance improves, user experience and trust increase, attracting more users and unlocking new use cases. These network effects accelerate growth, helping AI systems scale.

In addition, this process can be easily replicated for multiple models, workflows and services, addressing real enterprise needs.

What are the Key Components of a Data Flywheel?

What is needed to keep the flywheel spinning?

• AI Pipelines – Data in, development, application and LiveOps pipelines that operationalize GenAI or AI applications or agents and monitor their performance. This includes data ingestion, preprocessing, model development, training, evaluation, deployment, and monitoring.

• Feedback Loop – An architectural process that captures signals from the evaluation and monitoring process and feeds it back into the pipeline for retraining or fine-tuning.

• Orchestrator –  The orchestrator is the central “brain” of the flywheel. It manages, coordinates, and automates the entire process, deciding when to re-train, which data to use, how to deploy and when to roll back or escalate an issue.

Getting Started with a Data Flywheel: Best Practices

Here’s how to build and implement a data flywheel in your organization:

1. Start with a Use Case: Choose a focused, valuable use case where AI can deliver clear wins. For example, AI copilots or chatbots or model training of smaller models. Pro tip: Make sure it’s narrow enough to gather consistent, actionable data early on.
2. Set Up Data Collection Early: Ensure you have logs or other instrumentation set up to capture the data that will be used for improving the application or agent.
3. Create a Feedback Loop: Set up the evaluation, training and monitoring components and the  guardrails that will be used to process the data and determine updates and redeployments.
4. Orchestrate: Connect the various components with an orchestrator, like MLRun, that streamlines the process and runs the flywheel. MLRun can orchestrate log collection, running evaluation and monitoring, updating models as needed and redeploying automatically.
5. Monitor: After redeployment, continue to evaluate performance, integrity, operational stability, resource use, and more to keep the flywheel spinning.

Data Flywheels for Production with Iguazio’s MLRun and NVIDIA NeMo Microservices

MLRun integrates with NVIDIA’s NeMo microservices and creates a powerful, production-grade infrastructure for managing observable data flywheels. MLRun handles orchestration of data logging, performance monitoring, and pipeline execution, while NeMo provides modular services like LoRA-based fine-tuning, prompt-tuning, SFT, RAG evaluation and LLM-as-a-Judge assessment. Logs captured during inference are stored (e.g., in Elasticsearch), triggering MLRun workflows that invoke NeMo components for retraining or evaluation. This tightly coupled system enables domain-specific model optimization with minimal manual coding, scalable benchmarking and fast redeployment. Read more here.