This step focuses on the data foundations required for AI products. AI systems rely on more than models and prompts. They rely on data that is usable, governed, and fit for the needs of AI.

You will learn how data management must evolve for AI, how to turn unstructured and real-time datasets into reusable data products, and how to raise data quality standards to prevent incorrect outputs.

Key points

• Data teams are increasingly pulled into AI governance because AI failure can scale fast and regulations are intensifying.
• Many of the most valuable AI inputs are unstructured, such as documents, images, and audio.
• Data quality becomes more critical in AI because errors can propagate into decisions and user-facing outputs.

Conclusion

Step 4 is about making data usable for AI on purpose: selecting the right inputs, productizing unstructured data, and raising quality and governance so AI outputs remain trustworthy as you scale.