AI-era platforms must be able to handle unstructured data, adapt to various problem sets, and excel at filtering data. They should also support multimodal AI, managing not just text data but also video, audio, and image data. Another requirement is explainability, as AI outcomes are less interpretable than traditional big data workflows. Companies must also keep pace with rapid AI innovation. The article concludes by emphasizing that integrating these platforms is not a plug-and-play solution, but a holistic transformation that affects technology, people, and workflows.
Key takeaways:
- Legacy big data platforms may struggle with the complexity and scale of unstructured data, limiting their effectiveness in supporting AI workflows.
- Companies must evaluate whether to extend their existing big data platforms with AI add-ons or invest in platforms built from the ground up for AI.
- AI-era platforms must be able to handle unstructured data, filter it effectively, support various types of data, and adapt quickly to new advancements.
- Successful AI implementation requires handling the complexities of AI, incorporating human oversight, ensuring data quality, and swiftly adapting to new advancements, which may require significant cultural and operational shifts within a company.