Making AI Models Love Your Data
- By CDOTrends editors
- September 11, 2023
With the booming potential of Generative AI, having the right data can drastically shape the trajectory of a business. But how prepared are businesses for this next frontier of AI? A recent survey sheds light on this, underscoring the importance of robust data strategies and the challenges faced even by industry frontrunners.
Dataiku and Databricks have revealed critical insights from a survey of 400 senior AI professionals worldwide. These findings reiterate the pivotal role data plays in this ever-evolving landscape.
Over 40% of surveyed businesses acknowledged a pressing need for more data or better strategies to leverage their existing datasets, underscoring the importance of a cohesive data strategy. As the world increasingly relies on advanced AI tools, the quality and accessibility of data will significantly dictate success. Notably, businesses shouldn't hold off on AI adoption awaiting perfect data, since even industry leaders grapple with data-related challenges.
The Databricks Lakehouse Platform offers one answer. The 'lakehouse' approach centralizes all data types, ensuring high data quality and streamlining processes. Collaborations like the one between Dataiku and Databricks minimize data redundancy, thus keeping data consistent and reliable.
One often overlooked yet crucial facet of data management is lineage tracking. Dataiku cites their integration with Unity Catalog, which ensures businesses can trace data's lifecycle, bolstering project efficacy and success rate.
77% of surveyed companies indicated that their data and analytics teams comprise diverse backgrounds. And while collaboration is the cornerstone of progress, fusing the efforts of humans and machines, especially in the vast realm of data, can be daunting.
The labyrinth of platforms organizations use can inadvertently extend timelines, amplify costs, and even deter project commencement. Solutions like the Databricks Lakehouse Platform are heralding a new age of streamlined collaboration, ensuring consistency and fostering cross-departmental innovation. Dataiku further augments this collaboration by seamlessly integrating data experts with domain specialists, spanning the entire project lifecycle.
Centralizing data via the lakehouse model is strengthened when built upon open standards. Testament to the reach and relevance of open standards is the widespread use of tools like MLflow, boasting over 11 million downloads monthly. Such open standards offer businesses agility and adaptability, a must-have in the rapidly progressing domain of Generative AI.
Generative AI’s burgeoning significance in the enterprise domain is undeniable. However, its success hinges on three pivotal facets:
- Recognizing data as a competitive edge, emphasizing the need for a centralized, accessible data platform.
- Making strategic choices between available foundational models and crafting bespoke Generative AI models, the latter being a potential intellectual asset.
- Ensuring regulatory readiness, which entails robust AI governance and thorough monitoring.
The allure of Generative AI is potent, yet businesses must tread carefully with awareness and intent. Central to this journey is a unique dataset, functioning as a competitive lever. While foundational AI models offer a starting point, the report noted that crafting tailored Generative AI models can be the differentiator. Equally vital is ensuring a robust regulatory compliance framework, epitomizing the essence of responsible AI deployment.
Image credit: iStockphoto/PashaIgnatov