Data strategy
A data strategy is the blueprint for how an organization manages, leverages, and harnesses its data assets. It lays the groundwork for collecting, storing, processing, and utilizing data to support the organization’s objectives.
- Data is foundational to AI: A data strategy is all about ensuring that the right data is collected, stored securely, and made readily available for analysis and decision-making. After all, data is the foundation upon which AI strategies are built.
- Data Access, Quality, and Speed: A data strategy is responsible for addressing data access, quality, and speed. It defines how data will be accessed, how its quality will be ensured through data governance, and how to optimize data processing speed to derive meaningful insights.
- Legacy Data Lakes: Legacy data lakes exemplifies the data strategy’s role in managing existing data infrastructure. It evaluates whether the current data storage and processing methods are adequate or if improvements, like Starburst’s Data Lake Analytics Platform, are necessary to meet evolving data requirements.
AI strategy
Meanwhile, an AI strategy is centered on leveraging advanced technologies, such as Generative AI, LLMs, and neural networks, to drive innovation and achieve specific business goals. Here’s how it relates to the text:
- AI Innovation: An AI strategy focuses on how AI technologies can be used to solve real-world problems, enhance products and services, and create new opportunities.
- Technology Advancements: An AI strategy embraces these technological advancements and explores how they can be applied to enhance the organization’s AI capabilities. It involves decisions on which AI tools and techniques to adopt, how to integrate them into existing processes, and how to ensure scalability and sustainability.
- AI in Action: An AI strategy identifies specific AI applications, like chatbots or recommendation engines, that align with the organization’s goals. It outlines the steps to implement and deploy AI solutions effectively, ensuring that they provide value and meet user expectations.
How Starburst can help with your data and AI strategy: Enable smarter AI models and efficient AI teams
Data access, quality, and speed are crucial for gaining a competitive edge with AI. While legacy data lakes were designed to retain and analyze “big data,” Starburst’s Data Lakehouse Platform goes further.
It enables users to run analytics up to 10x faster on massive datasets, provides a single point of access and governance for all data sources, and allows access to valuable data beyond the data lake itself. In addition, Data Products in Starburst Enterprise and Starburst Galaxy enable large organizations to curate and serve data reliably across organizational and geographical boundaries for more complete and accurate model training and inference.
Starburst fuels AI and ML innovation faster than ever before.
- For model training: Customers can explore, enrich and organize all their data faster and more efficiently for data analysis, data prep, and model training
- For model serving and inference: Models (e.g. LLMs) can only be as intelligent as the data it has access to. Models can query live data quickly to deliver more accurate and complete responses.
