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The right data strategy is vital to your AI strategy

In recent years, the world has been captivated by the possibilities of generative AI and Large Language Models (LLMs), thanks in large part to the power and accessibility of models like ChatGPT.

However, it’s important to recognize that the fascination with AI is not new. The field of artificial intelligence has been a subject of ongoing interest and investment for over five decades. While rapid advancements in GPUs, specialized AI hardware, and neural networks have fueled exponential growth in AI innovation, one thing remains constant: data is at the heart of this revolution.

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Published: September 7, 2023

What is the difference between an AI strategy and a data strategy?

Both data strategy and AI strategy are integral to an organization’s success in the modern technological landscape, and yet they serve distinct purposes. A data strategy lays the foundation by ensuring data accessibility, quality, and speed, while an AI strategy focuses on harnessing advanced technologies to drive innovation, solve problems, and achieve strategic objectives. Together, these strategies create a synergy that empowers organizations to thrive in the age of AI.

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 Lake Analytics 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.

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Starburst Galaxy

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  • Query your data lake fast with Starburst's best-in-class MPP SQL query engine
  • Get up and running in less than 5 minutes
  • Easily deploy clusters in AWS, Azure and Google Cloud
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