The text discusses the significance of unstructured data in the context of data processing. It highlights the impacts on compute and revenue for cloud vendors, particularly Snowflake and Databricks. The focus is on the “Unstructured Data Funnel” and the importance of processing data at the object-storage level. The article brings to light the complexities and economic incentives associated with early data processing.
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Introduction
Unstructured data comes in various forms, such as text-heavy content, dates, numbers, and dictionaries. Over 80% of the world’s data is unstructured, making it a significant factor in the data world.
Why is unstructured data important?
GPT Models rely on unstructured data, including text documents, html files, and code snippets. As companies implement LLMs in production, the demand for processing this data increases, making it valuable to vendors like Snowflake and Databricks.
The Unstructured data funnel
The focus on where data processing happens is crucial for cloud vendors like Snowflake and Databricks, as it impacts the computational power needed and, consequently, the costs. Visualizing the data pipeline as a funnel helps understand the non-linear compute requirements at each stage.
Data movement
The first section of the funnel involves data movement, where tools like Fivetran, Portable, or Striim are used for simple transformations like joining streams or de-nesting unstructured data.
Data Lake / Object Storage
The second layer refers to data in object storage, providing a centralized location for data and offering ample compute power for complex processes and complexity reduction.
Data Warehouse / SQL Layer
This layer is suitable for processing structured data or tabular data, but it may not be the most cost-effective for unstructured data processing due to potential double-mark-up on compute.
Data Activation — the “Last mile” of analytics
Data activation involves small checks to ensure processes can kick off and moving cleaned data to systems of operation, representing the final stage of the funnel with minimal compute opportunities.
Conclusion
Unstructured data processing is crucial, especially with the rise of LLMs and GPT models, and the location of this processing within the data pipeline significantly impacts costs. It makes the most sense to focus on processing data at the object-storage level, as illustrated by Snowflake’s movement up the funnel to facilitate unstructured data processing.
AI Solutions for Middle Managers
If you want to evolve your company with AI, stay competitive, and use The Unstructured Data Funnel to your advantage, consider the following practical AI solutions:
- Identify Automation Opportunities: Locate key customer interaction points that can benefit from AI.
- Define KPIs: Ensure your AI endeavors have measurable impacts on business outcomes.
- Select an AI Solution: Choose tools that align with your needs and provide customization.
- Implement Gradually: Start with a pilot, gather data, and expand AI usage judiciously.
For AI KPI management advice, connect with us at hello@itinai.com. And for continuous insights into leveraging AI, stay tuned on our Telegram or Twitter.
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