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AI for Unstructured Data: Making Sense of Chaos

· 3 mins read

Extracting Insights at Scale from Unstructured Data with Exasol & AI

Extracting actionable insights from vast amounts of unstructured data is a formidable challenge for many organizations, particularly in industries like Healthcare, Retail, Pharmaceuticals and Technology. Collecting keywords from PDFs, deriving sentiments and topics from customer support tickets, aggregating feedback and summarizing conversation transcripts are just a few typical examples.

Given that 80% of data by volume is unstructured, while structured data analytics accounts for 60% of organizational spend1, it’s clear that most organizations aren’t spending enough or efficiently directing their investments to extract value from the bulk of their data.

Thankfully, Exasol and AI can help solve this problem.

Leveraging AI for Unstructured Data

AI applications are having a revolutionary impact across every industry. AI can now help organizations tap into the full value of their data quickly and effectively by making sense of unstructured documents generated from projects like clinical trials, customer support analysis, and market research. In these scenarios, Exasol’s unique architecture works to scale AI solutions securely across your entire organization, so you can combine Exasol’s lightning-fast data engine with the power of LLMs to automate your data extraction at scale. This saves your teams valuable time and improves their efficiency while driving towards the insights that matter.

Implementing AI-Powered Data Extraction with Exasol

To effectively harness your unstructured data, Exasol provides a scalable platform that seamlessly integrates AI models into your data workflow. Here’s how to implement this effective solution:

Storing and Loading LLMs

Large Language Models (LLMs) can be stored and loaded directly within Exasol’s infrastructure using BucketFS to ensure fast, secure access.

Choosing the Right LLM

Exasol allows you to select LLMs from sources like HuggingFace and ensure you pick one that’s tailored to your specific use case to ensure you get more accurate, relevant insights.

Using Exasol’s AI Lab

Exasol’s AI Lab offers handy pre-installed packages and blueprints for pre-processing your unstructured data. You can then retrieve insights from your LLM through User-Defined Functions (UDFs), streamlining your AI workflows.

Executing UDFs

Execute UDFs using SQL queries – Exasol’s query engine scales these across all hardware to ensure fast, efficient processing.

Storing and Analyzing Results

Store your results in Exasol or another relational database for further analysis. It’s this easy integration that allows you to incorporate Exasol seamlessly into your existing data workflows.

By combining Exasol’s infrastructure with AI’s capabilities, you can transform unstructured data into actionable insights, keeping your organization ahead in a data-driven world.

In the brief video below, you can see a real-world use case for this architecture, where a healthcare provider extracts data from clinical trial results and stores them in a relational format for further analysis.  This task would ordinarily take days or weeks to do manually, and scripted solutions could easily miss critical pieces of information and still take far too long to implement and troubleshoot. LLMs, on the other hand, offer a quick and powerful new solution to this challenge.

Unlocking Value with Exasol

Exasol’s unique architecture empowers your organization to unlock previously untapped value from your unstructured data – at scale – while seamlessly integrating AI and machine learning capabilities. Operating both in the cloud and on-premises, Exasol adapts to your regulatory and compliance requirements, and with AI Lab and all the essential components natively available at no additional cost, it offers a comprehensive and cost-effective solution for transforming your data into actionable insights.

Get started with Exasol’s AI solutions today – request your free demo to see it in action for yourself.

1 Source: https://edgedelta.com/company/blog/what-percentage-of-data-is-unstructured