AI for unstructured data: key features and capabilities
AI for unstructured data: key features and capabilities
Discover the key features AI systems need for effective unstructured data processing, enhancing GenAI and RAG applications with advanced analytics.
Key takeaways
1. AI excels at unstructured data processing by transforming complex document formats into structured outputs, essential for GenAI and RAG applications.
2. Intelligent data extraction techniques, like chunking and OCR, are crucial for converting raw data into analyzable formats.
3. Semantic understanding through NLP and hierarchical text retention enhances the ability to derive context and meaning from unstructured data.
4. Security and compliance are vital, with AI systems needing robust measures to protect sensitive information during unstructured data analysis.
5. Scalability and integration are key, with serverless architectures and big data technologies facilitating the seamless handling of large datasets.
In my last post about unstructured data management, I delved deep into what goes into preparing any kind of data for downstream generative AI applications. Obviously, the complexity of unstructured data increases storage costs and poses significant difficulties in analysis and security and a greater challenge for Gen AI models.
But cleaning this huge volume of complex data is where AI shines. By leveraging advanced techniques such as natural language processing, AI can transform this chaotic data landscape into structured outputs to be used downstream. In this post, I’ll explore how AI can be used for this purpose, and what features an AI Agent or system should have to do a good job of processing unstructured data.
Why AI excels at processing unstructured data
AI systems excel at unstructured data processing by automating the extraction and transformation of complex document formats into structured outputs. Imagine seamlessly preparing your data for cutting-edge applications like generative AI (GenAI) and Retrieval-Augmented Generation (RAG). These technologies thrive on high-quality inputs, and AI ensures that your data meets these standards, reducing errors and enhancing precision—especially crucial in regulated industries like finance.
In my opinion, for any organization or enterprise with messy data (which is all of them, by the way), using AI for cleaning this data up is the only reasonable solution. Here’s a quick rundown on why that’s the case:
Handling complexity and volume
Unstructured data, which includes text documents, emails, social media posts, images, and videos, lacks a predefined format. This makes it challenging to store unstructured data using conventional tools.
AI technologies such as machine learning (ML) and natural language processing (NLP) can automatically categorize and tag content, identifying trends and patterns within massive datasets.
Advanced techniques
Natural language processing enables AI to understand and interpret human language, extracting key information and patterns from vast datasets. Sentiment analysis, for example, can gauge public opinion from social media posts, providing businesses with insights into consumer behavior.
Additionally, computer vision allows AI to process visual data like charts and images which makes sure no data type remains untouched.
I have worked with multiple enterprises at Multimodal building just this kind of AI. Made especially for cleaning and preprocessing unstructured data, it performs way better than existing OCR or data extraction solutions. For my clients in finance, this clean data is like gold because it powers advanced models. Besides, I have seen other such products in this space too, which shows just how ripe and useful the technology is.
As we move towards a future dominated by unstructured data—projected to reach 180 zettabytes by 2025—most enterprises will need this solution, more so if they’re looking to build or deploy Gen AI/RAG applications.
Key features of effective AI systems for unstructured data
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Yes, you need a good AI system to process unstructured data. But how do you know what’s really “good”? For starters, you should look for a solution that’s secure and integrates well. Here are some other features it needs to have to be optimal:
1. Intelligent data extraction
Unstructured data presents a formidable challenge due to its lack of a predefined format. Intelligent data extraction is essential for transforming this raw data into structured formats that AI systems can analyze effectively.
Chunking and feature extraction: One of the core techniques in unstructured data processing is chunking, which involves breaking down large documents into manageable pieces. This facilitates better analysis by isolating semantically relevant components, allowing AI systems to focus on significant data points without being overwhelmed by volume. For example, in financial services, chunking can be used to extract specific sections from lengthy regulatory documents for compliance checks.
Optical Character Recognition (OCR): OCR technology plays a crucial role in converting images and scanned documents into machine-readable text. By extracting text from visual data, OCR enables AI systems to digitize and store unstructured data efficiently, making it accessible for further analysis. This is particularly useful in industries like healthcare, where patient records and medical forms are often scanned documents that need to be integrated into digital health systems.
2. Semantic understanding
Semantic understanding is pivotal in advanced unstructured data analytics, as it allows AI systems to derive context and meaning from diverse datasets.
Natural Language Processing (NLP): NLP is at the forefront of semantic understanding, enabling AI to perform tasks such as sentiment analysis, named entity recognition, and language translation. By processing text data from customer reviews or social media posts, NLP provides insights into consumer sentiment and market trends.
Hierarchical text retention: Maintaining nested relationships within text is crucial for preserving context during analysis. Hierarchical text retention ensures that important structural elements, such as headings and subheadings, are not lost during the transformation process. This feature is vital in document-heavy industries like finance, where the relationship between different sections of a report can impact decision-making.
3. Versatile data transformation
Transforming unstructured data into structured formats is essential for compatibility with AI applications.
Table and chart processing: Converting tables into structured formats like CSV or Excel and interpreting charts as semantic descriptions enhances data comprehension. This capability is crucial for sectors like finance and insurance, where tabular data from reports must be analyzed quickly and accurately.
Output formats: Ensuring compatibility with various output formats such as JSON helps preserve data structure during transformation. JSON's flexibility allows it to handle both structured and semi-structured data efficiently, making it a preferred choice for storing unstructured data after processing.
4. Security and compliance
Unstructured data analysis and management often involve sensitive information, necessitating robust security measures.
Data security measures: Implementing on-premise or virtual private cloud solutions ensures that sensitive data remains secure during processing. Compliance with standards like SOC 2 Type 2 is critical for industries such as finance and healthcare, where data breaches can have severe consequences.
Data governance: Adhering to data protection laws and internal policies is essential for responsible management of unstructured data. Effective governance frameworks help organizations maintain control over their data assets while ensuring compliance with regulations.
Both when I used to run machine learning teams at FIs and now with Multimodal clients, I have seen the immense security restrictions most enterprises have. If you own even a mid-sized organization in a regulated sector, security is the last thing you can compromise on. So when looking for an unstructured data AI solution, security should be a top priority.
5. Scalability and integration
Scalable solutions are necessary to handle the vast volumes of unstructured data generated by enterprises.
API structure: Utilizing serverless architectures facilitates seamless integration with existing systems without requiring custom code. This approach enables organizations to scale their AI capabilities efficiently as their data needs grow.
Big data technologies: Leveraging frameworks like Apache Hadoop and Spark provide distributed processing capabilities over clusters of computers. These technologies are essential for managing large datasets in real-time applications such as fraud detection or customer support analytics.
6. Performance optimization
Optimizing performance is key to ensuring high-quality outputs from AI systems analyzing unstructured data.
High-quality outputs: Using state-of-the-art models ensures precise extraction tasks, enhancing the accuracy of downstream applications such as GenAI or RAG systems. This precision is particularly important in industries where decision-making relies heavily on accurate data interpretation. A great example that I can think of here is financial institutions that rely on analyst reports and market sentiments.
Real-time processing: Enabling efficient querying and retrieval from large datasets supports real-time applications across various sectors. For instance, in retail, real-time analysis of customer feedback can inform marketing strategies and improve customer engagement.
Effective AI systems for unstructured data must incorporate intelligent extraction techniques, semantic understanding capabilities, versatile transformation processes, robust security measures, scalable architectures, and optimized performance strategies. These features empower organizations to unlock the full potential of their unstructured data assets, driving innovation and efficiency.
Special Invitation: Join Us in Vegas
If you will be at InsurTech Connect in Las Vegas October 15-17, pop by either Gener8tor (Kiosk R14) or Plug and Play (Kiosk P6) kiosk to meet the Multimodal team. Looking forward to seeing you there!
Build good AI with clean data
In the next few years, most organizations will either have adopted or will be in the process of building a Gen AI solution for operations. The biggest challenge for them has always been and will still be managing unstructured data. If you’re someone either looking for or building an AI solution, start shopping for an unstructured data AI solution first. Here are some quick tips:
Get a partner who can build multiple AI systems that can both process raw data and analyze unstructured data. In principle, this would involve them building you a customer unstructured data AI and then downstream Gen AI applications. This would also save you from multiple vendor onboarding.
Never compromise on integration. You don’t want your operations teams more frustrated with AI than they were without it. It’s also going to save you time. For instance, the integration of AI with cloud computing platforms is improving the efficiency of unstructured data storage and processing, enabling real-time analytics and decision-making.
Enterprises that harness AI for unstructured data analytics gain a competitive edge by transforming vast amounts of raw information into actionable insights. By leveraging AI to handle both structured and unstructured data, organizations can streamline operations, improve customer experiences, and drive innovation.
This brings us to the end of our series around unstructured data in enterprises. Be sure to check out the other two posts:
I will come back in two weeks with more about Gen AI applications for enterprises.
Until then,
Ankur.