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In today’s data-driven world, organisations face numerous challenges in managing and extracting insights from the vast amount of unstructured data they possess. Unstructured data can hold valuable information and insights. However, the lack of structure and standardised formats make it difficult to harness these insights effectively.
With the global artificial intelligence market growing significantly, the introduction of large language models (LLMs) such as ChatGPT can help to uncover valuable insights from unstructured data. In our latest blog, we take a look at the unstructured data market today and discover how implementing LLMs can provide an opportunity for unstructured data insights.
Understanding unstructured data
It’s important to understand the differences between structured data and unstructured data.
Structured data is typically categorised as quantitative data such as names, addresses and credit card numbers and is found in a database environment which is highly organised and can be easily manipulated and interrogated.
Unstructured data is information stored in various formats such as rich media (videos, images, charts, infographics) and text documents (email, pdfs, word documents, power points, social media posts) that doesn’t follow conventional data models and is difficult to store and manage. Unlike structured data, unstructured data lacks predefined categories and fixed formats, making it a challenge for typical analysis methods to handle.
Whilst structured and unstructured data have their differences, the majority of new data created today is unstructured. It’s estimated that unstructured data is 80% of all enterprise data. This has catapulted the emergence of new platforms to be able to manage it, including large language models (LLMs).
Unstructured data poses several challenges for organisations including its sheer volume and complexity which can make it challenging to organise, tag, and categorise for effective analysis and decision-making. Furthermore, information is often inaccessible, often buried deep within unstructured data, making it difficult to locate and extract. Without a systematic approach to organising and indexing this data, organisations may miss critical insights that could drive strategic initiatives. Finally, unstructured data is prone to errors, inconsistencies, and inaccuracies. This can be attributed to factors such as human error, data entry variations, and lack of data governance. These issues can negatively impact the reliability and integrity of data-driven insights and can cause risk issues when it comes to compliance and security.
The Power of Large Language Models (LLMs)
The global artificial intelligence market is growing significantly. According to Statista, the market is expected to grow its value of nearly 100 billion USD twenty fold by 2030, up to nearly two trillion US dollars.
At the forefront of the data revolution are Large Language Models (LLMs), sophisticated AI systems designed to understand, generate, and process human language. LLMs like GPT-3.5 are built on transformer architectures, which enable them to capture contextual relationships and nuances in text. These models have been trained on massive amounts of text data, giving them a broad knowledge base that allows them to generate coherent and contextually relevant content.
LLMs have emerged as a powerful tool in tackling the challenges associated with unstructured data. By leveraging the capabilities of LLMs, organisations can effectively manage and cleanse unstructured data to uncover valuable insights. Here’s how LLMs can make a difference:
1. Natural Language Understanding
LLMs excel in understanding the complexity and nuances of human language, allowing for accurate analysis and interpretation of unstructured data. This capability enables organisations to gain a deeper understanding of textual information and extract meaningful insights.
2. Data Cleansing and Standardisation
LLMs can automate data cleansing processes, addressing issues such as spelling errors, abbreviations, and inconsistent formats. By leveraging their language generation capabilities, LLMs can transform messy unstructured data into clean, standardised formats that are ready for analysis.
3. Sentiment Analysis and Entity Recognition
Unstructured data often contains information about sentiments and opinions. By analysing language patterns and contextual clues, they can determine whether a piece of text carries a positive, negative, or neutral sentiment. Businesses can leverage this capability to gauge public opinion, monitor brand sentiment, and even fine-tune their marketing strategies for example based on customer feedback and market trends. Furthermore, LLMs can identify and extract entities, such as names, organisations, and locations, which enhances data categorisation, indexing, and search capabilities enhancing the efficiency of data extraction from unstructured sources.
4. Contextual Understanding
LLMs can identify context and relationships within unstructured data. This allows organisations to discover hidden connections, such as co-occurring terms or related concepts, which enhances data linking, semantic search, and knowledge graph generation.
Overcoming Challenges and Ethical Considerations
As we harness the power of LLMs for unstructured data analysis, it’s crucial to address the challenges and ethical considerations that arise. LLMs, like any AI system, can inherit biases from their training data, leading to skewed results. Additionally, ensuring data privacy and protection while processing sensitive information remains a top priority. Striking a balance between the convenience of automation and the need for human oversight is essential to prevent the propagation of misinformation.
When it comes to unstructured data and how users index data, it’s not quite as simple or accurate as we think. Large pre-trained models may require fine tuning, specifically for indexing to achieve optimal performance. LLMs are also only as good as the data they are trained with. If an LLM is trained using inaccurate, outdated or redundant data, it can cause undesired results when using a newly trained model, as well adding to the cost by training unwanted data.
Best Practices for Utilising LLMs with Unstructured Data
To make the most of LLMs in unstructured data analysis, certain best practices should be followed:
Preprocessing: Cleaning and structuring the data before feeding it to LLMs can improve their performance and accuracy.
Fine-Tuning: Tailoring the LLM to specific industry or domain requirements can enhance its understanding of specialised terminology and context.
Human Expertise: Combining LLM-generated insights with human expertise ensures accurate analysis, especially in critical decision-making scenarios.
Citations: Know how the response from the LLM was generated and what sources were used to allow for easy validation and confidence in responses.
Enabling Future-Proof Data Management
In the era of data-driven decision-making, the integration of Large Language Models with unstructured data is a game-changer. LLMs’ ability to process, understand, and generate human-like content from unstructured sources opens up a world of possibilities across industries and sectors. As we venture further into this domain, it’s essential to embrace the potential of LLMs while upholding ethical standards, ensuring accurate analysis, and using this technology to drive positive change in the way we understand and leverage data.
At Automated Intelligence, we understand the challenges organisations face when dealing with unstructured data. Keep an eye out on our website and social channels for product updates and new features from our Datalift solution, a powerful data analytics and migration solution that quickly processes and indexes data at scale.
Unstructured data is no longer a challenge; it’s an opportunity. Let Automated Intelligence help you unlock the full potential of your unstructured data and revolutionise your data management practices. Contact us today to learn more, contact us on info@automated-intelligence.com
