Organizations have long capitalized on structured data — information that is stored according to clearly defined parameters, making it easy to search and analyze. However, it’s notoriously difficult to extract value from the unstructured data that accounts for up to 90 percent of business information.
Unstructured data doesn’t follow a predictable format or order, and is typically qualitative rather than quantitative. Much of it is generated by humans in the forms of emails, text messages, documents and social media posts. It may also be machine-generated, such as audio recordings and video surveillance footage.
In its raw form, unstructured data isn’t particularly useful to business stakeholders. It is typically trapped in silos and unclassified, lacking the context needed to mine it for valuable insights. With some upfront effort, however, organizations can begin to leverage unstructured data to better understand the “why” behind the facts and numbers.
Preparing Unstructured Data for AI
It may be tempting to think that AI is the silver bullet that allows organizations to tap the value of unstructured data instantly. However, general-purpose AI tools are merely trained to generate the next word in a sentence based on probabilities. They lack the business context needed to understand a specific organization’s data. Organizations need to take a foundational large language model (LLM) and optimize it using domain-specific data so that it understands and can analyze unstructured data.
Additionally, the LLM needs data that is trusted and complete. In most organizations, unstructured data is scattered across various systems, often in different locations. It is stored in different formats, and there are often multiple copies and versions. Organizations should consolidate and vet the data for accuracy so that it won’t lead to false conclusions.
The most critical step is to correlate unstructured and structured data. For example, to gain a complete picture of customer preferences, a retailer would need to enrich social media signals, customer service chats, emails and other unstructured data with metadata that adds the relevant context. AI-powered tools can automatically label and annotate data, saving users significant time and effort compared to manual tagging.
Putting Processes in Place
Few organizations have well-defined processes for handling unstructured data. Database administrators are responsible for managing structured data, but unstructured data is typically generated in an ad hoc fashion by individual users. Domain experts, data analysts and AI experts need to collaborate to determine which pieces of unstructured data are useful and relevant and how to interpret them. They should also develop data governance policies and practices and ensure that data is classified so that users cannot access sensitive information unless they’re permitted to do so.
Various tools and techniques can help users make sense of unstructured data. For example, knowledge graphs use business-relevant relationships and concepts to structure, link and contextualize unstructured data. This allows organizations to automate knowledge discovery and enhance enterprise search.
Leveraging the Right Tools
Once unstructured data is ready for analysis, users can leverage machine learning, natural language processing and other technologies to identify patterns and relationships. Retrieval-augmented generation (RAG) integrates an LLM with an external knowledgebase, using relevant data to enhance its responses without retraining the model.
Organizations should ensure that the right information is accessible to the right users at the right time. Given that data is siloed in the typical IT environment, organizations should consider adopting a data lakehouse to store their structured, semi-structured and unstructured data in a centralized repository. A data lakehouse can handle a wide range of data types, allowing users to access and query relevant data regardless of format.
As organizations adopt AI, there’s a growing urgency to gain value from unstructured data. Cerium’s data analytics team can help you analyze your unstructured data, choose the right data repository and develop a data governance program. We can then help you implement the right tools and techniques to unlock the value of your unstructured data assets.
