The Case for Structured Data in the Age of AI

As artificial intelligence (AI) continues to unlock value from unstructured data, experts are increasingly advocating for structured data approaches. While unstructured data—like documents, images, and social media feeds—holds immense potential, the complexities involved in processing it often make structured data a more reliable option for many organizations.

Unstructured data can indeed provide valuable insights. For example, AI technologies can analyze customer call transcripts or video images from manufacturing lines to improve business efficiencies and customer experiences. However, the process is far from straightforward. As Eilon Reshef, CPO of Gong.io, points out, “The hype around generative AI makes it sound like a plug-and-play process, but in reality, it’s still a complex task”. Extracting meaningful insights from unstructured data requires sophisticated AI models and significant computational resources.

Companies like Salesforce are working on integrating both structured and unstructured data to enhance AI capabilities. Their approach involves using Retrieval-Augmented Generation (RAG) to combine structured data from CRM systems with unstructured data from PDFs and emails, making AI insights more contextual and relevant. Despite these advancements, the challenges of managing unstructured data remain significant.

Experts recommend structured data approaches for several reasons. Structured data, typically stored in relational databases or data warehouses, is easier to manage, analyze, and integrate. It allows for more accurate and efficient data processing, reducing the risk of errors and inconsistencies. Moreover, structured data facilitates better compliance with regulations such as GDPR, which is crucial for avoiding hefty fines.

The implications of favoring structured data are profound. Organizations can achieve more reliable and actionable insights, streamline business processes, and ensure compliance with legal requirements. This approach also allows for a more straightforward implementation of AI technologies, making it accessible to a broader range of businesses.

Increasingly, data management will likely move towards a hybrid approach, where structured data forms the backbone of AI systems, complemented by unstructured data for added depth and context. This balanced strategy can help organizations maximize the benefits of AI while minimizing the complexities associated with unstructured data.

While unstructured data holds significant promise, structured data approaches offer a more practical and reliable path for many organizations. By focusing on structured data, businesses can harness the power of AI more effectively, ensuring better decision-making and enhanced operational efficiency. As AI technologies continue to evolve, a hybrid approach that leverages the strengths of both data types will likely become the norm, driving future innovations and business success.