For decades, structured data has been essential for organizations, filling spreadsheets, tables, and databases to support financial accounting, stock-keeping, customer relationship management, and industrial automation, to name just a few examples. Over this time, businesses have refined their ability to retrieve, organize, and analyze structured sales figures, customer records, and real-time operational metrics.
Generative AI, meanwhile, is still catching up. While generative AI can leverage unstructured data out of the box – including emails, social media posts, and videos – it continues to struggle with structured data. This has made seamless integration of structured data into enterprise GenAI systems a vital differentiator for businesses seeking to enhance their operations with AI.
To get the most of their enterprise data, organizations need to understand the strengths and challenges of both structured vs. unstructured data and learn to harness their combined power. In this post, we’ll explore what makes these data types different, why they matter, and how advanced enterprise GenAI solutions can unlock their full value.
Structured data includes all data stored in fixed fields within a record or file such as rows and columns in spreadsheets or relational databases. Because it is highly organized and stored in predefined formats, structured data is machine-readable, allowing for efficient querying and analysis.
Structured data examples:
Advantages of structured data:
Unstructured data encompasses all data that lacks a fixed format, such as text documents, images, videos, and other rich media. Its lack of a fixed structure makes it more challenging to search, retrieve, and analyze.
Unstructured data examples:
Advantages of unstructured data:
Semi-structured data bridges the gap between these structured and unstructured data types. Formats like JSON or XML, which have some structure but also contain freeform text, combine the strengths of both structured and unstructured data.
Structured data is indexed according to a defined schema, which makes it easy to search using SQL queries and database filters. For instance:
Structured data is ideal for precise searches where accuracy and speed are critical. This type of search works less well for exploring relationships among data points or uncovering hidden patterns.
Traditional keyword searches struggle with unstructured data because it does not follow predefined schema. Unstructured data is most effectively searched using vector search, a method that uses vector embeddings to understand the semantic meaning of data.
Examples:
To represent enterprise data – which might be stored as a pdf document – as vectors, the data is first split into smaller chunks. Each chunk is then transformed into high-dimensional embedding vectors using an embedder in such a way that the direction of the vector captures its semantic meaning.
When a user submits a search query, the query is also converted into a vector using the same embedder. Then, the query vector is compared to the embeddings in the vector database that represent the data. Finally, the most similar vectors are retrieved.
Both the retrieval and the generation steps used by retrieval augmented generation (RAG) solutions are tailored to the properties of unstructured data. Integrating structured data presents unique challenges:
Schema Complexity: Structured data often requires specific schemas that LLMs are unable to interpret natively.
Risk of Inaccuracy: When ingesting tabular data, LLMs may misattribute structured data to data labels, leading to inaccuracies.
Real-Time Updates: RAG systems can struggle to keep up frequently updated structured data, risking outdated insights.
Addressing these challenges requires systems capable of seamlessly handling both structured and unstructured data types without compromising accuracy or speed.
Relying on unstructured data alone limits the depth and breadth of insights that businesses can extract. Let’s examine the missed opportunities in several industries:
Without structured data, portfolio managers can only access qualitative insights from market news or research reports. They miss critical quantitative trends, such as changing portfolio allocations or live asset performance, which are essential for risk management.
Structured compliance data, like loan-to-value ratios or credit risk scores, is critical for detecting fraud or regulatory issues. Without access to this data, supervisors must rely on slower, less precise manual processes.
Structured CRM data enables targeted sales campaigns, while unstructured customer feedback adds qualitative insights. Without integration, sales teams fail to identify at-risk customers or uncover cross-sell opportunities.
An enterprise GenAI application capable of interacting with structured data bi-directionally—both reading and writing—unlocks transformative benefits. Here are three examples of how this works in practice:
A financial analyst queries a GenAI platform for quarterly performance summaries, combining structured balance sheet data with unstructured earnings call transcripts. The system not only retrieves insights but also updates performance metrics in real-time based on new inputs.
A sales executive uses a GenAI system to analyze structured CRM data and unstructured social media comments to identify promising leads. The system automatically updates lead statuses, streamlining the sales pipeline.
A banking supervisor queries a GenAI platform to assess compliance with structured regulatory data and unstructured audit findings or inspection reports. The system flags discrepancies, suggests corrective actions, and updates compliance dashboards with real-time risk scores based on the latest data.
To fully leverage the advantages of structured and unstructured data, organizations need a platform purpose-built for both. The Squirro Enterprise GenAI Platform empowers businesses to unlock insights from their entire data ecosystem.
By harnessing structured and unstructured data seamlessly, Squirro enables businesses to drive smarter decisions, reduce inefficiencies, and gain a decisive edge over competitors.
Understanding the dynamics of structured vs unstructured data is crucial for achieving competitive advantage in today’s fast-paced business environment. With the Squirro Enterprise GenAI Platform, businesses can unlock the full potential of their data, saving time, enabling deeper insights, and transforming decision-making.
Book a demo to explore how Squirro can transform your enterprise GenAI performance today.