Leveraging Gen AI on Structured Enterprise Data

Generative AI is beginning to impact the analysis of structured enterprise data. It enables the conversion of natural language queries to structured query language (SQL) and facilitates vector similarity search to uncover valuable insights previously buried in the data, says Chad Meley, CMO at Kinetica.

Generative AI has captured widespread attention by producing novel and innovative outputs in creative domains, from captivating music compositions and breathtaking artwork to crafting compelling pieces of writing. In the business realm, the initial applications of generative AI have focused on extracting insights from the same kinds of unstructured data, such as call center notes or customer contracts. However, its influence now extends into structured enterprise data analysis. By leveraging the power of generative AI, organizations can unlock hidden insights, fundamentally transforming how businesses extract intelligence from their data. This innovative expansion highlights the adaptability of generative AI.

Generative AI is ushering in a transformative era in analyzing structured enterprise data through two key innovations. First, it enables the conversion of natural language queries to structured query language (SQL), a process often referred to as conversational query. This breakthrough allows users to interact with databases and extract insights using plain language, democratizing data access and analysis across organizations. Secondly, generative AI facilitates vector similarity search based on structured data. Structured data, encompassing well-organized information like orders, inventory, web traffic, and sensor readings in IoT, is the foundational data type that fuels industry innovation. Generating embeddings specific to structured information enhances search capabilities, making it easier to identify patterns, trends, and correlations in vast datasets, greatly expediting decision-making processes, and uncovering valuable insights previously buried in the data.

Natural Language to Structured Query Language (NL2SQL)

NL2SQL processes plain language queries or questions, converting them into structured SQL queries that databases can understand. It involves a two-step process. First, the Large Language Model (LLM) uses natural language understanding to dissect the user’s query, identifying key components such as the entities, conditions, and desired data. This comprehension phase is critical, as it accurately interprets the user’s intent. Second, the LLM generates SQL queries based on the parsed information. It maps the natural language elements to corresponding SQL commands, including SELECT, FROM, WHERE, and JOIN statements, tailored to the database’s schema. This transformation allows users to effortlessly communicate with data sources using conversational language, making data analysis more accessible and efficient for an organization’s broader range of individuals.

Language to SQL conversion, although a transformative technology, faces some limitations. One challenge lies in the potential for incorrect syntax in user queries, as natural language can be imprecise and context-dependent. Furthermore, generative AI models may occasionally generate “hallucinations,” where the AI produces SQL queries that do not align with the user’s intent or database lexicon. Lastly, sub-optimized query planning can occur, leading to inefficient database interactions.

However, these limitations are actively being addressed. Improved training data, fine-tuning techniques, and robust validation processes help reduce incorrect syntax and hallucinations. Additionally, innovations in query optimization and reinforcement learning enhance query planning, ensuring that the generated SQL queries are syntactically correct, semantically accurate, and efficient. These ongoing developments are making language-to-SQL SQL conversion more precise and user-friendly, paving the way for natural language to become the lingua franca of business analytics.

See More: How AI Will Shine a Light on Dark Data

Vector Similarity Search 

Vector similarity search, a powerful concept closely intertwined with generative AI, enables the discovery of patterns and insights that were previously beyond the reach of traditional analytical approaches. At its core, vector similarity search leverages the capabilities of generative AI models to transform structured data into high-dimensional vectors, facilitating efficient comparisons and pattern recognition.

Traditional analytical methods typically rely on structured queries or deterministic methods to identify patterns in data. While effective in many scenarios, these approaches often need help with complex or high-dimensional datasets like customer profiles, sensor data, network traffic data, financial data, etc. Vector similarity search, on the other hand, taps into the inherent relationships and similarities within data by encoding it into multi-dimensional vectors. These vectors encapsulate the essence of the data’s attributes and their interconnections.

Generative AI models, such as deep learning-based autoencoders and transformers, are pivotal in creating these vectors. By training on vast datasets, these models understand the data’s latent structures, enabling them to map data points to vectors in a way that captures essential features, even in high-dimensional or complex data like time series and spatial datasets. 

What sets vector similarity search apart is its ability to uncover patterns and associations not explicitly defined in advance. This data-agnostic approach means patterns emerge organically from the relationships encoded in the vectors rather than relying on predefined rules or queries. As a result, vector similarity search can unveil hidden connections, anomalies, or trends that traditional methods might overlook.

Consider a picture of a dog with its intricate features, such as breed, size, coat color, tail length, tail curliness, eye shape, ear shape, paw size, paw pad texture, snout length, nose color, body weight, whisker presence, and thousands of other features. Those features are stored as a series of numbers within a long vector representation. This vector encapsulates the essential attributes of the image in a high-dimensional space. Utilizing this vector space, the AI can discern similarities between objects. For instance, searching for animals similar to a dog may find a vector corresponding to a wolf, recognizing common traits despite their differences. 

This same principle extends beyond images and can be applied to profile things like stocks in financial analysis. The features of a stock at any point in time may include P/E ratio, liquidity, volatility, volume weighted average price, bid, ask, bid-ask spread, market capitalization, beta, dividend yield, free cash flow, etc. By encoding these various financial data characteristics of a  stock into vectors, generative AI can create a meaningful representation of the stock’s profile. These vectors can then be used to identify other stocks that exhibit similar qualities, enabling investors and analysts to uncover potential investment opportunities or assess risks effectively. Generative AI’s ability to distill complex data into structured vectors provides a versatile and powerful tool for finding analogs and making data-driven decisions across diverse domains. 

The features of a security event may include IP addresses, timestamps, types of threats, packet sizes, source and destination ports, and more. Generative AI can identify similar events that aid in threat detection by encoding these various network security event characteristics into vectors. The features of a logistics operation may encompass shipment weight, origin and destination locations, delivery times, transportation modes, weather events, driver attributes, and cargo types. Generative AI can facilitate the identification of similar logistics scenarios that enhance supply chain efficiency by encoding these various logistics data characteristics into vectors. Similar implications exist in automotive, retail, healthcare, and national defense.  

Vector similarity search and natural language to SQL, enabled by generative AI, will revolutionize structured data analysis by transcending the limitations of traditional approaches. It equips organizations to explore data at the speed of thought and exploit intricate patterns and insights that were once elusive.

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