Bridge the Semantic Gap with Kyvos’ Semantic Data Models

We live in an era of information overload, where organizations are drowning in a sea of data. While these digital assets promise valuable insights, they can be overwhelming if not managed effectively. For centuries, even before the advancement of technology, businesses realized that understanding the relationship between key components or entities relevant to business such as people, processes and resources is the key to successful operations. Even with ever-growing data volumes, this principle remains the same, only now organizations must focus on how to make sense of this data and make it accessible to all users within a complex digital ecosystem. This can be achieved with self-serve analytics, however without a shared understanding of terminology and meanings across the organization, self-serve tools alone may fall short.

Imagine a group of treasure hunters exploring an extensive, unknown territory, where each member has unique skills and knowledge for the expedition. However, despite having a map, these members might encounter challenges if they don’t share a common language or interpret the symbols and landmarks differently.

A similar scenario unfolds in business operations where teams and individuals have divergent perspectives, conflicting priorities and communication barriers. These challenges lead to the semantic gap that often exists within organizations. Let’s understand in detail how this gap affects businesses and what it actually means.

What is a Semantic Gap?

The semantic gap refers to the disconnect between how information is represented in a system (such as databases or data models) and how humans interpret it. This gap can be aggravated by the emerging data democratization trend, where organizations want to make data accessible to every business user rather than just IT experts. Sure, this shift empowers every business user, but it also results in organizations adopting more than one BI tool, as different business units or groups prefer solutions that cater specifically to their unique requirements. These multiple versions of the same data lead to silos and inconsistent reports, widening the semantic gap and causing misunderstandings and misinterpretations.

One common issue is differences in terminology, which occur when individuals or teams use multiple terms to describe the same concept. This disparity in terminology is a direct result of a lack of business abstraction, which prevents teams from having a standardized framework for understanding and communicating key concepts, contributing to semantic gap. For instance, a sales team might describe the process of identifying prospects as “lead generation” and track leads in a CRM system using specific fields and criteria. In contrast, the marketing team may refer to it as “customer acquisition” in a different database with different attributes. This disparity in data representation can make it challenging to identify areas of improvement and analyze how successful any campaign was.

Secondly, differences in opinions due to divergent knowledge perspectives can cause semantic gaps. Business users with diverse backgrounds, experiences and expertise might have varying understandings and opinions on analyzing a particular dataset. For instance, if a financial analyst analyzes marketing data, they might prioritize metrics such as cost per acquisition (CPA) and return on investment (ROI) while viewing marketing spending and might think of ways to minimize the expenditure. On the other hand, a product manager analyzing the same data might need metrics such as customer lifetime value (CLTV) and view marketing spending as an investment that drives lead generation, optimizes retention strategies and contributes to revenue growth. This difference in perspective creates a semantic gap because each role uses different metrics and concepts to frame their analysis which can lead to conflicting priorities and disagreements on the best course of action.

Thirdly, contextual differences can lead to differing interpretations and incorrect assumptions and considerably complicate decision-making when data is applied across varying regions or demographics. For instance, a successful marketing campaign conducted in a specific region might not yield the same results in another area. If the same campaign data and strategies are used in the new region without considering contextual factors, such as cultural differences, competitive landscapes, economic conditions and much more, they risk creating a semantic gap in database modeling. This gap in capturing region-wise entities and their inter-relationships could lead to ineffective promotional strategies and messaging, expensive mistakes, missed opportunities and strained resources.

Organizations can address these issues and bridge the semantic gap with semantic data models. Let’s understand what this term actually means.

What is a Semantic Data Model?

A semantic data model is a unified framework that defines the structure and captures the meaning, relationships and context of data elements relevant to the business domain. It provides a shared vocabulary for describing data, so business users of different teams and machines can all interpret and use the information accurately and effectively.

For example, a product can have certain attributes like a product ID, name, description, price and category and these attributes can have relationships with other entities such as customers, orders, suppliers and inventory. This logical structure and the underlying relationships between products, orders, inventory and customers are evident to humans. Still, a machine requires explicit instructions in the form of semantic data models to understand these relationships. In this case, a semantic data model can define not just the relationships between products and other entities such as customer, orders, etc, but also the context and semantics that give those relationships significance. This creates a shared understanding for the metadata available for this specific e-commerce domain.

How does the Kyvos’ Semantic Performance Layer Bridge the Semantic Gap?

Kyvos is a semantic performance layer for AI and BI that bridges the semantic gap by adding business domain knowledge right on top of the data storage layer. The platform provides a unified view of enterprise-wide data by maintaining business metrics, data access rules, calculation logic, etc., in one place. This way, by standardizing and centralizing the organization’s data logic, Kyvos empowers all teams to have a shared understanding of metrics definitions, lowering the risk of misinterpretation or inconsistent query results. Using its AI-powered smart aggregation technology, Kyvos processes all the potential data combinations in advance based on the intelligence gathered from query usage patterns and creates price-performant semantic data models.

Role of Kyvos’ Semantic Data Models in Enhancing Conversational Analytics

The emergence of Gen AI-powered conversational analytics has enabled users to engage with their data more intuitively using natural language. These models have amazed everyone with their ability to interpret, translate and generate human-like content. While LLMs are trained on a wide array of web-sourced data, they are still prone to contextual errors due to their lack of enterprise-specific knowledge, causing a semantic gap and leading to hallucinations and biased outputs.

Kyvos’ semantic performance layer leverages its Gen AI capabilities to tackle the semantic gap within LLMs. The platform overcomes this challenge by generating vector embeddings from metadata in a semantic data model, including descriptions, tags, dimensions, attributes and measures. These embeddings are then stored in a vector database to make sure that the data interpretation remains consistent and precise across the organization.

When a user submits a query, Kyvos generates a query vector embedding and compares it with relevant semantic data models to assess their similarity scores. Then, it compares the similarity score of the query vector embedding with the stored ones and identifies the relevant semantic data model and columns. After identifying the relevant semantic data model, Kyvos employs semantic search to pinpoint the most suitable dimensions and metrics within the model that become the context for LLMs.

This is how Kyvos, with the help of semantic data models, bridges the semantic gap and enables LLMs to generate accurate responses.

Final thoughts

In the era of data democratization and self-serve analytics, semantic data models provide a powerful foundation for bridging the semantic gap. They establish a standardized framework that aligns business metrics and definitions across teams and provides a shared understanding of key concepts. This empowers every business user to analyze data effectively and get consistent, reliable and contextually relevant insights, which helps them to interpret data accurately and make data-driven decisions.

Kyvos’ semantic performance layer and semantic data models take it a step further by providing a single source of truth for enterprise-wide data. It enables users to get sub-second query responses on all types of queries run across hundreds of dimensions and measures. For modern organizations, this means making informed decisions with confidence and agility.

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