Current AI and analytics systems are powerful tools that handle thousands of layers of technical and non-technical data. However, insights cannot be trusted blindly when data is interpreted without context. Enterprises have access to charts, reports, and predictive insights, but without a deeper understanding of what that data truly means and how exactly it applies in the real world, raw data with generic interpretation becomes misleading for business teams.
Importance of trusted, governed data models for GenAI adoption
AI and BI systems draw conclusions based on pattern recognition and statistical correlations. But without understanding the business context, they can deliver irrelevant recommendations and inaccurate predictions. Without business logic and rules, LLMs can hallucinate, generating reasonable-sounding solutions that are either irrelevant or not practical to implement.
In enterprise data environments, decisions have legal, ethical, and financial implications, and the structure and reliability of data become critical. This is where semantic intelligence and governed data models that organize data in a central and structured way become essential.
See also: Using a Semantic Layer to Propel a Data-Driven Culture
Semantic layers as the foundation for enterprise context
A semantic layer works like a bridge between raw data and business users. The layer defines business-friendly terminology, relationships between different objects and units, metrics, rules, and logic. Without a semantic layer, it’s difficult to form an enterprise-wide context where each team or tool interprets data uniformly.
It establishes a single source of truth across teams, implementing a shared language and enforcing common metrics across systems. The layer provides meanings to terms and standardizes definitions so that each term gets interpreted consistently across multiple departments of an enterprise. The centralized layer also maintains consistency through governance policies that ensure data accessibility, its correct use, and high-quality data.
A semantic layer promotes trust in GenAI or AI systems by providing context and knowledge about restricted data and accessible data. In the same way, it enforces data governance policies like access controls, regulatory frameworks, and data pedigree at the business logic level.
Real-world impact: Reducing hallucinations, ensuring compliance
More organizations and businesses are tapping into GenAI for data interpretation and management, but face two mission-critical risks: AI hallucinations, when AI generates plausible but inaccurate results, and compliance violations, when AI ends up sharing sensitive or classified information.
Reducing hallucinations while ensuring compliance is a challenge that few AI-BI tools are equipped to handle. However, a semantic layer directly addresses these two problems and acts as a guide for AI tools by providing the meaning and definitions of metrics, structuring the data according to business logic, and enforcing compliance. This ensures that the results generated by the analytics and AI tools are accurate, reliable, and derived using logic that aligns with business goals.
Enterprises must adhere to compliance standards such as HIPAA or GDPR, which are designed to protect sensitive data. It becomes imperative to enforce these standards at the data access level to maintain security and governance. The semantic layer implements compliances at the entry level and works like a filter. It prohibits AI and BI tools from either retrieving or displaying unauthorized data and ensures that all data is traceable. This reduces legal and ethical risks considerably, increases trust in organizational data, and enhances the accountability factor of data-driven decisions.
How semantic intelligence bridges data teams and business teams
Teams working in isolation are still a reality in many organizations. In particular, there can be a disconnect between data teams—data analysts, engineers, data scientists—and business teams that take care of marketing, sales, operations, etc. As a result, it becomes challenging to make data-driven decisions as teams lack a common data ground to rely on.
Semantic intelligence provides data logic and business context across all departments, allowing both technical and non-technical users to understand and work with data. Semantic layers allow business users to analyze data in familiar business language via a self-service dashboard. This allows even non-technical teams to take an active part in the decision-making process without relying on data teams for insights and fosters a healthy collaboration. Teams now work together towards policy and strategy since they all have a uniform layer of accurate data at their disposal.
What does it look like in the real world?
In a BFSI business, data teams may consider app logins as ‘active customers,’ while business teams define that based on transactions. If the marketing team launches a campaign targeting the wrong active customers, it could lead to poor conversions and a waste of marketing budgets. However, a semantic layer can help standardize ‘active customers’ as people who logged into the app at least once and made a transaction within 20 days of logging in, helping target the right demographic.
Similarly, in a large multi-specialty hospital, there are leadership, operations, finance, and analytics teams that interpret the term ‘readmissions’ differently. While analysts report 18% readmission rates, the operations team reports only 13%. As a result, there is often no consensus on strategy because teams bring different data. With a semantic layer in place, factors such as readmissions, treatment success rates, and bed utilization are defined according to standardized business logic and unified data to make strategic decisions.
Final thoughts
Analytics is only as good as its context. Without complete meaning, definitions, and metrics, data insights can be unproductive and misleading. This is particularly relevant in light of the expectation that technology is meant to empower businesses with data-driven decisions. Contextual insights that are aligned with business goals and outcomes can be the catalyst for true growth.
Dharmendra Chouhan is the Director of Engineering at Kyvos Insights, bringing over 15 years of expertise in software engineering, product development, and enterprise architecture. Known for leading end-to-end product development, he excels at building scalable solutions and translating customer needs into strategic innovations. A strong advocate for big data, analytics, and open-source technologies, Dharmendra plays a key role in driving Kyvos’ success through his technical acumen and leadership.
