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What this blog covers:

  • Learn about semantic layers as they provide businesses with a unified data view, simplifying complex data into business language, facilitating self-service analytics.
  • How do semantic layers bridge the gap between understanding and knowledge for Generative AI models, improving the accuracy of AI-generated insights.
  • Understand how Kyvos’ Generative AI-powered semantic layer delivers conversational analytics to communicate with data in business language.

Breaking the Analytics Logjam with Generative AI

The advent of technology has propelled businesses into a market position where data has become the prime commodity. Organizations are generating massive amounts of data on a daily basis and the increasing size is accompanied by higher complexities. To thrive and sustain success, they rely on data analytics tools that can provide insights not only into operations but clients and customers as well.

However, traditional data analytics platforms are no longer able to keep up with the vast amounts of data required to mine insights, leading to low performance and inability to scale. Cloud-based platforms have emerged to combat these challenges, yet many lack the resilience to scale continuously. A semantic layer eliminates the complexities that arise with growing volumes by converting complex data into easily consumable business language. It facilitates data democratization and self-service analytics, eliminating the need for specialized skillsets and diminishing the dependence on IT personnel.

Semantic layers enable businesses to combat the challenges of scale and size, helping them maintain high-performance data analysis.

Generative AI & Semantic Layer: Adding Context to Analytics

To augment the data analytics space even further, recent developments in AI, especially in Generative AI (Gen AI) and large language models (LLMs) have shown immense promise. However, as with any new technology, it is rigged with several challenges too. These LLMs can sometimes ‘hallucinate’ responses, wherein the outcome is inaccurate or not derived from the high-quality dataset, leading to misleading insights. Since there is no way to know how an LLM model arrived at its output, it makes them somewhat unreliable.

The symbiosis of Generative AI and semantic layer can eliminate these hallucinations. Generative AI models gain knowledge from their training datasets but don’t understand data like humans do. An enterprise semantic layer bridges the gap between understanding and knowledge by providing them with the proper context and data structure to improve accuracy of responses.

With a high-level context about metrics, dimensions, entities and relationships between them, users can generate relevant outputs with the provided datasets. Instead of querying the database directly, the models leverage the semantic layer and query the data using business definitions, leading to accurate insights.

As semantic layers already include stringent data security measures, such as multi-level data security, role-based data access, row and column level security and data masking, the Generative AI can operate within this secure framework. This not only leads to better performance but also safeguards an organization’s data from breaches or leakages. Safeguarding critical information, business secrets or sensitive customer information helps maintain trust with clients.

Democratizing Data and Delivering True Self-Serve Analytics

With the combined prowess of Generative AI and the semantic layer, true data democratization comes into play. Users are empowered with the ability to communicate with data using simple, natural language. This eliminates the need for specialized skillsets and enables a larger user base to effectively leverage data for informed decisions.

By making data accessible to everyone, the duo breaks down the barriers that traditionally kept valuable insights locked away in silos. Enhanced data accessibility leads to better decisions across the organization, enabling all users to contribute to the organization’s success.

Kyvos Generative AI Powered Semantic Layer: Achieving New Heights in Conversational Analytics

Kyvos continues to push the boundaries of innovation with our revolutionary Copilot. It leverages Generative AI and empowers business users to interact with their metrics in plain language to obtain summaries, contextualized insights and much more.

With Kyvos Copilot, users can delve into context-aware data exploration guided by contextual prompts and intelligent data selection, ensuring a seamless journey from data to insights. The ability to backtrack conversations, revise queries and explore parallel tracks enhances flexibility and encourages deeper diving into KPIs. The codeless approach enables users to unlock insights through natural conversations using simple business terms, without the need to learn any querying language.

Kyvos Copilot parses through vast datasets, flagging anomalies and condensing pivotal insights into tailor-made summaries. This not only saves time but also ensures that decision-makers receive actionable insights directly, without the need for manual trawling through data. Moreover, the capability comes with a natural language query playground, allowing users to converse in business language and receive instant insights through intuitive tables, graphs, charts or other visuals.

Reducing the time and effort needed for data analysis, Kyvos empowers users to focus on more critical tasks, ultimately enhancing productivity. Our named connector for LangChain allows not only AI applications but also large language models (LLMs) to talk to Kyvos and use it as a trusted data source. As the demand for data-driven insights continues to grow, Kyvos remains at the forefront of revolutionizing the way organizations interact with and derive value from their data.

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