Sajal Rastogi from Kyvos Insights explains why he thinks the use of GenAI and semantic layers is a gamechanger for democratising data.
Business intelligence (BI) has long held out the promise of a future in which all enterprise operations are guided by data-driven decisions. However, real data democratisation is still elusive despite investments in tools and training.
The reason? Accessibility.
Most business users depend on data engineers and specialised IT teams to provide the skills (data modelling, dashboard creation, SQL and more) that traditional BI platforms demand, inevitably shifting the burden back to IT teams.
According to an Inzata survey, data analysts spend more than 50pc of their time catering to ad-hoc reporting requests, instead of analysing and extracting insights.
Self-service analytics, therefore, is more of a challenge rather than a solution because of this systemic misalignment, which goes beyond simple implementation.
Forrester research shows that despite 79pc of business leaders saying they are investing in critical data training, only 11pc of employees feel confident in their data skills. This creates an accessibility gap, which leads to businesses gathering more data than ever before but using it less effectively.
However, a new generation of technology is now changing the game. Semantic intelligence coupled with generative artificial intelligence (GenAI) are doing away with the need for technical fluency, code and dashboards.
The outcome? BI that is at last accessible to all teams – not just to analysts and data engineers, but also to marketers, operators and finance teams.
The rise of no-code
Driven by natural language processing (NLP), no-code tools allow business users to converse or talk with data instead of pulling it. Ask a question in plain English, and the platform responds with data, insights and even visualisations.
This change goes beyond simply improving the user experience. It redefines who gets to ask questions and how. Grand View Research anticipated that the global conversational AI market is set to grow at 23.7pc CAGR from 2025 to 2050. Meanwhile, a Precedence Research forecast for 2025-2034 estimates that 84pc of companies already use no-code/low-code platforms to close their technical skills gap .
What is semantic intelligence?
A semantic layer sits between raw data and the user. It converts data models, including tables, joins and columns into business-friendly language. Rather than requesting “table.sales_amount where cust_id = 009,” users can ask, “What were our sales for customer 009?” and receive relevant responses.
Most business users don’t care about data structure – they care about answers.
The semantic layer offers consistent definitions across departments and make queries understandable and reliable. This abstraction hides complexity. No need to worry about joins, filters or schema logic. A marketing analyst, for example, can ask, “Which campaigns drove the most leads last quarter?” and receive answers powered by models built on sound data logic –without writing a line of code.
GenAI meets semantic intelligence
This is where the magic happens.
GenAI uses the semantic layer to understand the purpose and intent of a query and produce precise and pertinent responses. Even questions with loose or ambiguous wording, such as “How did marketing do last month?” can elicit in-depth, contextual insights. The goal is not to simply answer, but to explain, summarise and visualise, instantly and for everyone.
Businesses are already feeling the beneficial effects of adopting AI-powered BI.
According to a Vention study, businesses that use AI-powered BI stacks are expected to increase profitability by 38pc as a result of improved automation, quicker workflows and more intelligent decision-making.
Real-world use cases
IT bottlenecks disappear when business users can directly query data. Teams now receive insights in minutes rather than days after making ad hoc requests. Marketers can analyse campaign ROI. Supply chain managers can track logistics in real time. Finance teams can instantly generate reports on cost variances.
The outcome? Quicker and more flexible choices in all aspects of business.
Let’s look at a case study to understand how semantic layers are impacting businesses in the real world.
A leading global investment bank faced considerable challenges in predicting and managing risk. It wasn’t because of a lack of data but due to its fragmentation. On top of that, inconsistent definitions across departments lead to full risk reports taking weeks or even months to create. This delay made it harder to respond to changes in the market and put compliance at risk.
The bank used a semantic intelligence platform to fix its huge data lakehouse architecture. This got rid of confusion and improved data governance by ensuring that key financial and risk metrics had the same definitions. This semantic layer allowed finance teams to speak the same language across regions and departments, so they didn’t have to deal with conflicting data interpretations. It also enabled self-service analytics, which meant that finance professionals could look at trends and run risk simulations on their own without needing SQL or Python.
The effect on the business was clear and immediate. Reports that used to take weeks to prepare were now available in minutes. Analysts could perform analysis on the fly and look at possible risks without having to wait for IT. It also gave data scientists and engineers more time to focus on building models instead of putting out fires with report requests.
Implications for Enterprise data culture
As tools evolve, so do roles. GenAI and semantic layers democratise analytics and data literacy develops naturally as data becomes more accessible. By interacting with data more regularly, business users build their confidence and strengthen the culture that is driven by insights.
Instead of spending time building dashboards or fulfilling requests, data leaders can focus on strengthening governance and quality, building robust semantic models and driving innovation in data applications. Semantic intelligence thus results in a truly democratised data enterprise –where insights are generated by anyone, anywhere, without waiting for technical mediation.
By Sajal Rastogi
Sajal Rastogi is director of technology at Kyvos Insights, where he leads the development of scalable, cloud-native analytics platforms. His expertise spans distributed systems, cloud data warehousing and backend scalability.
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