By Pratik Jain, Senior Director of Technology at Kyvos Insights
Nowadays, there’s a common assumption: capture and centralize data at the enterprise level, on one platform and useful information will follow immediately. This belief is fueling massive investments in cloud warehouses and lakehouses. In 2024, Market Growth reports that more than 90% of businesses were using some form of data warehousing infrastructure, with 58% choosing cloud solutions.
However, the reality is sobering. Despite centralizing data in a unified environment, most organizations fail to produce insights fast enough to drive critical decisions. The challenge lies not just in storage or centralization but also in how quickly businesses can query and translate data to aid decision-making.
The Root Cause: Query Performance Bottlenecks
Modern organizations often process petabytes of information and querying such volumes challenges even the most advanced platforms. Most data management platforms offer storage that is scalable yet queries on large or complex datasets can take a long time to run, which can slow down dashboards and analytical workflows.
Even the best BI tools and data platforms can make analysts wait for minutes to process a single query, leading to missed business opportunities. Self-service BI initiatives also falter as users lose confidence in tools that feel sluggish.
Why Traditional BI Tools Fall Short
Traditional BI tools were not meant to handle today’s massive amounts of data. These tools struggle to query billions of rows directly, especially when there are multiple concurrent users.
As a workaround, teams resort to pre-aggregated datasets, heavy caching and data extracts. The payoff is faster dashboards, but lesser control over data and undermined real-time analytics. As a result, users are forced to compromise on the depth of analysis for speed and performance.
The Real Business Impact
Slow dashboards are a major business liability that leads to lost productivity and missed opportunities. For instance, consider a global retail chain that must wait for overnight processing to update weekly sales dashboards. Analysts spend hours waiting for reports, operational teams act on stale data and executives shift from proactive to reactive decision-making.
A study by Medium found that 73% of organizations report that slow Power BI performance impacts decision-making, leading to an average cost per organization of $2.1 million. Not only that, 40% of executives also admitted to abandoning reports altogether due to poor performance. However, on the other hand, the same study found that organizations with high-performing BI systems make decisions 67% faster than those struggling with sluggish reports.
Why a Semantic Layer is Crucial
To overcome querying bottlenecks, enterprises are shifting their focus to a semantic layer. This multi-functional layer sits between a business’s data storage and analytics tools, acting as a high-speed middle tier that accelerates querying without compromising data integrity.
A semantic layer helps extract instantly consumable insights from massive, complex datasets, supporting real-time decisions with millisecond-level responses. It provides consistent definitions of common terminologies, so that different users and departments don’t have different interpretations of the same data. It also allows BI tools to access the original dataset without compromising security and governance, helping analysts and BI systems access governed data at speed.
The New Needs: GenAI and Instant Answers
Generative AI (Gen AI) applications and conversational BI interfaces are in high demand. According to IDC research, 66% of CEOs said that Gen AI tools benefitted them significantly, especially by making their businesses run more smoothly and making customers happier.
However, Gen AI tools cannot wait for several minutes for a chart or a data point. They need sub-second results to deliver a natural and effective experience for the user. Therefore, simply focusing on unifying data, without the acceleration provided by a semantic layer, fails to meet these new, stringent performance requirements.
Conclusion: Unification is Only the First Step
Merging and connecting an organization’s data is definitely important in creating a strong data strategy. However, it’s only the first step to becoming truly data-driven. The real challenge, and opportunity, lies in making queries run faster. A semantic layer provides that edge as it bridges the gap between datasets and a business’s demand for fast, reliable insights.
About Author:
Pratik Jain, Senior Director of Technology at Kyvos Insights, has over 20 years of experience in building high-performance analytics and AI platforms. He brings deep expertise in architecting and delivering scalable, enterprise-grade analytics products. As a Generative AI thought leader, he has driven innovation across multiple product suites leveraging GenAI. Pratik also leads the UX/UI strategy at Kyvos, ensuring seamless and intuitive user experiences across platforms.
https://www.kyvosinsights.com/pratik-jain/
About Kyvos:
Kyvos is a semantic layer for AI and BI. It gives enterprises a single, consistent, business-friendly view of their data for trusted AI and BI eliminating metric drift across BI tools, and grounding AI in governed semantic context for higher accuracy. Kyvos delivers lightning-fast analytics at massive scale and high concurrency, including richer multidimensional analytics on the cloud, while helping organizations control costs without performance trade-offs.