Central Source of Truth
Liran Cohen
March 5, 2025
Self-service analytics has long been the holy grail for data teams. The promise is simple: empower business users with direct access to the data they need, when they need it, without relying on analysts. Yet, time and time again, companies invest heavily in self-service initiatives, only to find that usage is low, trust is fragile, and business teams still rely on analysts for answers.
So why does self-service analytics so often fail? And more importantly, how can AI finally help us turn the tide?
Organizations attempting to scale data access have historically faced two choices:
Hire more analysts - Expanding the team to handle increasing data requests.
Enable self-service - Automating access to data so business users can retrieve insights independently.
While self-service sounds ideal, the reality is far more complex. Many companies have invested in self-service initiatives only to see low adoption and ongoing reliance on analysts. The main reasons behind these failures include:
Dashboards are overwhelming for non-data-savvy users.
Business users don’t want to sift through charts and filters; they want direct, actionable answers. With the rise of chat-based interactions, static dashboards feel outdated and cumbersome.
Data inconsistencies erode confidence.
When different dashboards show conflicting numbers for the same metric, stakeholders default to asking analysts to validate the "correct" data.
Business needs evolve faster than data teams can build new dashboards.
Static dashboards answer pre-defined questions, but users increasingly demand insights tailored to their immediate needs—just like they would when speaking to a human expert.
Self-service analytics has struggled because traditional BI tools couldn't adapt to these realities. AI offers a path forward.
We are at a turning point where AI allows us to move beyond dashboards and towards intelligent, proactive data apps.
Rather than forcing users to navigate complex BI tools, AI-powered interfaces can deliver context-aware insights, personalized recommendations, and dynamic data interactions.
But AI isn’t a magic solution—it’s a tool. To use it effectively, we must understand what it can and can’t do.
What AI can do
What AI can't do
Given the AI's capabilities and drawbacks, let's examine how AI can enable self-service analytics given the three failure points we discussed earlier in this post:
AI-powered chat interfaces allow users to ask data questions in natural language, eliminating the need to navigate complex dashboards. Instead of selecting filters and drilling down through charts, users can simply ask, “Why did our revenue drop last month?” and receive an insightful, contextual response.
AI can also identify issues and opportunities in our data and proactively push recommendations and action items.
To increase trust in AI-generated insights, systems must be transparent and verifiable. AI can integrate with a dynamic Semantic Layer, ensuring that metrics are consistently defined across all reports. This way It can also provide explanations for discrepancies, helping users understand why two dashboards might show different numbers.
Unlike static dashboards that answer only pre-defined questions, AI can dynamically generate new metrics, breakdowns, and insights based on user queries. If a requested metric isn’t pre-built, AI can assemble it from existing data models and highlight it as "not yet verified," ensuring transparency while expanding analytical coverage.
"When you say "active user", what do you mean? We have 10 different definitions for what is an active user"
While AI can revolutionize self-service analytics, it still requires structured data and business context to function effectively. But a company's data is a mess, with duplicated logic and scattered business definitions locked in SQL code, BI tools and in people's minds.
We need a more accessible way to store data, closer to the business language - so AI can have the right context, semantics and structure to avoid hallucinations and generating inconsistent results.
Lynk bridges the gap to enable self-service analytics with AI with three core concepts
Super fast onboarding, continuous governance.
While there is a huge potential in AI and a clear need for a Semantic Layer to power it - re-building the whole data infrastructure is a heavy and expensive task.
Lynk Discovery engine scans DB schemas and SQL (and dbt) repositories to extract existing semantic definitions and build your Semantic Layer instantly. It also runs periodically (daily/ hourly) to continuously govern a company's data and business logic is always healthy and AI-ready.
Born in the era of AI, Lynk Semantic Layer is a gives AI the structure it needs in order to generate consistent results your team and customers can trust. Unlike other Semantic Layers, Lynk is built for Analysts and non-technical users, to avoid bottle necks on the Data Engineering team. Lynk is also designed for the right balance between flexibility and structure.
Use Lynk's AI-chat interface or build your own AI powered applications to fit your specific needs, all within Lynk.
Traditional self-service analytics has failed due to complexity, lack of trust, and limited coverage. AI presents an opportunity to move beyond static dashboards to dynamic, intelligent data applications—but only if the right infrastructure is in place.
With Lynk, organizations can bridge the gap between AI’s potential and real-world usability, empowering business teams with accessible, trustworthy, and proactive insights—while keeping data teams in control.
Automate data workflows with consistency, clarity and trust, to enable AI and business users succeed with data