The Rise of Conversational Analytics in The AI Era
- Arkon Data
- Oct 23
- 5 min read
We are witnessing a breakthrough in the way companies make analytics. For years, business intelligence meant dashboards, charts, and SQL queries. Data teams worked tirelessly to translate business questions into reports that could take days or even weeks to build. But in 2025, that model is breaking. The new era of conversational analytics is redefining how enterprises interact with their data: not through clicks or filters, but through natural conversation.
What Is Conversational Analytics?
Conversational analytics refers to tools that allow users to query and analyze data using natural language, through text or voice, instead of traditional BI interfaces.
Imagine asking:
“Show me last quarter’s revenue by region.” “Which products are driving the most growth?” “Compare customer retention between North America and Europe.”
The system interprets your intent, retrieves the data, and delivers a chart or narrative answer instantly.
It’s not just analytics; it’s analytics that talks back.
This shift democratizes data access. Business leaders, sales teams, and even operations managers can now explore insights without technical training. Instead of waiting on analysts or navigating complex dashboards, they simply ask and get answers in seconds.
Why Conversational Analytics Matters Now
The surge of Generative AI has changed expectations. Users have grown accustomed to tools like ChatGPT that understand context, remember prompts, and generate fluent, human-like responses. This behavior has spilled into the enterprise world.
Leaders now want GenAI for their data, systems that not only visualize information but also reason about it. In fact, seventy-five percent of new analytics content will be contextualized for intelligent applications through generative AI (GenAI) by 2027, enabling a composable connection between insights and actions, according to Gartner (2025).
However, this new expectation exposed a critical bottleneck: data complexity. Many organizations discovered that AI is only as smart as the data it’s built on. Without clean, contextual, and well-governed data, conversational analytics quickly turns into a game of “garbage in, garbage out.”
That’s where the new generation of platforms like Databricks AI/BI Genie are stepping in.
From Dashboards to Dialogue: The Genie Effect
Databricks introduced AI/BI Genie as a native conversational analytics layer for its Data Intelligence Platform.
What sets Genie apart is its direct connection to enterprise-grade data. Instead of relying on static reports or isolated datasets, Genie interacts with live data stored in Databricks, which is structured, governed, and enriched through Unity Catalog.
Genie enables business users to ask:
“Which customer segments showed the highest churn last month?” “How is gross margin trending year-over-year?” “Can you break down revenue growth by product line?”
And instead of simply returning a number, Genie delivers dynamic visualizations such as bar charts, trend lines, and summaries, all generated conversationally and backed by enterprise governance.
It’s conversational analytics that truly understands the context of your data.
Conversational Analytics Software Providers
While Genie represents a new generation of enterprise-grade conversational analytics, it’s not alone. Several platforms are converging toward this paradigm, each from a different ecosystem:
Microsoft Copilot for Power BI: Brings conversational querying to Microsoft’s BI ecosystem, letting users interact with data in plain English across reports and datasets.
ThoughtSpot Sage: A pioneer in search-driven analytics, offering natural language querying and AI-generated insights.
Salesforce Einstein Copilot: Integrates conversational AI into CRM data, enabling users to ask contextual questions about sales, customers, and campaigns.
Qlik Sense Insight Advisor: Automatically generates charts and narratives based on conversational queries.
SAP Joule: A newer entrant embedding generative and agentic AI into SAP DataSphere for contextual enterprise analytics.
Together, these tools signal a clear trend:
BI is becoming conversational, and data is becoming dialogic.
The Foundation Behind Every Conversation: Data Context
Here’s the hidden truth about conversational analytics: Language models can’t truly understand data unless the data itself is well-prepared.
Natural language queries rely on metadata, relationships, and definitions that tell the system what it’s looking at. If “customer,” “revenue,” or “churn” are defined differently across systems, the answers will be inconsistent or wrong.
This is where data context becomes the differentiator.
Arkon Data Platform (ADP) plays a crucial role in enabling conversational analytics by extracting and structuring data from complex enterprise systems like Oracle Fusion Cloud, IoT devices, or PoS systems, while preserving context and metadata.
Once this data lands in a Databricks Lakehouse, tools like Genie can consume it seamlessly, enabling more accurate and insightful conversations.
In other words, ADP ensures that your AI speaks the same language as your business.
Industry Use Cases: Conversational Analytics in Action
Conversational analytics is already transforming decision-making across industries:
Retail & CPG: Store managers can ask about top-performing products, promotion effectiveness, or inventory risks in real time.
Finance: Executives can monitor liquidity ratios, forecast revenue, and perform scenario analysis through natural language queries.
Healthcare: Administrators can track patient outcomes, resource utilization, or claim resolution patterns.
Manufacturing: Plant operators can request performance metrics or downtime root causes directly through conversational interfaces.
Logistics: Operations leaders can monitor shipment delays, fuel costs, or supplier performance instantly.
Each query saves hours of manual analysis and translates directly into faster, more confident decisions.
The Future: From Chat to Action
As enterprises mature beyond generative AI into agentic AI, conversational analytics will evolve further. Next-generation systems won’t just answer questions; they’ll take action.
Imagine asking your analytics assistant not only to detect a revenue drop but also:
Trigger a workflow to alert your sales manager.
Adjust an ad budget in real time.
Simulate what-if scenarios and recommend corrective actions.
That’s the convergence of conversational analytics with agentic intelligence, where insights lead directly to automated outcomes.
Want to learn more about Agentic AI? Read our latest blog post →
At Arkon Data, We Make Gen AI Work
Conversational analytics platforms like Databricks Genie are only as strong as the data foundation beneath them.
At Arkon Data, we prepare that foundation.
We integrate, structure, and govern enterprise data so that AI and analytics systems, from Genie to Copilot to Joule, can deliver insights that are reliable, explainable, and scalable.
Because in the end, every great conversation starts with good data.
Ready to make your data conversational?
1. What is conversational analytics?
Conversational analytics lets users query and explore data through natural language — by typing or speaking — instead of using dashboards or SQL. It transforms how teams interact with data, making analytics as easy as having a conversation.
2. How does conversational analytics improve decision-making?
It removes bottlenecks between business users and data teams. With tools like Databricks AI/BI Genie, users get real-time answers and visual insights instantly, enabling faster, more informed decisions without technical barriers.
3. Why is clean, contextual data important for conversational analytics?
Even the smartest AI tools are only as good as their data. Arkon Data Platform (ADP) ensures that the data feeding these systems is structured, governed, and contextualized, so AI delivers accurate and trustworthy insights.
4. Which industries are using conversational analytics today?
From retail and finance to manufacturing and healthcare, conversational analytics is being used to monitor operations, forecast trends, and automate insights — all through simple, natural-language queries.
5. What’s next for conversational analytics?
The next frontier combines conversational and agentic AI — where systems don’t just answer questions but take action automatically, adjusting workflows, budgets, or operations based on real-time insights.
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