Imagine if getting a business insight was as easy as having a conversation. For years, business intelligence (BI) promised to put data at everyone’s fingertips. The latest evolution of this promise is conversational analytics – the idea that anyone can simply ask questions in natural language and instantly get answers from their data. Picture asking, “What were our Q4 sales in Europe?” and getting an immediate answer with a clear chart, instead of digging through dashboards or waiting days for an analyst’s report. That’s the vision of “talking to your data,” and it’s incredibly compelling. Industry analysts certainly agree: Gartner predicted that by 2023, 25% of employee interactions with applications would happen via voice or conversational interfaces.
However, delivering on this vision has proven difficult across the industry. Many BI vendors have tried to add natural language query features or chatbots on top of their platforms, yet most organizations still struggle to truly leverage them. Why? Because turning everyday language into accurate, useful business insights is harder than it looks. In this post, we’ll explore the core challenges that have held back conversational analytics in BI – issues like ambiguous user queries, context that gets lost in conversation, and integration hurdles with enterprise data. Then, we’ll see how a new approach from inmydata is overcoming these challenges to finally realize the potential of conversational BI. Along the way, we’ll highlight the business value of doing it right: accelerating time to insight, democratizing access to data, and improving decision-making across the organization.
The Promise (and Frustration) of Conversational BI
The appeal of conversational analytics is easy to understand: it promises fast, on-demand answers without requiring technical skills. Traditional self-service BI tools did empower users to create their own reports and dashboards, but they still come with a steep learning curve. Many companies still rely on dedicated analytics teams, defeating the purpose of self-service BI. The ideal scenario is a BI experience so simple that anyone can use it – essentially making analytics as easy as a conversation. Yet if you’ve ever tried the natural language query features in today’s BI tools, you know they can disappoint. Often, you have to phrase questions carefully to get a meaningful answer, and follow-up questions don’t always work because the system forgets what you just asked. In many cases, the chatbot’s data scope is limited or requires special setup. No wonder many early conversational BI tools felt gimmicky – busy executives tried them, got frustrated, and went back to asking analysts for help.
What makes delivering true conversational analytics so challenging? Let’s break down a few of the biggest hurdles that have stymied the industry.
Why Delivering Conversational Analytics Is Hard
- Ambiguity in natural language queries. Human language is wonderfully flexible – and that’s a problem for computers. A simple question like “Give me the top 10 stores based on sales in the last week” might seem straightforward, but it can be interpreted in multiple ways. Did “last week” mean the last full business week or the last seven days? Without clarity, an AI has to guess your intent, often with wrong results. This ambiguity undermines trust – if users aren’t sure the system understood them, they hesitate to act on the answer.
- Poor context retention in conversation. Real conversations flow from one question to the next. In a discussion with a human analyst, you might ask, “How were Q4 sales in Europe?” and then follow up with, “And how does that compare to Q3?” A good analyst knows that “that” refers to the Q4 Europe sales figure you just asked about. Many BI tools struggle with this kind of back-and-forth. Early conversational systems often treat each query in isolation, so a follow-up question confuses them or forces you to restate all your parameters. Yet users naturally expect to ask an initial question and then follow-ups to dig deeper. If the BI assistant can’t remember context – for example, understanding that your second question refers to the previous one – the experience quickly becomes frustrating.
- Limited integration with structured data. Another major challenge is connecting the chat interface to the complexity of real business data. Enterprise data lives in databases and data warehouses, with defined schemas, table relationships, and business logic. Many conversational BI attempts aren’t deeply integrated with those structures. The result: the system might answer simple questions (e.g. total sales last month) well, but stumble on anything requiring joins, calculations, or knowledge of business definitions (“net profit margin in Q4 vs Q3”). In fact, Google’s own recent foray into conversational BI (via its Looker platform) highlighted this issue – without a proper semantic layer mapping business terms to data, the AI can produce answers that sound plausible but are actually wrong. In short, the lack of seamless integration with live, messy enterprise data has been a key factor holding back conversational analytics.
These aren’t the only obstacles (performance and security concerns, for example, also come into play), but they are some of the most universal. It’s no wonder that despite all the buzz, truly effective conversational analytics in BI has been elusive.
But it’s not all gloom – recent advances in AI and some clever engineering are paving the way for a new generation of solutions. Here at inmydata we've taken a fresh approach to make conversational analytics work in real business settings. By directly addressing ambiguity, context, and data integration challenges, our platform is delivering the kind of conversational BI experience the industry has been striving for.
How inmydata Cracks the Code of Conversational Analytics
We've managed to be one of the first to deliver a true conversational BI experience by tackling these challenges head-on. Rather than simply bolting a chatbot onto a traditional BI tool, the our team reimagined the analytics platform from the ground up for natural language interactions. Here’s how it works:
- Structured query translation for clear answers: inmydata bridges the gap between messy human language and the structured world of databases. The platform effectively translates your plain-English question into a precise data query – but it avoids ambiguity by relying on a well-defined semantic layer. Common business terms and calculations are pre-defined in our data model, so the system knows exactly what you mean by “sales” or “last week.” Thus, the AI doesn’t have to generate a complex SQL query from scratch – the heavy lifting is handled by the predefined data view. Our platform architecture ensures the AI only ever needs to generate simple queries, since any complex logic is handled upstream. This dramatically reduces the chance of error. For the end user, you simply ask a question and get the right answer without worrying about perfectly phrasing the question.
- AI-guided context understanding: To maintain a truly conversational flow, our analytics “copilot” keeps track of context between questions. Ask “What were our Q4 sales in Europe?” then “How does that compare to Q3?”, and it will understand the second question in light of the first. The system achieves this by combining the natural language prowess of large language models (LLMs) with context-aware design. Because we feed the AI with rich metadata about your business (definitions of metrics, time periods, etc.), the AI has built-in awareness of what you mean. In essence, it gives the AI a “cheat sheet” about your data, supplying the context needed to answer follow-ups correctly. The result is a smooth dialogue: you don’t have to repeat yourself, and the AI’s answers stay on-topic – just like a human analyst who remembers the conversation.
- Seamless integration with data at speed and scale: Another cornerstone of our approach is how seamlessly it connects to your actual data sources and how quickly it delivers responses. It can query millions of rows and return results within a second or two, so performance keeps the experience smooth. inmydata also works securely with your enterprise data – only a tiny encrypted sample is sent to the LLM at the start (just enough to convey the structure), and no sensitive data is retained. All the heavy lifting happens within our own secure environment. In practice, you get the flexibility of an AI assistant with the security of a mature BI platform. inmydata connects directly to your databases, respects your permission settings, and uses live data – so when an executive asks a question, the answer reflects the latest information under proper governance.
- Interactive validation for trusted conversational analytics: Another crucial feature that sets inmydata apart is its built-in validation capability. As discussed, a significant challenge with AI-driven conversational analytics is that some platforms can provide answers that seem plausible yet turn out to be incorrect. inmydata addresses this directly by pairing every conversational response with an interactive visualization. Users don't just get a simple answer—they see an accompanying chart that clearly displays the underlying data. By interacting with this chart, users can quickly review filters, drill down into details, and verify the accuracy and relevance of the provided insight. This transparency not only builds confidence but ensures all business decisions are grounded in clear, validated information.
By addressing ambiguity, context, and data integration in these ways, our conversational analytics tool behaves far more reliably and intelligently than earlier attempts. But what does this translate to in terms of business value? Let’s explore how solving these challenges makes a difference for the organisation.
Business Impact: Faster Insights, Greater Access, Smarter Decisions
Adopting conversational analytics isn’t just a novel user experience – it comes with real business benefits that can be felt across departments:
- Accelerated time to insight. The most immediate benefit is speed. What used to take days of waiting for a report can now be answered in seconds by simply asking. When questions arise in a meeting or during a decision, teams get answers on the fly, dramatically shortening decision cycles. By removing bottlenecks, conversational BI ensures insights are available when needed, so decision-making isn’t held up waiting for data.
- Democratized access to data. Conversational analytics truly opens up data access beyond the usual pool of specialists. Not everyone knows how to build a pivot table or write SQL – but with a well-designed BI assistant, they don’t have to. Any authorized employee can retrieve information just by asking in plain language. This “democratization” of data means fewer gatekeepers and a more data-driven culture. As one industry expert put it, no special training or technical expertise should be required – you just need to know what to ask. Our approach embodies this ease of use, empowering front-line managers, sales reps, and operational staff to use data in their day-to-day work, not just analysts. Insights aren’t confined to a specialized team; they become a shared resource, fuelling informed decisions at all levels.
- Improved decision-making across departments. When more people can get answers quickly, the quality of decisions improves. Teams can act on up-to-the-minute data instead of waiting ages for reports. And because each answer is accompanied by a chart, users know exactly what data they’re looking at, which builds trust. Meanwhile, with routine queries handled by the AI, your skilled analysts are free to focus on deeper analysis and strategic projects. Every team can be more proactive, and data experts can tackle high-value work instead of churning out repetitive reports
In short, conversational analytics has the potential to accelerate and improve business processes from the boardroom to the front line. It closes the gap between a question and its answer, making the organisation more agile and responsive. Early adopters are finding that it doesn’t just save time – it changes how people approach problems when they know data is literally a question away.
Conclusion: Embracing the Conversational Future of BI
The next era of business intelligence is poised to be conversational. We’re moving toward a world where asking your data for insights is as natural as messaging a colleague. This shift is already underway – BI is part of this transformation. The technology behind this trend, from advanced language models to smarter data integration, has matured to the point that real, practical solutions are here. The long-standing challenges of understanding ambiguous questions, carrying context between interactions, and connecting to complex data sources are finally being solved.
We are are leading the charge, turning the dream of easy, conversational access to data into a reality. It’s time to rethink how your teams get answers from data. Embracing conversational analytics can shorten the path from question to insight, reduce dependence on specialized gatekeepers, and empower every stakeholder to make data-driven decisions with confidence.