How Conversational BI Solves Key Analytics Challenges

Creating dashboards and reports shouldn’t require a technical background, or hours of your users’ time. When applications lack user-friendly analytics, customers are forced to depend on IT just to understand their own data.

That’s where conversational BI comes in. Whether you’re a business leader or a data analyst, conversational BI adapts to how you work, learns your preferences, and helps you move from question to insight faster than ever before. It’s time to move beyond traditional BI and see how AI is reinventing the way teams connect with data.

Here, we discuss how conversational BI solves four common analytics challenges.

1. The IT Factor

In the traditional world of analytics, analysts and data scientists require a deep understanding of data sources to write complex SQL queries. But your average user may not have access to the type of knowledge a data scientist does to dive into their organization’s data. Business users depend on continuous back-and-forth with these technical teams to help them extract meaningful insights from large datasets. This is typically a time-consuming process where important context gets lost in translation.

Conversational BI can help organizations tackle these challenges by acting like a dedicated analytics intern who can understand natural language requests and generate preliminary visualizations while still providing the flexibility for users to dive deeper into the data when needed.

This AI-driven approach allows users to ask questions in natural language and receive data visualizations, insights, and actionable next steps in return, fundamentally reshaping analytics workflows and the relationship between data analysts and business users.

2. The Cold Start Problem

Seasoned analysts know the feeling all too well: staring down a massive CSV or JSON file with no obvious starting point. It’s the cold start problem: you have an abundance of raw data, but you can’t easily see patterns and trends from it. Before insights can emerge, you lose time just trying to get oriented.

Conversational BI helps analysts and your end-users break through analysis paralysis by generating preliminary visualizations based on natural language requests. With an analytics tool that can generate visualizations by using AI, your application’s users could submit this request: “Visualize the relationship between product ratings and revenue from product sales.” The tool then analyzes all available data sources and loaded datasets to generate an easy-to-analyze chart..

This functionality jump-starts deeper data analysis by providing users with a visual starting point that they can react to and iterate on using their expertise and intuition. For example, when asking for the visualization of product ratings and revenue from product sales, a user might be presented with a bubble chart where the bubbles are proportional to a third dimension of data. To avoid cluttering their analysis, they might remove that dimension to zero in on the two-dimensional relationship they are exploring.

3. The Data Insights Dilemma

Some of your customers’ most stressful moments come from delivering information to stakeholders and navigating the back-and-forth as they attempt to unpack it. When stakeholders ask, “What does the data tell us?” it rarely stops there. One of the biggest problems with raw data is that it doesn’t easily show trends or areas of concern. As a result, their follow-up questions for analysts tend to accumulate rapidly:

• “What can we do about it?”
• “Can we cut the data differently?”
• “Can we just go back to the first iteration?”

Conversational BI provides your customers with quick and well-informed answers by allowing them to independently explore these questions in real time. AI capabilities embedded within a robust analytics solution can perform high-level analysis of visual data in response to natural language queries.

When evaluating the relationship between product ratings and revenue from product sales, conversational BI embedded into your application could surface key trends, identify anomalies, and provide actionable recommendations for next steps. For example, it could instantly identify product categories with low revenue and low ratings, flagging them as potential issues that warrant deeper investigation. The conversational nature of these interfaces enables this deeper dive by allowing business users to ask as many follow-up questions as needed.

4. “How Do I…” Angst

Advanced analytics platforms often come with extensive documentation. While it’s necessary to have this information, the sheer volume available can be overwhelming for users, potentially leading to operational inefficiency and angst about navigating features and functionalities. Users frequently need to open multiple browser tabs and frantically switch between product guides to perform even simple tasks, like embedding visualizations, handling permissions, or exporting data to CSV formats.

Conversational BI platforms can be trained on this documentation to answer user questions instantly and directly within an analytics tool. This prevents users from needing to leave the analytics environment and conduct a time-consuming search for information about how to complete their desired tasks. For example, users could ask conversational BI questions like “How do I embed a visualization?” or “How do I export a visual into CSV?” and receive step-by-step instructions without disrupting their workflow.

Preventing hallucination through boundary-setting is critical. To combat this, well-designed conversational BI systems will decline to answer questions outside the scope of their training data. Rather than generating incorrect or irrelevant information purely to fill the silence, they will provide responses like “I don’t have an answer because it’s not related to the product.”

The Future of AI in Analytics

Conversational BI solves four fundamental challenges in analytics: the IT factor, the cold start problem, the data insights dilemma, and “how do I…” angst. This technology makes analytics more efficient and accessible to both technical and business users. It enables a shift from analyst-dependent workflows to self-service analytics

Logi Symphony by insightsoftware is the foundational analytics solution within our comprehensive Logi data platform that puts dashboards, reports, and conversational BI directly inside the tools your users already work in. Instead of forcing people to jump between applications, you get analytics that fits your product like it was built there from the start.

Logi Symphony gives you complete control over how analytics gets delivered. You can embed insights directly into your application or internal tools, design exactly the experience your users need, and include intelligent features that help users find answers without needing to consult IT.

With Logi Symphony’s conversational BI, your users can ask questions, get answers, and act with natural language queries and AI-powered dashboard suggestions.

Logi Symphony’s conversational BI enables:

• Intuitive interactions
• Seamless workflows
• In-product guidance

Ready to learn more? Watch our on-demand webinar for a deep dive into Logi Symphony’s conversational BI.

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