This report defines conversational queries, why analytics and business teams should consider using them, and how they can do so.
Business managers must have facts at their fingertips to make smart decisions in competitive, dynamic markets. But those facts can be evasive. Rigid and outdated BI artifacts prevent business managers from locating, understanding, and sharing answers to their ad-hoc questions. Many of these managers cannot answer their own questions because budget constraints and organizational silos block their access to BI tools. Flying blind, they make decisions based on hunch and habit rather than hard evidence. In a March 2024 poll by BARC, only 26% of 42 data practitioners said business managers in their organization get the operational facts they need from BI reports and dashboards.
Figure 1: Do your business managers get the operational facts they need from BI reports and dashboards?
Conversational queries can help. This category of business intelligence (BI) uses generative AI to provide real-time search for and retrieval of tabular data through a natural language interface. It complements traditional BI capabilities that measure, predict, and visualize market trends or key performance indicators (KPIs). Conversational queries enable business managers with minimal data expertise to rapidly assemble facts as they make tactical decisions, forecast performance, and brainstorm ideas. They respond to human questions such as the following:
1 N = 42. LinkedIn poll, March 2024
Conversational queries are one of many analytics use cases for generative AI. GenAI is a type of neural network that trains itself to interpret and generate digital content such as text or audio. Much of the current GenAI excitement centers on the language model (LM), which predicts content – often strings of words – after learning how words relate to one another in an existing corpus of similar content.
LMs such as Chat-GPT from OpenAI or Gemini from Google help knowledge workers of all types improve their productivity. And now conversational queries help knowledge workers consume data, using either dedicated tools such as Quaeris or GenAI features within BI platforms such as Tableau. Various operational applications also can include conversational queries as part of embedded BI modules, for example to generate charts or graphs within a customer portal for financial services. In these and other ways, conversational queries will become a standard aspect of modern analytics.
The BI community, including managers, is especially excited about the ability to query data without writing SQL. In a recent survey by BARC and Eckerson Group, 59% of 214 data leaders and practitioners ranked natural-language queries as the top GenAI capability that their company will use to consume BI outputs. This basic use case is more popular than other GenAI use cases such as generating visualizations or interpreting predictive models.
Figure 2: Which GenAI features and capabilities will business intelligence and analytics users within your organization leverage to consume BIA content?2
2 N = 214. BARC and Eckerson Group, January 2024
To understand the role and value of conversational queries, let’s assess the state of business intelligence (BI) today. We start by considering the two stakeholders that consume BI: Power users and business managers.
Figure 3: Power users vs. business managers
With these factors in mind, it’s no surprise that business managers express optimism about the potential benefits of GenAI. The BARC/Eckerson Group survey showed that 77% of business managers believe GenAI will improve the use of BI in their company to a moderate, high, or very high degree.
This contrasts with power users, 90% of whom believe the improvements will be moderate to low or very low. The implication: Business managers are shaping up as the most enthusiastic adopters of GenAI use cases for BI – such as conversational queries.
| LOW TO VERY LOW | MODERATE | HIGH TO VERY HIGH | |
|---|---|---|---|
| BUSINESS MANAGERS | 22% | 33% | 44% |
| POWER USERS | 40% | 50% | 10% |
Figure 4: To what degree will GenAI improve the use of business intelligence and analytics in your organization in the next 12-18 months? (by role) (n=208)3
Conversational query tools enable humans to ask questions, find answers, and interpret what those answers mean. Here is the process.
3 N = 208. BARC and Eckerson Group, January 2024
Three familiar trends create inexorable demand for BI self-service. First is digital transformation. As companies digitize their operations, they create streams of transactions, logs, and other data that hold potential insights on customer interactions or other aspects of their business. Second, companies continue to democratize access to their data. Business managers, from individual contributors up to executives, need fast and accurate data to make smart decisions. Digital transformation and data democratization contribute to the third trend: Demand for real-time data. Businesses need instant visibility into their operations to meet the needs of impatient and demanding customers. Together these trends support the case for companies to invest in BI innovations such as conversational query tools.
Use cases for conversational queries cover a broad spectrum given the popularity of digital transformation and data democratization across many functions. Let’s consider an example use case for a chain of automotive dealerships. Their operations team needs to identify ways to reduce the operating costs of its maintenance and repair services. During a daylong meeting they review their standard reports and dashboards, then brainstorm options for streamlining. This leads to a series of rapid-fire questions, which a team member poses to her conversational query tool. And the query tool might even suggest adjacent questions.
Here is an example.
| Original Question | Suggested Adjacent Questions |
|---|---|
| What were monthly repair volumes each month over the last year? |
|
| Which service personnel performed those repairs? |
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| What replacement parts did those jobs require? |
|
Answering these questions in real-time enables the operations team members to accelerate data exploration, testing and validation of their ideas. They build three specific cost-cutting measures based on evidence of historical practices that inflated spending. As they share this proposal with executives, they use their conversational query tools to address additional questions on a real-time basis. This process enables the team to build, propose, and implement its cost-cutting measures in less time and with higher confidence in the outcome.
Configured and implemented well, conversational query tools enable various business managers to enrich tacit knowledge with hard, real-time data. By summoning the right facts without waiting on BI power users, these business managers can support a variety of use cases for teams spanning finance, operations, marketing, and customer service. They can reduce time to value and respond to market changes in a more agile fashion. They also can make fewer demands of BI teams and free them up to tackle more strategic work, for example to collaborate with data scientists and incorporate AI/ML models into their reporting.
BARC recommends that data and business leaders take the following steps to understand and capitalize on this opportunity for their organization.
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