The Rise of Conversational Queries

In a recent LinkedIn live event, The Rise of Conversational Queries: Best Practices & Lessons Learned, Kevin Petrie Vice President of Research at BARC , and Rishi Bhatnagar , Founder at QuaerisAI , discussed how organizations can leverage the power of conversational queries.The session explained how this technology, powered by Generative AI (GenAI), can transform data access and help businesses make better decisions.

Overview of Conversational Queries

Kevin Petrie began by pointing out a common problem in data-driven decision-making: business managers often don’t get the data they need on time. Traditional BI reports and dashboards don’t always provide the right insights when they’re needed most.

The rise of Conversational Queries using natural language, powered by GenAI, aims to solve this. It lets users ask questions in plain language, making data more accessible.

This approach means users don’t have to rely on specialized BI teams or learn complicated commands. They can quickly get insights from sources like operational databases or SQL data warehouses, making the process faster and more efficient. This approach pays back for your Conversational Query Platform in a matter of months.?

How Conversational Queries Work

The process of conversational queries has a few steps. First, the user types or speaks a question. A language model understands the question and turns it into a query that finds the right data. Then, the model gives a response based on the results.

What makes conversational queries from QuaerisAI different is that they are interactive. Users can ask follow-up questions or explore related data, digging deeper for better insights. Over time, the system learns and gets smarter, giving better suggestions based on past questions.

Early Use Cases and Applications

Rishi Bhatnagar shared examples of how early users of Quaeris are applying conversational queries in different industries. He mentioned:

1. CFOs Office & and Contract Analytics: A CFO’s office used QuaerisAI for dashboarding, Intelligent Reporting and creating Business Apps such as Contracts Analytics. By pulling data from hundreds of contracts and getting them into analytics ready format, they could track and review them instantly and more efficiently.
2. Call Center Operations & Recruiting: A U.S. company with call centers in the Philippines uses QuaerisAI to monitor call center performance,. The tool allowed their clients to ask their own questions about call metrics, making the reporting process more flexible. They also simplified the hiring process by using QuaerisAI to search through 1000s of resumes. Instead of going through resumes manually or relying on their ATS, they used the tool to find candidates based on context and keywords, thus saving a lot of time.
3. Asset Management: A large asset management firm uses QuaerisAI to give portfolio managers better access to market data and deeper analytics to help their customers. The company also explored fees and flows to understand profitability at Client as well as Advisor level more dynamically and way faster.

Key Trends and Lessons Learned

In these examples, a clear pattern appeared: Conversational Queries help non-technical business users access and understand data quickly. Instead of depending on BI experts, professionals can get the information they need in real-time, making daily tasks easier.

However, Rishi warned that this technology isn't a perfect solution for everything. Conversational queries work well for quick questions but are less effective for causal analysis or forecasting, that may require a range of variables with uniquely defined weights. He also stressed that companies should choose a Conversational Query platform that can work across your data assets as well as transactional systems/SORs rather than just a Q&A on a single data-platform.

Overcoming Challenges: Data Governance and AI Hallucinations

One of the challenges with GenAI-powered tools, like conversational queries, is the risk of "hallucinations"—when AI gives incorrect or misleading answers. To prevent this, Kevin stressed the need for strong data governance. AI is only as good as the data it uses, so it's important for companies to have high-quality, well-managed data.

Kevin mentioned that 45% of companies are not yet prepared with the data governance needed to fully support AI and machine learning. Good data governance also helps protect sensitive information, like personal data, and ensures intellectual property is secure.

The Future of Conversational Queries

Looking ahead, both Kevin and Rishi are positive about the future of conversational queries. As more businesses use this technology, the focus may move from simple efficiency to more creative uses. Automating data-driven tasks, working with AI models, and giving more people access to data could change how companies handle their information.

Conversational queries have great potential, but success will depend on right platform choice, careful planning, strong data governance, and focus on business problems.