Why natural language interfaces are the future of BI

Natural language interfaces (NLIs) are changing how businesses use data. Instead of needing technical skills like SQL, NLIs let you ask questions in plain English and get instant, clear insights. This makes data accessible to everyone - not just experts - speeding up decision-making and improving collaboration.

Key Benefits of NLIs:

• Easy Access: Anyone can ask questions like "How were sales last month?" without needing IT support.

• Faster Insights: Tasks that used to take days, like generating reports, now take seconds.

• Improved Collaboration: Teams can share insights easily, breaking down silos.

• Real-Time Analytics: Businesses can respond quickly to changes with live data.

Quick Comparison: Traditional BI vs. Conversational BI

NLIs are reshaping business intelligence by making data easy to use, faster to access, and more collaborative. Companies like JPMorgan Chase and Bank of America are already seeing results, with reduced analysis time and better customer engagement. By 2026, 30% of enterprise apps will include personalized AI interfaces. Now is the time to explore NLIs for your business.

Exploring Data Through Natural-Language & Conversational Analytics | Unscrambl

How Business Intelligence Has Changed: From Technical Tools to Conversational Analytics

Business intelligence has come a long way - from being reliant on complex, technical tools to embracing intuitive, conversational systems. This shift has opened up data access for businesses across the United States, making it easier for employees at all levels to interact with insights.

Old BI Tools: A Challenge for Non-Technical Users

In the past, traditional business intelligence tools often created more problems than they solved, especially for non-technical users. These tools made accessing data a frustrating process, limiting how organizations could fully embrace data-driven decision-making. In fact, fewer than 25% of organizations had successfully developed a strong data-driven culture [9].

Here’s where older BI tools fell short:

• Technical Complexity: Using these tools required advanced knowledge, such as writing complex queries or navigating unintuitive interfaces. This reliance on IT departments slowed down decision-making [8][9].

• Time-Consuming Reports: Generating reports could take days. Sales directors, for instance, had to submit requests to IT teams and wait for custom reports to be created [8].

• Rigid Systems: Many tools couldn’t adapt to evolving business needs without expensive customizations, which added delays and costs [9].

The result? A staggering 87.5% of organizations had low data and analytics maturity, with most falling into the "basic" or "opportunistic" categories [9]. Only 48.5% of companies were using data to drive innovation [9]. Porter Thorndike, Principal Product Manager at Cloud Software Group's IBI division, summed it up:

"Traditional BI typically involves curated data and applications driven by IT" [10].

This IT-heavy approach led to data silos, where only technically skilled individuals had access to insights [7]. These limitations set the stage for a new wave of tools powered by natural language interfaces (NLIs).

AI and NLIs: Simplifying Data Access for Everyone

Fast-forward to today, and AI-driven NLIs have transformed the way businesses interact with data. These tools remove the technical barriers of older BI systems, making it possible for anyone to gain insights quickly and easily.

Breaking Down Barriers: NLIs allow users to ask questions in plain language, eliminating the need for specialized skills. For example, a marketing manager can simply ask, "What were our top-performing campaigns last month?" without needing to understand SQL or database structures [11].

Instant Insights: Modern BI systems powered by NLIs provide real-time answers. What used to take days can now be done in seconds, with no need for IT intervention [13]. For instance, JPMorgan Chase’s NLU-powered chatbot reduced the time spent on data analysis by 40% [3]. Similarly, Bank of America’s virtual assistant, Erica, has served over 19.5 million users and handled over 100 million requests [3].

Vidya Setlur, director of research at Tableau, highlights this transformation:

"With NLP-enabled chatbots and question-answering interfaces, visual analytical workflows are no longer tied to the traditional dashboard experience. People can ask questions in Slack to quickly get data insights" [12].

Encouraging Data-Driven Thinking: By making data accessible to non-technical employees, NLIs are helping to build a workplace culture where decisions are guided by data [12].

The demand for these tools is only growing. The NLP market is expected to expand from $38.55 billion in 2025 to $114.44 billion by 2029 [3]. This growth reflects how conversational analytics is reshaping business intelligence, turning it from a niche tool into a resource that empowers employees across entire organizations to make smarter, faster decisions.

Main Benefits of Natural Language Interfaces in Business Intelligence

Natural language interfaces (NLIs) are reshaping business intelligence by making data easier to access, speeding up decision-making, and improving teamwork. These tools are helping organizations unlock the potential of conversational analytics, offering practical advantages that make a real difference.

Better Access for All Teams

NLIs make data accessible to everyone in an organization, not just the tech-savvy. By allowing users to interact with complex datasets using everyday language, they remove barriers that often limit insights to technical experts.

Simplifying Data Access: With NLIs, technical skills are no longer a requirement. A marketing manager can ask, “What were our conversion rates last quarter?” and instantly receive accurate, actionable insights - no IT department needed [5][6].

This shift is already paying off. When employees can easily access and interpret data, they feel more empowered to make strategic decisions. NLIs also boost data literacy by presenting visual insights that are easier for non-technical users to understand [5][4].

As Vidya Setlur, Director of Research at Tableau, puts it:

"NLP-driven analytical experiences have democratized how people analyze data and glean insights - without using a sophisticated analytics tool or craft[ing] complex data queries" [12].

This improved accessibility naturally leads to faster insights, which is the next big advantage.

Faster Insights and Decision-Making

Speed is where NLIs truly excel. Tasks that once took days or even weeks can now be completed in seconds, dramatically changing how businesses react to opportunities and challenges.

No More Waiting: Gone are the days of waiting for IT to generate custom reports. NLIs enable self-service analytics, giving users immediate access to the answers they need. This shift eliminates IT bottlenecks, allowing businesses to move from reactive to proactive decision-making [1].

Real-Time Responses: NLIs provide live data, enabling businesses to respond instantly to market changes [3]. In today’s fast-paced world, the ability to make quick decisions can mean the difference between seizing an opportunity or losing it.

Automated Reporting: Reporting processes have been revolutionized. With NLP-powered automation, detailed and customized reports are generated in seconds. For example, Access Holdings Plc integrated Microsoft 365 Copilot and Azure OpenAI Service, cutting code development time from eight hours to two and reducing chatbot deployment time from three months to just ten days [3].

Better Team Collaboration

NLIs don’t just streamline individual workflows - they also enhance collaboration across departments, fostering a more inclusive and data-driven work environment.

Breaking Down Silos: By democratizing data access, NLIs enable seamless communication between teams. Marketing can easily share performance metrics with sales, and operations can provide real-time insights to finance - all without needing technical translation [6].

Shared Insights: These tools present data in easy-to-understand formats, making it simpler for teams with diverse technical skills to have meaningful discussions [4]. This shared understanding ensures everyone is on the same page, leading to better decision-making.

In 2025, Power Digital Marketing embedded Snowflake Cortex into their platform, enabling natural language queries. This resulted in a roughly 30x improvement in the speed of gaining insights [14].

NLIs also ensure proper governance by recognizing user roles and permissions. This means employees access only the data they’re authorized to see, maintaining security while still encouraging collaboration [4].

The collaborative benefits extend beyond internal teams. GrowthLoop, for instance, integrated Snowflake’s Cortex AI to provide marketers and operators with self-serve data access. Anthony Rotio, Chief AI Officer at GrowthLoop, explains:

"Whether it's a performance marketer launching a new A/B test, a lifecycle marketer finding lapsed customers or a marketing ops lead reducing ticket volume, natural language removes the technical barrier. It accelerates time to insight, speeds up activation and ensures data remains governed and consistent" [14].

This shift creates a workplace where data drives conversations, decisions happen faster, and teams work together more effectively - all while supporting a culture of transparency and informed decision-making.

How Natural Language Interfaces Work in BI Today

Natural language interfaces (NLIs) are reshaping how businesses interact with data, making it easier for teams across industries to access insights and act faster. From generating automated reports to analyzing customer feedback, these tools are proving their worth in real-world scenarios, showing why conversational BI is becoming a game-changer for modern organizations.

Automated Reports and Real-Time Analytics

Today’s NLIs can generate detailed reports in seconds, providing real-time insights that help businesses make decisions on the fly. By automating this process, companies no longer have to wait for technical teams to build reports, which means they can respond to market changes much faster [3].

Predictive Analytics in Action: Walmart has incorporated AI-driven predictive tools into its KPI tracking. By analyzing historical sales data, customer habits, and external factors, Walmart reduced forecast errors by 30%, cut stockouts by 20%, and lowered excess inventory by 15% [16].

Access to real-time analytics allows businesses to adapt quickly to shifting market conditions. Instead of relying on outdated reports, decision-makers can use up-to-the-minute data to stay ahead of the curve.

Customer Feedback Analysis and Business Improvement

NLIs are also transforming how businesses extract value from customer feedback. These systems can process massive amounts of data, spotting trends and patterns that might be missed otherwise.

Real-Time Sentiment Monitoring: For example, American Express uses natural language processing (NLP) to analyze customer interactions as they happen. This has led to a 20% improvement in its Net Promoter Score and a 15% reduction in customer churn [3].

Deep Feedback Insights: NLIs don’t just flag sentiment as positive, negative, or neutral - they also group customer responses into themes using topic modeling. This helps businesses understand not only how customers feel but also why they feel that way [18].

During product launches, companies using sentiment analysis have seen a 25% boost in positive sentiment within 48 hours by quickly addressing customer concerns [3]. Additionally, Voice of Customer (VoC) platforms now analyze interactions from multiple sources - like social media, emails, and support tickets - giving businesses a complete picture of what’s working and what needs attention [18].

KPI Tracking and Quick Question Answering

NLIs also make it effortless for users to track key performance indicators (KPIs) and get answers to complex questions without needing technical expertise. By simply asking a question in plain language, users can receive clear, actionable insights.

Instant KPI Insights: For example, users can ask, “How are we performing against our quarterly goals?” and instantly get the data they need [16].

Easing the Support Load: Bank of America’s virtual assistant, Erica, showcases how conversational BI can reduce operational strain. Erica has served over 19.5 million users, handled more than 100 million requests, and reduced call center volume by 30%, all while increasing mobile banking engagement by 25% [3].

Personalized Intelligence: Netflix uses AI and machine learning to analyze user data - like viewing habits and search queries - to deliver personalized recommendations. This strategy has boosted user retention, increased viewing time, and supported steady subscriber growth [16].

NLIs are also evolving to handle more advanced tasks, such as processing multilingual inputs and analyzing internal sentiment to guide HR and leadership decisions. Some companies are even testing voice BI, where executives can query reports hands-free during meetings using wearable devices [17]. Integrating predictive analytics into KPI tracking has helped businesses reduce forecast errors by 30%, leading to smarter planning and better resource allocation [16].

These developments are redefining how businesses interact with their data, making advanced analytics accessible to everyone - from top executives to employees on the front lines.

How to Add Natural Language Interfaces to Your BI Strategy

Natural language interfaces (NLIs) can elevate your BI strategy by improving data access and speeding up insights. To make this a reality, you’ll need to evaluate your data systems, choose the right tools, and ensure your teams are well-prepared. With 81% of companies prioritizing customer experience as a competitive edge, adopting NLIs could be a game-changer for your organization [19].

Check Your Organization's Data Setup

Before diving into NLIs, take a hard look at your current data infrastructure. Can it handle conversational BI effectively? Start by reviewing your data architecture and consolidating information into a unified system.

Strong data governance is another must. This includes maintaining data integrity, ensuring privacy, and setting compliance measures - especially when employees from various departments start querying sensitive business information [15]. Governance policies should define data quality standards and access permissions, helping NLIs provide accurate and secure responses [1].

Don’t overlook the scope of your data. With unstructured data making up as much as 90% of new enterprise data, you’ll need systems capable of managing both structured and unstructured formats [19]. Metadata from data catalogs can help clarify relationships and terms, making your data more accessible [1].

A cloud-first approach often simplifies NLI integration. Cloud BI platforms offer scalability and AI capabilities, which help manage fluctuating query demands and support advanced natural language processing [15].

Once your data foundation is ready, it’s time to select the right tool to turn that data into actionable insights.

Use Querio for Conversational BI

Querio is a versatile platform designed to bring natural language interfaces into your BI strategy. It connects directly to major databases and enables AI-driven data querying that’s accessible to users of all technical levels.

With Querio, teams can ask complex questions in plain English and get precise, immediate answers - no need to wait for custom reports from technical staff. This conversational approach empowers employees to make quicker, data-backed decisions.

The platform also features dynamic dashboards for real-time KPI tracking and customization. Teams can tailor these dashboards to focus on the metrics that matter most to their objectives, ensuring every department gets the insights they need.

Another standout feature is Querio’s notebooks, which enable collaboration between business and data teams. Analysts can create detailed queries and share them with non-technical users, who can then tweak or extend these queries using natural language. This bridges the gap between technical and non-technical users, fostering better collaboration.

Querio’s direct database integration eliminates the need for complex pipelines or lengthy setups. It works seamlessly with existing data stacks, including catalogs, warehouses, and other BI tools, without requiring major architectural changes [1].

The platform’s AI capabilities also adapt to business-specific terminology, user roles, and organizational contexts. This ensures that insights are tailored - sales teams get sales-specific data, while marketing teams see metrics relevant to their campaigns, all from the same source [1].

Once the technology is in place, the next step is empowering your teams to use it effectively.

Train Teams to Use NLIs Effectively

Training is essential to ensure your teams can fully leverage NLIs. Hands-on workshops, webinars, and e-learning tailored to real-world scenarios can increase user confidence and engagement by over 50% [20].

Focus on practical applications that reflect day-to-day business tasks. For example, show how NLIs can streamline workflows or simplify data analysis. Hands-on training ensures employees know how to integrate NLIs into their routines [5].

"The hottest new programming language is English." - Andrej Karpathy, OpenAI founding member [14]

This quote underscores the importance of teaching teams how to interact with AI systems. Employees need to learn how to phrase questions clearly and interpret AI-generated responses accurately.

A gradual rollout is often more effective than launching NLIs across the entire organization at once. By introducing the system department by department, you can address issues early and refine both the technology and training processes based on real feedback [5].

Take a cue from Enovate Medical, which successfully integrated BI into healthcare operations. By involving IT specialists, clinicians, and data analysts in regular training sessions, they improved clinician efficiency by 25% and sped up data access by 30% [20].

Building internal user communities can also help. These groups encourage employees to share tips, ask questions, and exchange best practices, fostering collaboration and continuous learning [20].

Resistance to change is natural, but clear communication can help. Outline the specific benefits of NLIs for each department - sales teams might value pipeline insights, while operations teams may focus on efficiency metrics. Listening to feedback and addressing concerns quickly builds trust and confidence in the new system [5].

"Implementing NLP in business intelligence is a decisive step toward unlocking valuable insights and enhancing decision-making processes. Leveraging this tool can transform unstructured data into actionable intelligence, staying ahead in a competitive landscape." - Zac Amos, Features Editor, ReHack [19]

Conclusion: Why Natural Language Interfaces Are the Future of BI

Natural Language Interfaces (NLIs) are breaking down technical barriers, turning data analysis into an accessible, everyday tool for businesses. By making insights available to everyone - not just data experts - NLIs are reshaping how organizations approach Business Intelligence (BI).

The numbers speak volumes. The NLP market is forecasted to grow from $38.55 billion in 2025 to $114.44 billion by 2029, and it could reach $158.04 billion by 2032. This rapid growth highlights the undeniable shift toward simplifying data access and analysis for broader use [3].

We’ve already seen how companies like Bank of America, with its Erica assistant, and KPMG’s Ignite platform are using NLIs to streamline operations and enhance customer experiences. These tools aren’t just about convenience - they’re driving measurable results. For instance, organizations using AI-powered interfaces have reported up to a 3.5x boost in customer satisfaction rates [2]. The ability to ask questions in plain English and get immediate, actionable insights is a game-changer for decision-making. By 2026, it’s expected that 30% of new enterprise applications will include personalized AI interfaces - up from less than 5% today [4]. Businesses that wait risk falling behind competitors who are already embracing these advancements.

The time to act is now. Evaluate your current data systems, implement a strong NLI solution like Querio, and ensure your teams are ready to leverage these tools. NLIs aren’t just the future of BI - they’re the key to staying competitive in a rapidly evolving market.

FAQs

How do natural language interfaces (NLIs) help teams work together more effectively?

How Natural Language Interfaces Simplify Collaboration

Natural language interfaces (NLIs) are transforming how businesses access and use data by making it more straightforward for teams to collaborate. With NLIs, employees can simply ask questions in plain English - no technical knowledge, like SQL, required. This means data insights are no longer limited to experts; everyone in the organization can tap into valuable information. The result? People in different roles can quickly find the answers they need without waiting on specialized teams, cutting down delays and improving workflow.

These tools also speed up decision-making by providing instant insights. For instance, a marketing manager can effortlessly check how a campaign is performing, while a sales leader can identify top-performing regions in real time. With data readily available to all, teams can align their strategies, make informed decisions together, and work more efficiently across departments. This shared access to information fosters better teamwork and ensures everyone is on the same page.

How can businesses integrate natural language interfaces into their current BI systems?

To bring natural language interfaces (NLIs) into your business intelligence (BI) systems, start by pinpointing the specific challenges you aim to solve. Are you looking to make data queries easier or improve access for team members without technical expertise? Defining these goals upfront will help shape the integration process and ensure the solution meets your needs.

The next step is preparing your data. It’s essential to have clean, organized, and structured data to support accurate natural language processing (NLP). Skipping this step can lead to messy results and unreliable insights, so don’t underestimate its importance.

Finally, select an NLI solution that integrates smoothly with your current BI tools. Once it's up and running, invest time in training your team to use the interface effectively. Encourage a data-driven mindset across your organization to fully leverage the advantages of conversational analytics.

How do natural language interfaces (NLIs) ensure data security and privacy for non-technical users?

Natural language interfaces (NLIs) place a strong emphasis on data security and privacy, employing measures like robust encryption, multi-factor authentication, and role-based access controls. These tools work together to make sure that sensitive information is only accessible to those with proper authorization.

To take security a step further, many NLIs implement a Zero Trust security model. This means every access request is thoroughly verified - no matter the user's role or location. By doing so, the system reduces the chances of unauthorized access and bolsters overall security.

On top of that, NLIs undergo regular compliance checks, risk evaluations, and follow strict data governance policies. These practices ensure that while teams can easily access and use data for smarter decision-making, the integrity and security of the data remain intact.

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