NLQ + Governance = Querio: The Right Way to Do AI BI
Business Intelligence
Jul 29, 2025
Explore how the integration of natural language querying and strong data governance revolutionizes business intelligence by making data accessible and secure.
Querio combines Natural Language Querying (NLQ) and data governance to simplify how you interact with data while ensuring security and compliance. With Querio, you can ask questions in plain English and get instant, accurate insights without technical expertise. At the same time, its governance framework ensures data accuracy, consistency, and protection, making it a reliable solution for business intelligence.
Key Takeaways:
NLQ for Everyone: Ask questions like “What were last quarter’s sales?” and get immediate answers without needing SQL or complex dashboards.
Governance Built-In: Querio enforces consistent definitions, secure data access, and compliance with privacy laws.
Direct Data Access: Connects to live data sources (e.g., Snowflake, BigQuery) without duplicating datasets, ensuring up-to-date insights.
Real-Time Dashboards: Create interactive visualizations and collaborate across teams for faster decision-making.
Security Standards: SOC 2 Type II certified, with AES-256 encryption and role-based access for safe data handling.
Querio’s blend of user-friendly querying and enterprise-grade governance solves the challenges of traditional BI tools, making data insights accessible and secure for all teams.
Ep. 3 - This is the “Killer App” of Enterprise AI
Querio's Main Features: Connecting NLQ and Governance
Querio combines user-friendly data access with enterprise-level governance. Its architecture ensures that every natural language query (NLQ) is backed by strong security protocols and consistent data definitions, creating a solid framework for AI-powered business intelligence. By merging simplicity with rigorous governance, Querio provides accurate and secure insights.
Natural Language Querying for Quick Insights
Querio transforms how teams interact with data by letting users ask questions in plain English and receive instant, accurate visualizations. Its AI-powered agent translates these queries into precise SQL commands, connects directly to live data sources, and presents clear visual outputs. This allows teams to explore or expand metrics without needing technical skills.
Behind the scenes, Querio handles complex joins and calculations, delivering results as easy-to-read charts and graphs. For example, finance teams can quickly analyze budget variances, product managers can track feature adoption, and sales leaders can monitor pipeline performance - all without writing a single line of code. This approach makes data accessible across the organization, speeding up decision-making at every level.
Direct Data Connections Without Copying
Querio establishes read-only connections to major data warehouses like Snowflake, BigQuery, and Postgres without duplicating data. This eliminates the risks of working with outdated information, a problem that still affects 80% of companies making critical decisions based on stale data [5].
To ensure security, Querio uses encrypted credentials and adheres to enterprise security standards, providing access to up-to-date data while avoiding the costs and complications of maintaining duplicate datasets. Thanks to these direct connections, teams can monitor business metrics in real time, quickly adapting to changes - whether it's tracking daily sales or analyzing customer behavior trends.
Governance Through Context Layer and Security
While direct connections keep data fresh, Querio's context layer ensures queries are secure and standardized. This governance framework allows data teams to define business logic, table relationships, and metric calculations once and apply them consistently across all queries. This shared framework ensures that terms like "revenue", "active customers", or "conversion rate" are interpreted the same way across the organization.
Querio meets SOC2 Trust Service Criteria for security, privacy, confidentiality, processing integrity, and availability [2]. It uses role-based access control and follows least privilege principles [2].
Data breaches remain a serious concern - 54% of organizations reported third-party breaches in the past year [3], and noncompliance-related breaches add an average cost of $220,000 [4]. Querio's governance framework minimizes these risks while maintaining the accessibility that makes self-service analytics valuable.
The platform operates on secure AWS infrastructure, leveraging AWS SOC3 Certification and working toward SOC2, ISO 27001, and ISO 9001 certifications [2]. These robust security measures enable organizations to gain fast, reliable insights through AI-driven business intelligence.
How NLQ Improves Self-Service Analytics
Natural Language Querying (NLQ) breaks down technical barriers, empowering users to explore and analyze data on their own. With Querio's AI-powered system, business users can uncover insights without relying on technical support, enabling quick and independent analysis. This user-friendly approach paves the way for effective self-service analytics across various roles and expertise levels.
Currently, fewer than 20% of companies fully utilize complex, unstructured data [1]. Querio's NLQ features change this by making data exploration as simple as asking questions in everyday language.
Supporting Users of All Skill Levels
Querio's AI-driven querying system is designed to support users across all experience levels. For instance, marketing managers can evaluate campaign performance, finance teams can monitor budget discrepancies, and product managers can track user engagement - all without needing to understand intricate database structures.
The platform's interactive workspace fosters collaboration between business and data teams. Querio’s AI agent translates natural language questions into SQL queries, runs them on live data sources, and delivers results as easy-to-understand visualizations. While technical teams can step in to refine queries when necessary, business users maintain the ability to explore data independently.
Users can dive deeper into metrics, compare time periods, and segment data simply by phrasing their queries conversationally. For example, a sales manager might ask, "Show me Q4 revenue by region", then follow up with, "Now break it down by product category", or "Compare this to last year’s Q4." This seamless flow of queries creates a dynamic and intuitive analytical experience.
With real-time analytics, Querio ensures insights reflect the latest business conditions. Unlike traditional BI tools that rely on static data snapshots, Querio connects directly to live databases, delivering up-to-the-minute insights. This is especially valuable for time-sensitive tasks like tracking daily sales or evaluating the immediate impact of marketing campaigns. These capabilities underscore Querio’s focus on delivering accurate, secure, and accessible insights.
The flexibility of NLQ naturally extends to real-time, interactive dashboards that simplify decision-making.
Faster Decision-Making with Interactive Dashboards
Querio’s drag-and-drop dashboards transform raw data into clear, actionable visualizations, speeding up the decision-making process. The platform’s charting tools allow users to craft visualizations that bring clarity to complex datasets.
Decision-making becomes quicker when teams can generate insights during meetings instead of waiting for follow-up sessions to gather data. For example, a product team reviewing user engagement metrics can immediately explore different user segments, analyze feature adoption rates, and identify trends - all in real time, enabling more informed strategic discussions.
These dashboards serve both executive and departmental needs. Executives can monitor high-level KPIs through scheduled reports, while department managers can focus on specific metrics using the same data foundation. This layered approach ensures everyone in the organization works with consistent information while still accessing the details necessary for tactical decisions.
Querio’s collaboration tools make it easy to share insights across teams. For instance, if a finance analyst spots a significant trend in customer behavior, they can instantly share the dashboard with key stakeholders, add annotations, and kickstart meaningful, data-driven conversations. This eliminates the delays traditionally caused by waiting for technical teams to prepare reports or visualizations.
As self-service analytics tools have evolved, analytics adoption has grown significantly. By 2024, adoption rates surged from 35% to 50%, driven by augmented analytics solutions that have made these tools accessible to a wider range of users [6]. Querio also helps organizations move beyond simple reporting by enabling KPI tracking and storytelling. Users can create narrative dashboards that walk viewers through key insights, emphasize important trends, and provide the context needed for impactful decision-making.
Governance in Querio: Building Trust and Compliance
Strong data governance is the foundation of Querio’s analytics, blending consistent definitions with top-tier security. Querio's framework ensures that every query, dashboard, and insight is built on the same reliable definitions, all while upholding strict security standards. This approach not only guarantees clarity and compliance but also empowers business users to trust their analytics. With confidence in their data, decision-makers can act faster and with greater certainty, eliminating concerns over data accuracy. Below, we’ll explore how Querio achieves this through standardized definitions and secure analytics.
Shared Definitions for Consistent Analytics
Querio’s context layer acts as a central hub for business definitions, ensuring everyone across the organization uses the same metrics and calculations. This eliminates discrepancies, such as marketing and finance reporting different customer acquisition costs due to inconsistent methodologies.
For example, when a sales manager asks for "monthly recurring revenue", the context layer ensures the calculation matches the one used in executive reports. Even for complex metrics that pull from multiple data sources, Querio automatically applies the correct business rules and joins, maintaining consistency.
A semantic layer bridges the gap between raw data and business users by translating technical information into terms that are easy to understand [7]. Querio’s semantic layer enforces uniform definitions and calculations organization-wide. For instance, whether customer churn is analyzed by the customer success team, product team, or executives, the platform ensures the same definition is applied.
One global financial services company used a semantic layer to unify metrics across the organization, enabling better decision-making and improving product placement performance [7]. Querio also enhances data interpretation by providing detailed metadata for each metric. This includes the calculation method, data sources, and business context, ensuring users always have the background they need to interpret results accurately [8].
Security and Compliance Features
While shared definitions foster consistency, Querio’s robust security features protect data at every level. The platform is SOC 2 Type II certified, meeting rigorous standards for security, availability, processing integrity, confidentiality, and privacy. This certification demonstrates Querio’s commitment to safeguarding sensitive business information.
All data connections are secured with AES-256 encryption, providing strong protection. Querio connects directly to data warehouses like Snowflake, BigQuery, and Postgres using read-only, encrypted links. This approach eliminates the risks of data duplication while ensuring real-time access to up-to-date information.
Granular permissions offer precise control over who can access data, from entire databases down to individual tables and columns. This allows organizations to provide general access to business metrics while restricting sensitive financial or personal data to authorized personnel.
Audit trails further enhance security and compliance by recording every query, dashboard creation, and data access event. These logs include user details, timestamps, and data accessed, giving security teams a complete record for review.
A global manufacturing company leveraged a semantic layer to streamline data usage across the enterprise. This reduced redundant modeling efforts, improved data literacy, enabled self-service analytics for a large user base, and cut cloud costs - all while maintaining a predictable cost structure for analytics [7]. This example highlights how strong governance can balance security with operational efficiency.
Querio’s governance also extends to business processes. The platform includes version control for metric definitions, tracks changes to business rules, and offers rollback options when needed. These features allow governance policies to adapt to evolving business needs while preserving historical data integrity for trend analysis and regulatory compliance.
How to Implement Querio's AI BI Solution
To integrate Querio into your organization, focus on configuring its natural language query (NLQ) and governance features, training teams to use the platform effectively, and setting up metrics to track both adoption and success. A structured approach ensures a smoother rollout, faster adoption, and a better return on your investment.
Setting Up NLQ and Governance Features
A successful Querio deployment begins with secure data connections and a well-defined governance framework. Start by connecting Querio to your data warehouse using read-only, encrypted credentials to ensure live and secure data access.
Next, set up the context layer to enable effective natural language queries. This involves defining business metrics, mapping table relationships, and creating a glossary of terms. For example, you might define "monthly recurring revenue" with its calculation method, data sources, and rules. Begin with essential metrics and expand over time to ensure users receive accurate, consistent results when they ask questions in plain English.
Granular permissions are key for controlling data access. While general business metrics can be widely available, sensitive information - like salaries or confidential financial data - should be restricted to authorized personnel. Establish clear governance policies detailing who can access specific data and under what conditions.
Document the definitions of metrics and business rules within Querio’s governance framework. Include calculation methods, data source priorities, and how to handle edge cases. Use version control to track changes, allowing for rollbacks when needed, which helps maintain consistency for analysis and compliance reporting.
Once the technical setup is complete, shift your focus to preparing your teams to make the most of Querio.
Training Teams on the Platform
Training is essential for driving adoption, with 55% of organizations reporting its effectiveness in boosting BI usage [11]. Develop a tailored training program that meets the unique needs of each department while emphasizing Querio’s natural language and governance features.
Identify departmental champions to lead adoption efforts. Research shows that organizations using local champions are 22% more likely to see developers embrace AI [10]. These champions should receive advanced training and act as resources for their teams.
Offer role-specific training to show how Querio addresses specific challenges. For instance, teach marketing teams to analyze campaign performance, help finance teams generate budget reports, and guide operations teams in tracking key performance indicators. Formal training can increase the likelihood of widespread adoption by about 20% [10].
Leadership advocacy is another critical factor. As Brian Houck from Microsoft highlights: “Just as simple as having leadership strongly advocate for the use of AI tools makes developers seven times more likely to be daily users” [10]. Ensure executives actively promote Querio and demonstrate its value through their own usage.
Teach users how to craft effective natural language queries. While Querio can interpret conversational questions, users benefit from learning to specify time periods, filters, and the type of analysis they need. Provide examples of well-structured queries and show how the platform interprets different styles.
Host regular training sessions to introduce new features and share best practices. With nearly half of organizations prioritizing workforce training over the next 12 to 18 months [9], creating a collaborative environment where users share tips and celebrate successes is a smart strategy.
After training, measure success by tracking user engagement and business outcomes.
Measuring Success and Growing Usage
To evaluate Querio’s impact, track both quantitative metrics and qualitative feedback. Organizations often see 25-40% improvements in data management metrics within a year of implementing structured governance [12]. Establish baseline metrics before deployment to measure progress and demonstrate ROI.
Monitor adoption metrics like active users, daily queries, and dashboard creation rates. Identify which departments are embracing Querio quickly and where additional support is needed. These insights can help ensure teams move from basic queries to more advanced analytics.
One example of measurable success comes from a U.S.-based clinical research organization. After implementing a centralized data catalog, researchers located datasets 60% faster, reduced report errors by 35%, and cut exploratory analysis time by 45%. Within six months, they documented over $2.3 million in cost savings and productivity gains, directly tied to their governance initiative [12].
Share results that highlight faster decision-making, reduced manual reporting, and improved access to insights. For example, track how Querio shortens monthly reporting cycles or enables more frequent performance reviews.
Measure data quality improvements and governance compliance. Metrics like consistent definitions across departments, fewer data-related errors, and adherence to security policies demonstrate Querio’s value. Organizations with strong governance frameworks report benefits such as improved data security (66%) and fewer compliance breaches (52%) [12].
Gather user feedback through surveys and interviews to identify areas for improvement. Use this input to refine training, adjust governance policies, and prioritize new features.
Expand Querio’s usage gradually, introducing it to more departments and use cases as initial implementations succeed. Share success stories to encourage adoption across the organization. Notably, 64% of business owners believe AI will strengthen customer relationships and productivity, while 60% expect it to drive revenue growth [13].
Finally, establish a regular review process to assess and refine governance metrics as your business evolves, ensuring Querio continues to deliver valuable insights and support organizational goals.
Conclusion: Getting the Most from AI BI with Querio
In the world of business intelligence, striking the right balance between accessibility and security is no small task. Querio bridges this gap with its blend of natural language querying and strong data governance. With Querio, businesses no longer have to choose between making data widely accessible and maintaining strict control over sensitive information.
This platform eliminates technical hurdles, delivering secure and reliable data access through its context layer, SOC 2 Type II certification, and a 99.9% uptime SLA - ensuring the dependability enterprises need.
The numbers highlight why this balance is critical. By 2027, an estimated 60% of organizations will fail to unlock the value of their AI initiatives due to poor data governance frameworks [14]. Meanwhile, 87% of organizations anticipate generative AI will have a major impact on their operations [14]. The urgency to establish effective governance has never been greater, and Querio rises to the challenge.
Querio seamlessly integrates governance into the analytics experience. Users can ask questions in plain language, while data teams retain control through encrypted credentials and read-only connections. This ensures that data remains secure, accurate, and actionable for informed decision-making [15].
On top of its technical strengths, Querio’s transparent pricing - starting at $14,000 per year with unlimited viewers - makes enterprise-grade AI BI accessible to businesses of all sizes. This straightforward model empowers teams without the headaches of complicated pricing structures.
For organizations looking to embrace the future of analytics, Querio offers a clear path forward. By combining intuitive natural language querying with rigorous governance, it creates a system where decisions are not only faster but also more reliable. As data volumes grow and AI adoption accelerates, Querio’s approach provides the solid foundation businesses need - not just to keep up, but to thrive.
The future of business intelligence is AI-powered. The question isn’t whether to adopt it, but how to do it effectively. Querio delivers the tools and framework to ensure your data remains both accessible and secure, guiding you confidently into the AI-driven future of business analytics.
FAQs
How does Querio keep data secure and compliant while enabling natural language queries?
How Querio Keeps Your Data Safe
Querio places a strong emphasis on data security and regulatory compliance, ensuring you can work confidently without sacrificing ease of use. Data at rest is encrypted with AES-256, while data in transit is safeguarded using HTTPS with TLS 1.3. On top of that, strict access controls and data classification systems ensure that only authorized users can access sensitive information.
To stay compliant with industry standards like ISO27001, Querio implements rigorous risk management strategies and policy enforcement measures. This means you can use natural language to explore your data, knowing it’s protected, accurate, and aligned with regulatory requirements.
What are the key steps to successfully implement Querio's AI-driven BI solution in an organization?
To get the most out of Querio's AI-driven BI platform, here's a roadmap to guide your implementation process:
Assess your readiness: Start by evaluating your organization's current setup - look at your infrastructure, the quality of your data, and your team's skill set. This step ensures you're set up for a smooth integration.
Tailor the solution to your needs: Choose the AI functionalities that align with your business goals. Focus on features that enhance natural language queries and self-service analytics to meet your specific requirements.
Get your data in order: Clean and organize your data while keeping governance policies in mind. This ensures your data remains accurate, secure, and compliant.
Set a clear strategy: Create a BI plan with well-defined objectives. This should include measurable KPIs and any necessary platform adjustments to align with your goals.
Run a pilot program: Test the platform with a specific team or department to gather feedback. Use this input to fine-tune the solution before scaling up.
Launch and provide support: Roll out the platform across your organization. Offer training sessions to ensure everyone adopts the new system and establish ongoing practices to maintain governance and support.
By following this approach, your organization can use Querio's AI BI platform to make smarter, quicker decisions while keeping your data secure and reliable.
How does Querio ensure data accuracy and consistency across departments?
How Querio Maintains Accurate and Reliable Data
Querio takes a comprehensive approach to ensure data remains accurate and consistent. By combining a strong governance framework with cutting-edge AI tools, it enforces clear policies and standards to uphold data integrity. On top of that, it automates essential processes like data validation, classification, and monitoring, making data management more efficient.
With AI copilots handling repetitive tasks, Querio minimizes human error and ensures every department has access to secure, dependable, and current information. This not only streamlines operations but also boosts confidence in your data, enabling smarter decisions across the board.