English In, Insights Out: Querio’s NLQ for Any Warehouse

Querio's Natural Language Query (NLQ) simplifies how businesses access data by allowing users to ask plain English questions and receive visualized answers instantly. No SQL skills needed. Connect directly to your data warehouse (e.g., Snowflake, BigQuery, Postgres) without moving data or compromising security.

Key benefits:

• Ask questions like: "What were our top-selling products last quarter?"

• Get results instantly: Charts, tables, or graphs tailored to your needs.

• Boost productivity: Teams access insights without relying on analysts.

Setup essentials:

1. Connect Querio to your data warehouse with read-only, encrypted access.

2. Configure permissions using role-based access control (RBAC).

3. Ensure compliance with standards like SOC 2 Type II, GDPR, and CCPA.

Why it matters: Teams can make faster, data-driven decisions, saving time and reducing dependency on technical support. From tracking sales trends to analyzing customer behavior, Querio empowers every department to turn data into actionable insights.

Query your Data Lake with Natural Language | Amazon Web Services

Getting Started: What You Need for Querio's NLQ

To get started with Querio's natural language querying (NLQ), you'll need administrative access to your data warehouse, valid credentials, and alignment with your organization’s security policies. Querio integrates seamlessly into your existing setup, respecting your current data governance framework while handling much of the technical work.

Connecting Querio to Your Data Warehouse

Querio integrates directly with Snowflake, BigQuery, and Postgres, eliminating the need for data replication or migration. Your data stays exactly where it is, preserving your architecture and security controls.

The setup involves configuring database connectivity, sharing schemas, and enabling query execution. By providing your database schema - tables, columns, and their relationships - you give Querio the context it needs to generate precise queries.

Here’s how it works: Querio combines user queries with your data model to create prompts, uses AI APIs to generate SQL, and runs the queries directly in your database [1]. This ensures your natural language inputs are translated into accurate and efficient SQL queries.

Security remains a top priority. Querio connects without replicating data, and your existing user permissions and access controls stay intact. You can configure targeted access to balance usability with security.

Setting Up Data Access and Permissions

Once Querio is connected, establishing proper access controls is critical for secure operation. Role-based access control (RBAC) is a key measure - create service accounts with carefully defined access levels tailored to your needs.

Stick to the least privilege principle by granting Querio access only to the tables and schemas your team needs. Use dedicated, read-only database users to minimize exposure while ensuring access to relevant business data.

Enable multi-factor authentication (MFA) for all users, especially those with broader permissions [2]. Regularly audit access to ensure permissions align with current roles, updating them as needed [2].

To further protect your data, review access logs regularly for unusual activity [4]. As more team members begin using natural language queries, this monitoring becomes even more important.

It’s worth noting that data breaches led to the loss of 446 million records in the U.S. in 2018 alone [5]. Properly setting up permissions isn’t just a good practice - it’s essential for safeguarding your organization’s data.

Compliance and Security Standards

Querio prioritizes data protection and meets SOC 2 Type II compliance, with additional certifications like ISO 27001 and ISO 9001 in progress [2]. These certifications reflect its commitment to meeting enterprise-grade security standards.

Encryption standards are robust. Data at rest is secured with AES-256 encryption, while data in transit is protected by HTTPS/TLS 1.3 [3]. Whether data is stored temporarily or transferred between your systems and Querio, these protocols ensure it remains secure.

Querio operates within a secure Virtual Private Cloud (VPC) hosted on Amazon Web Services (AWS), offering enterprise-level infrastructure security [2][3]. Secure connections are maintained using SSH tunneling, SSL/TLS, and IP whitelisting [3].

Privacy compliance is comprehensive. Querio adheres to CCPA and GDPR, ensuring data handling meets both U.S. and international privacy standards [2][3]. Upon onboarding, Querio signs a Data Processing Agreement (DPA) to formalize its data protection commitments [2][3].

Querio also follows strict data retention policies. It does not permanently store customer data, and any temporary storage required for processing is secured within the encrypted VPC environment [3]. Regular vulnerability assessments and architecture reviews help ensure these measures remain effective [3].

For questions about specific security policies or compliance documentation, you can reach out to hello@querio.ai for further details [2].

Using NLQ to Query Any Data Warehouse

Once your data warehouse is connected and permissions are set, you can dive into analyzing your data by asking plain English questions. Querio's NLQ makes it easy to get insights instantly.

Writing Plain English Questions

With your warehouse ready, you can ask questions just as you would in a conversation about business metrics. The trick is to be specific and clear. For instance, instead of asking something vague like "How are sales doing?", try framing it like this: "What were our total sales in USD for Q3 2024?" or "Show me monthly revenue growth from January 2024 to December 2024." The more precise your question, the better Querio can deliver the results you need.

Adding context is key. If you're interested in regional performance, say "sales by state" rather than "regional sales." For inventory-related questions, specify units: "How many pounds of inventory are in our California warehouse?" This level of detail ensures the results align with your expectations.

Querio also understands natural date references. You can ask things like "What were our sales last month?" or "Show me year-over-year growth for 2024 compared to 2023." Using terms like "last quarter", "this year", or "previous month" eliminates the need for exact dates and keeps things simple.

If the first answer isn't quite what you're looking for, tweak your question. Adding more details or rephrasing can help Querio zero in on the exact insight you're after [6].

Viewing and Reading Results

Once Querio processes your query, it presents results in formats designed for clarity and analysis. Depending on the nature of your question, you'll see visualizations like charts, tables, or dashboards.

For example, if you ask about quarterly revenue, Querio might display "$2,847,392.15 for Q1 2024", formatted in standard U.S. conventions. Sales trends generate line charts, while regional data often appears as bar charts or maps. These visualizations include hover-over details and can be customized for reports or presentations.

When detailed breakdowns are needed, Querio provides structured tables with clear headers, sorted data, and totals where relevant. For example, asking "Show me sales by product category and state for 2024" produces a table you can export or use as a starting point for further analysis.

Querio also tracks your query history, letting you revisit past questions or refine earlier analyses. This feature is especially handy during exploratory sessions, where one question often leads to another.

These tools make it easy to move from raw data to actionable insights.

Real Examples for Business Metrics

Querio's NLQ can transform how you approach key business metrics. Here are some practical examples:

• Revenue Analysis: Questions like "What's our total revenue in USD by month for 2024?" help finance teams monitor performance and spot seasonal trends. Follow up with "Which states contributed the most revenue in Q4 2024?" to dig deeper into geographic drivers.

• Inventory Management: Operations teams can stay on top of stock with queries like "How many pounds of raw materials do we have across all warehouses?" or "Which products have less than 1,000 units in stock?" These insights highlight supply chain status.

• Customer Behavior: Marketing and sales teams can learn more about customer trends by asking, "What's our average order value in USD for customers in Texas?" or "How many new customers did we acquire each month in 2024?"

• Performance Comparisons: Compare results over time with questions like "Compare our 2024 sales growth to 2023 by quarter" or "Which product categories grew the fastest this year?" These comparisons help pinpoint strengths and areas for improvement.

• Operational Metrics: Operations teams can track efficiency with queries such as "What's our average shipping time in days by state?" or "How many orders were processed each week in December 2024?" These insights help identify bottlenecks and improve service levels.

Querio’s natural language approach makes it easy to explore your data. Each answer can lead to new, more focused questions, turning your curiosity into actionable insights about your business performance.

Getting More Value: Visualizing and Sharing Insights

Raw data is just the starting point. The real impact comes when these insights are transformed into clear, shareable visualizations that are tailored to the needs of your stakeholders. By presenting data in a way that's easy to understand and act on, you empower better decision-making across your organization. Querio simplifies this process by converting raw query results into actionable visualizations, customized for each audience.

Customizing Visualizations for Stakeholders

Not every stakeholder looks at data the same way. A CFO might focus on big-picture revenue trends, while a regional sales manager needs detailed breakdowns by territory or product line. Querio makes it simple to create dashboards that cater to these different needs by offering customizable metrics, filters, and visualization formats [7].

For executives, dashboards should prioritize clarity and focus on key performance indicators. For instance, a clean revenue chart showing monthly growth in U.S. dollars - like "$3,247,891.23" - is far more effective than a cluttered table. Placing the most critical metrics at the top ensures they grab attention immediately.

Querio takes the complexity out of dashboard creation by automatically identifying key data fields and enabling real-time adjustments. This means even non-technical teams, like marketing, can build dashboards to track customer acquisition metrics without needing advanced technical skills [9].

Operational teams can benefit from interactive filtering options. For example, a warehouse manager might want to toggle between views of inventory by location, product category, or supplier. Using consistent color coding - green for positive metrics, red for issues, and neutral tones for baseline data - makes it easier to spot trends or outliers. These visualizations set the stage for seamless, automated sharing.

Scheduling and Sharing Reports

After creating tailored visualizations, the next step is ensuring those insights reach the right people at the right time. Manual report distribution can be time-consuming and prone to errors, but automation eliminates these hurdles, allowing teams to focus on strategic priorities [8]. Here are some tips for effective report sharing:

• Set clear schedules: Match report frequency to stakeholder needs - daily updates for operational teams, weekly summaries for department heads, and monthly overviews for executives. Align these schedules with your data refresh cycles to ensure accuracy [8].

• Customize content: Use parameters and filters to tailor reports for different audiences. For example, detailed performance metrics for marketing teams can come from the same dataset as high-level summaries for leadership [8].

• Secure access: Implement strong data governance policies to ensure sensitive information, like financial reports, is only accessible to authorized personnel [8].

• Monitor workflows: Set up alerts to catch issues like failed deliveries or data refresh errors. Keeping an audit log ensures transparency and accountability [8].

For teams that thrive on real-time collaboration, consider integrating reports directly into communication platforms. This keeps everyone aligned without the need to toggle between multiple systems.

Before rolling out automated workflows company-wide, start with a small pilot project to identify any hiccups and fine-tune the process [8]. Training users on how to interpret reports and where to go for support is also essential. Even the most well-designed reports lose their impact if the insights aren't understood or actionable [8].

Data Governance and Best Practices

Effective data governance turns natural language querying (NLQ) from a handy feature into a dependable business asset. Without the right governance in place, even the smartest AI tools can generate flawed insights, leading to costly mistakes. In fact, poor data quality costs businesses an average of $12.9 million every year [12][15].

Data breaches are another major concern, with financial institutions facing an average cost of $5.72 million per breach and global averages sitting at $4.45 million [10]. A strong governance framework is essential to reduce these risks.

Building a Governed Context Layer

A governed context layer acts as a bridge between complex database structures and everyday business language. This layer simplifies technical database schemas into terms that make sense to non-technical teams, allowing them to ask meaningful questions without needing deep technical skills [17].

Here’s how it works: data teams set up the context layer to define how tables connect, establish metric definitions, and standardize business terms. For example, a query like "monthly recurring revenue" automatically pulls the correct data and performs the necessary calculations.

Consistency is key. Standardizing naming conventions, units, and formats across all data sources ensures clarity. For instance, a revenue figure should always appear as "$3,247,891.23", no matter which table it comes from.

Different departments might need tailored contexts, but the underlying governance must stay uniform:

Collaboration is essential for building this layer. Data teams bring technical expertise, while business teams define the questions that matter most. Regular reviews ensure the context layer evolves alongside business needs, maintaining both accuracy and relevance.

This layer doesn’t just standardize metrics; it creates a foundation for secure, trustworthy insights across the organization.

Maintaining Data Security and Quality

Security and quality go hand in hand to ensure reliable insights. Querio tackles both through multiple layers of protection, starting with SOC 2 Type II certification and compliance with CCPA and GDPR regulations.

The platform minimizes risks by using direct, encrypted, read-only connections instead of duplicating data. Access controls follow a least privilege approach, limiting users to only the data they need for their roles.

Maintaining data quality is an ongoing effort. Regular audits help catch inaccuracies, inconsistencies, or anomalies before they impact decisions [12][13]. Automated monitoring tools track critical quality metrics like completeness, accuracy, and timeliness, reducing the need for manual checks [16].

Data validation rules act as gatekeepers, stopping problematic entries from ever entering the system [11]. For example, these rules might ensure revenue figures are positive, dates fall within expected ranges, or required fields are properly filled out. Catching errors early prevents a ripple effect of issues across multiple reports.

User education is just as important. Teams need to know not only how to ask the right questions but also how to interpret results and spot potential errors. Stakeholder involvement is crucial to maintaining high-quality data throughout the organization [14].

SQL vs. NLQ: Efficiency Gains

Once data integrity is ensured, the focus shifts to how efficiently teams can access and use that data. The difference between traditional SQL querying and natural language querying is more than just convenience - it can have a measurable impact on business operations.

SQL requires specialized skills, limiting access to data insights. For instance, answering a simple question like "What were our top-selling products last quarter?" might involve joining tables, applying filters, and aggregating results. This process can take 30–60 minutes per query and often requires significant training.

Natural language querying removes these barriers. The same question can be answered in seconds, with results automatically formatted and visualized. This speed isn’t just a time-saver - it compounds across an organization as multiple teams access insights more frequently.

The benefits are even greater for complex queries. Analyzing customer behavior across touchpoints might take hundreds of lines of SQL code and extensive testing. With NLQ, business users can simply describe their goals, and the AI handles the technical heavy lifting.

While NLQ is ideal for routine business questions, SQL still has its place for complex analytical work. A balanced data strategy uses both methods, matching the right tool to the specific task. This approach ensures every team can turn raw data into actionable insights without unnecessary delays or technical hurdles.

Conclusion: Transforming Business Intelligence with Querio

Querio is changing the way organizations access and use data. By eliminating the technical hurdles that once confined data insights to specialized teams, it makes analytics accessible to everyone across the organization [19]. This shift allows departments to make quicker, more informed decisions based on data they can access instantly.

The ability to query data in plain English, as detailed earlier, creates a new level of efficiency for businesses. With less than 20% of companies currently utilizing their unstructured data due to its complexity [19], Querio opens the door to a wealth of untapped opportunities. Instead of waiting for IT support, teams can now retrieve insights on their own, enhancing their ability to adapt and respond to changes swiftly [19].

Querio's fast response to natural language queries enhances decision-making in real-time. Whether during meetings or other critical moments, this speed ensures that businesses can act decisively when it matters most [19].

Expanding access to data also builds data literacy, empowering teams across the board to make better decisions. With routine questions no longer requiring input from data scientists, technical experts can dedicate their time to solving more advanced challenges [19].

This shift represents more than just a new tool - it’s a change in how organizations operate. Embedding natural language querying (NLQ) into daily workflows accelerates strategic decision-making and aligns with the growing trend of conversational interfaces. Businesses are increasingly seeing the value of making data access as simple as a conversation.

Organizations that fully integrate NLQ into their operations treat it as the primary way to access business intelligence [18]. This approach transforms data into a conversational resource, enabling smarter, faster decisions that benefit every department.

FAQs

How does Querio protect our data and ensure compliance when connecting to a data warehouse?

Data Security and Compliance with Querio

Querio takes data security and compliance seriously, employing measures like role-based access control (RBAC) and multi-factor authentication (MFA) to tightly manage who can access sensitive information. By limiting access to authorized users only, they add an extra layer of protection to your data.

To further secure your information, Querio uses read-only credentials for data connections. This ensures your data stays safe and unaltered during use.

On top of that, Querio is SOC 2 compliant, adhering to rigorous standards for security, availability, and confidentiality. These measures collectively ensure a secure and dependable connection to your data warehouse, so you can focus on gaining insights without worrying about safety.

How do I set up Querio's NLQ for a new data warehouse, and how long does it usually take?

Setting up Querio's Natural Language Query (NLQ) for a new data warehouse is a quick and straightforward process, usually taking just a few hours. Here's how it works:

• Connect your data warehouse: Easily and securely link your database to Querio. Popular platforms like Snowflake and BigQuery require minimal effort to get started.

• Configure the interface: Adjust the NLQ interface to match your data structure and business needs, ensuring it works seamlessly with your setup.

• Deploy dashboards: Build and launch interactive dashboards, allowing you to query your data using plain English right away.

While the exact setup time depends on the complexity of your data environment, the process is designed to be efficient and intuitive, helping you unlock insights without delay.

Can Querio's NLQ handle complex business queries, and how does it improve efficiency compared to traditional SQL?

Querio's NLQ makes tackling complex business queries a breeze by letting users ask questions in plain English. Instead of wrestling with intricate SQL code, users can quickly extract insights from their data using natural language.

This not only saves time but also boosts productivity by providing faster access to actionable insights. And since it integrates smoothly with data warehouses, Querio's NLQ enables real-time decision-making - eliminating the usual technical hurdles of traditional SQL queries.

Related posts

• What Is Natural Language Querying in BI?

• BigQuery Made Conversational: Querio’s NLQ Crushes SQL Backlogs

• Goodbye Manual SQL: Querio’s NLQ Powers Real Business Decisions

• Natural-Language Dashboards? Querio Generates Charts From Your Words