- Timbr NLQ allows properly authorized business users to retrieve data with natural language, transforming complex SQL queries into intuitive, conversation-driven inputs directly in Excel for streamlined analysis.
- Timbr NLQ Excel add-in enhances productivity for business analysts and data driven teams, enabling efficient Excel-based insights across multiple tables and interconnected datasets.
- The add-in connects Excel to multiple data sources through Timbr’s semantic layer, and leverages semantic relationships to simplify data access without requiring SQL expertise.
Introduction
Excel remains a central tool for data analysis in business, yet accessing and querying data across multiple tables and sources can be challenging, especially for non-technical users. The Timbr NLQ Excel Add-in changes that by allowing users to retrieve data directly into Excel through natural language queries (NLQ), providing an intuitive way to access information without needing SQL expertise. Combined with Timbr’s granular access controls, Timbr NLQ users can securely access only the data they’re authorized to view, ensuring compliance and data security. This article explores how the Timbr NLQ add-in works, its benefits for business users and analysts, and how it differs from traditional database connections to Excel
What is the Timbr NLQ Excel Add-in?
The Timbr NLQ Excel Add-in is a feature of the Timbr Intelligent Semantic Layer, the ontology-based platform designed to connect, virtualize, and model data across sources. For business users and analysts, Timbr NLQ offers a straightforward way to retrieve complex data sets in Excel using conversational language, without needing to write SQL. By typing questions in plain language, users can access data through a guided query interface that translates natural language into SQL queries, retrieving data from one or multiple databases.
How Does Timbr NLQ Work?
Timbr NLQ Excel Add-in works by connecting Excel to the Timbr Intelligent Semantic Layer, allowing business users to query data from multiple sources mapped within a semantic model. The following explanation summarizes the Timbr NLQ video explainer.
1. Setting Up the Semantic Model
Users start by creating a semantic model on top of their data within the Timbr Intelligent Semantic Layer. This model connects databases such as MySQL, PostgreSQL, or Databricks and maps data by means of business terms and semantic relationships. These relationships replace traditional SQL JOINs, enabling more intuitive querying.
2. Connecting Excel to Timbr NLQ
After the semantic model is set up, users open Excel, select the Timbr NLQ add-in, and connect by entering the environment URL and user token. They then choose the knowledge graph they want to query, which is mapped to the connected data sources.
3. Creating a Query with Natural Language
Through the Timbr NLQ interface, users type a question or request in plain language. The NLQ interface provides auto-complete suggestions, helping users refine their input and build accurate queries. For instance, a user might type, “Find financial organizations with the country code USA that made an investment in a funding round of a biotech company that had an IPO”, and the add-in will guide them to create a SQL query that retrieves this data.
4. Running the Query and Retrieving Data
When the query is complete, users click ‘Run,’ and Timbr translates the natural language input into an SQL query. This query retrieves the requested data from the selected sources and displays it in Excel, ready for analysis.
Benefits of Timbr NLQ for Business Users and Analysts
The Timbr NLQ Excel Add-in provides a number of advantages for users who need to access data for analysis:
1. Efficient Data Retrieval
The add-in enables users to pull data into Excel using natural language, which can reduce the time needed for data retrieval. With auto-complete suggestions and a guided query interface, business users can access data without relying on technical support.
2. Access to Complex Data Sets without Manual SQL
Timbr NLQ allows users to retrieve data from multiple sources without having to manually handle complex SQL JOINs. By using predefined relationships within the semantic model, users can access interconnected data across tables or sources more efficiently.
3. Enhanced Independence for Data-Driven Teams
Business analysts and decision-makers can directly retrieve the data they need, reducing dependency on IT support for query generation. This approach can facilitate quicker insights and support timely decision-making.
4. Real-Time Data Access from Multiple Sources
Timbr’s semantic layer provides users with real-time access to data across connected databases, allowing for more current insights. Users can connect to databases such as MySQL, PostgreSQL, and Databricks, accessing the data they need without managing multiple interfaces.
How Timbr NLQ Differs from Traditional Database Connections in Excel
Traditional methods of connecting Excel to databases, such as ODBC or JDBC connections, usually require SQL expertise and knowledge of database schema. The Timbr NLQ Excel Add-in differs in several key ways:
1. Natural Language Interface vs. SQL-Based Querying
Traditional methods often require SQL knowledge, which can be challenging for non-technical users. Timbr NLQ eliminates this barrier by enabling natural language queries, which are accessible for business users and analysts without SQL expertise.
2. Semantic Modeling with Explicit Relationships
When using conventional database connections, retrieving data from multiple tables typically requires manually setting up JOINs. With Timbr’s semantic modeling, relationships are predefined, making it easier to pull data from various sources without needing to manually configure JOINs.
3. Unified Access Across Data Sources
Traditional methods may require using one of a number of methods available to connect with multiple data sources, which can be time-consuming and complex to manage. Timbr’s semantic layer provides a unified interface, allowing users to retrieve data from multiple sources through a single connection that includes virtualized data.
4. Query Datalakes
Data lakes can store enormous amounts of data, but finding the right data can be like searching for a needle in a haystack. Timbr NLQ users conveniently retrieve data from data lakes modeled in the Timbr semantic layer, which allows seamlessly mapping of intricate relationships within data lakes.
5. Generation of Error-free SQL Queries
Timbr NLQ implements auto-complete suggestions that guide users in creating their queries, which reduces the likelihood of errors in query syntax and structure. This can improve the experience for users who are less familiar with SQL.
Real-World Applications
Timbr NLQ Excel add-in is suited for various applications in business environments, such as:
- Sales Analysis: Business analysts can retrieve sales data segmented by region, product, or time period, allowing for performance analysis without technical bottlenecks.
- Financial Reporting: Finance teams can access data from multiple sources to create reports and summaries directly in Excel.
- Customer Insights: Marketing teams can pull customer data from CRM and transactional systems to gain insights into customer behavior and trends.
Getting Started
To use the Timbr NLQ Excel Add-in, users need a license for the Timbr Intelligent Semantic Layer, available as a SaaS or on-premises solution. Starting Dec 1, 2024, licenses can be purchased from the Azure Marketplace or through Timbr’s website. Once set up, users can connect their semantic model to various data sources and begin retrieving data within Excel.
Conclusion
The Timbr NLQ Excel Add-in offers a streamlined approach to data retrieval for business users and analysts working with Excel. By simplifying SQL queries through natural language and leveraging Timbr’s semantic modeling, users can access data across multiple sources without the need for SQL knowledge. This can enhance productivity for data-driven teams and improve the efficiency of Excel-based analysis.
For those looking to simplify their data access process and enable self-service analytics in Excel, the Timbr NLQ Excel Add-in provides a practical solution that bridges the gap between data complexity and accessibility.
Start leveraging the power of SQL ontologies today with Timbr and see how it transforms your approach to data modeling and querying.
