Data connectors

Data connectors are software components that establish links between different data sources and analytics or business intelligence platforms. They act as bridges that allow organizations to access, retrieve, and integrate data from various systems—such as databases, cloud applications, data warehouses, and APIs—into a centralized environment for analysis. These connectors handle the technical complexities of authentication, data formatting, and communication protocols, making it possible for users to work with data from multiple sources without requiring deep technical expertise. By standardizing how data flows between systems, data connectors eliminate the need for manual data extraction and custom coding, streamlining the process of bringing disparate data together for meaningful analysis.

Data connectors play a critical role in modern Business Intelligence by breaking down data silos that prevent organizations from gaining complete insights. When customer data lives in a CRM, sales figures reside in an ERP system, and marketing metrics exist in separate cloud applications, making informed decisions becomes nearly impossible without a way to combine these sources. Data connectors solve this challenge by providing seamless access to all relevant information, regardless of where it's stored.

This connectivity directly impacts business agility and decision-making speed. Instead of waiting for IT teams to build custom integrations or manually export and combine data, business users can quickly connect to the sources they need and begin analyzing information immediately, reducing time-to-insight from weeks to minutes.

1. Authentication and authorization: The connector establishes a secure connection to the target data source using appropriate credentials and authentication methods.

2. Schema discovery: The connector identifies available tables, fields, and data structures within the source system to understand what data can be accessed.

3. Data retrieval: Based on user queries or predefined parameters, the connector fetches the requested data from the source system.

4. Data transformation: The connector converts the retrieved data into a format compatible with the destination analytics platform, handling differences in data types and structures.

5. Data delivery: The processed data is made available in the analytics environment where users can query, visualize, and analyze it alongside data from other sources.

1. A retail company uses data connectors to link their Shopify e-commerce platform, Google Analytics, and inventory management system to their analytics platform. This integration allows them to analyze customer behavior, sales performance, and stock levels in a single dashboard, identifying which products are trending and when to reorder inventory based on real-time data.

2. A financial services firm connects their Salesforce CRM, accounting software, and customer support ticketing system through data connectors. Their analysts can now track the complete customer journey from initial contact through sale and ongoing support, calculating customer lifetime value and identifying factors that influence retention.

3. A healthcare organization uses data connectors to bring together patient data from electronic health records, billing systems, and appointment scheduling software. This consolidated view helps administrators optimize resource allocation, reduce wait times, and improve patient care coordination across departments.

1. Reduce the time and technical resources required to integrate multiple data sources into analytics workflows.

2. Provide business users with self-service access to data without requiring SQL knowledge or IT intervention.

3. Maintain data freshness by supporting scheduled updates or real-time synchronization depending on business needs.

4. Improve data accuracy by eliminating manual data transfer processes that are prone to human error.

5. Scale easily as organizations add new data sources, with pre-built connectors available for hundreds of popular applications.

6. Support comprehensive analysis by making it possible to combine data from previously isolated systems.

ThoughtSpot offers a comprehensive library of data connectors that connect directly to popular data warehouses, cloud platforms, and business applications. These connectors are designed with the business user in mind, featuring straightforward setup processes that don't require extensive technical knowledge. By providing robust connectivity options, ThoughtSpot allows organizations to bring all their data into a unified analytics experience where users can search and explore using natural language. Spotter, your AI agent, can then work across all connected data sources to deliver insights and answer complex business questions that span multiple systems.

1. Data integration

2. ETL (Extract, Transform, Load)

3. API

4. Data warehouse

5. Business Intelligence

6. Data pipeline

7. Cloud storage

Data connectors are fundamental components that make modern analytics possible by seamlessly connecting diverse data sources to create a unified view for better business decisions.