Data Collection

Analyze Anything. Unleash Powerful Analytics.

Analyze various data types and augment with AI powered insights for powerful multi-tenant reporting.

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Most data sources require independent data ingestion and ETL pipelines creating a large overhead of independent data pipelines.

Consolidate data pipleines while creating a scalable process to offer custom data collection in a multi-tenant platform.

Using APIs and embedded form builders, data is available for analysis with a native multi-tenant security layer removing a common hurdle development teams face.

Break Free Of Relational Data And Open Up the Possibilities

As the types of data found in most companies and SaaS platforms continues to grow, Qrvey helps enterprises of all sizes offer analytics tools that can handle the growth while offering a scalable and cost efficient platform. 

User Feedback System
integrate to data sources
self service analytics


Discover how Impexium was able to offer self-service surveys within their multi-tenant platform to thousands of associations.

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Analytics for Those Who Want More

Build Less Software. Deliver More Value.

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FAQs About Data Collection

Forms and surveys have a wide range of applications across various industries and situations. Here are some common use cases:


Forms and surveys can be used for:

  • Quizzes and Assessments: Create online quizzes or assessments for educational purposes or skill evaluation.
  • Permission Slips & Consent Forms: Gather consent for activities or data collection from parents or guardians.
  • Health Screenings & Check-Ins: Facilitate health screenings or collect check-in information at events.
  • Contest & Giveaway Entries: Manage entries for contests, sweepstakes, or giveaways.

Customer Satisfaction Surveys:

  • Gauge customer sentiment and identify areas for improvement.
  • Product Feedback Surveys: Gather user feedback on new features or product iterations.
  • Event Feedback Forms: Collect feedback from attendees to improve future events.
  • Support Issue Forms: Allow users to report problems and submit support requests.

Market Research & User Behavior:

  • Market Research Surveys: Gather data on target audience preferences and market trends.
  • User Onboarding Surveys: Understand user experience during the initial product setup.
  • Website Feedback Surveys: Collect feedback on website usability and navigation.
  • Product Feature Interest Surveys: Gauge user interest in potential new product features.

Internal Processes & Operations:

  • Employee Onboarding Forms: Collect new employee information and streamline the onboarding process.
  • Expense Report Forms: Simplify expense reporting for employees and managers.
  • Internal Feedback Surveys: Gather feedback from employees on company culture or policies.
  • Equipment Request Forms: Automate equipment request processes within an organization.

Real-time data collection has a vast array of applications across numerous industries. Here are some common use cases that highlight its power and versatility:

Monitoring & Optimization:

  • IT Infrastructure: Real-time monitoring of server performance, network traffic, and application health helps identify and address issues before they disrupt operations.
  • Supply Chain Management: Track inventory levels, deliveries, and potential delays in real-time to optimize logistics and ensure efficient product movement.
  • Financial Markets: Stock traders rely on real-time data feeds to analyze market trends, identify opportunities, and make informed investment decisions.
  • Website & App Performance: Monitor website traffic, user behavior, and application performance metrics in real-time to identify bottlenecks and optimize user experience.

Personalization & Customer Experience:

  • Recommendation Engines: E-commerce platforms use real-time data on customer behavior and purchase history to recommend personalized products and improve conversion rates.
  • Chatbots & Virtual Assistants: Real-time data allows chatbots to respond to customer queries with more relevant information and provide a more personalized experience.
  • Content Delivery & Advertising: Real-time data allows websites to personalize content and target advertising based on user location, interests, and browsing behavior.
  • Fraud Detection & Security: Financial institutions and online payment platforms utilize real-time data to detect fraudulent activity and prevent unauthorized transactions.

Safety & Public Services:

  • Traffic Management: Real-time traffic data helps optimize traffic lights, alert drivers of congestion, and improve overall traffic flow.
  • Emergency Response: Real-time data from sensors and connected devices can be used to identify and respond to emergencies like fires, floods, or accidents more efficiently.
  • Public Health Monitoring: Real-time data on disease outbreaks and healthcare utilization helps health authorities track trends and implement preventive measures.
  • Environmental Monitoring: Track real-time data on air quality, water pollution, and weather conditions to understand environmental changes and take appropriate actions.

Log files that contain data points with timestamps and other descriptive information but may not follow a strict tabular format such as:

  • Server logs
  • application logs
  • Network logs 

JSON: This common data format to exchange information between applications. They use tags or key-value pairs to organize data, but the structure can vary depending on the specific use case.

Email: Emails often contain text, attachments like documents or images, and headers with metadata like sender and recipient information. While some elements have a basic structure, the content itself is typically unstructured.

Sensor data: Data collected from sensors, like temperature readings or device status updates, often comes with timestamps and identifiers but might not follow a predefined format.

Text documents: Documents like emails, reports, presentations, and social media posts contain text with varying lengths and formats. While some might have basic structure (headings, paragraphs), analyzing the actual content requires natural language processing techniques.

Images and videos: These multimedia files lack inherent structure and require specialized algorithms for analysis. Techniques like image recognition and computer vision can extract information from these formats.

Audio recordings: Similar to images and videos, audio recordings require speech-to-text conversion or other processing methods to extract meaningful information.

Social media data: Social media posts, comments, and reviews often contain a mix of text, emojis, hashtags, and mentions. While some structure might exist (e.g., usernames, timestamps), extracting insights requires advanced natural language processing and social network analysis techniques.