1. Data collection
Sample design: Support complex sample design to ensure that the sample is representative of the overall population.
Data entry: Data is collected through a variety of means such as spreadsheets, online forms, or mobile applications.
2. Data management
Data cleansing: Checks the quality of data and corrects or removes errors or inconsistencies.
Data storage: Store large amounts of data securely and provide backup and recovery options.
3. Data analysis
Statistical analysis: Provides basic descriptive statistics to advanced multivariate analysis tools.
Trend analysis: Tracking consumption patterns over time.
Predictive modeling: Using historical data to predict future consumption trends.
4. Report generation
Visual reports: Generate charts, graphs, and other visual tools to help non-technical users make sense of data.
Custom reports: Allows users to create personalized reports based on specific needs.
5. User interface
Ease of use: Provides an intuitive user interface for users with no statistical background.
Multilingual support: Multiple languages are supported for easy international use.
6. Security and compliance
Data protection: Ensure the security of sensitive information and comply with data protection regulations such as GDPR.
Access control: Use role rights management to control the access levels of different users.
7. Integration capability
External system integration: The ability to integrate with other databases or information systems, such as health information systems.