Data Collection Strategies for Better Insights#DataCollection
- 1. Presented by Ms Subhasheni A Assistant Professor Department of Computer Science Sri Ramakrishna College of Arts & Science Coimbatore Data Collection Strategies in Data Analytics
- 2. • Data collection is the process of gathering information for analytical purposes. • It is the foundation for effective data analytics. • Ensures that the data used for analysis is relevant, complete, and accurate. Introduction to Data Collection
- 3. • Enables accurate decision-making. • Provides reliable inputs for analysis. • Improves operational and strategic efficiency. • Supports predictive and prescriptive analytics. Importance of Data Collection
- 4. 1. Structured Data – Organized and easily searchable (e.g., databases). 2. Unstructured Data – Images, videos, social media posts. 3. Semi-Structured Data – Email, XML files. Types of Data
- 5. • Surveys and Questionnaires – Direct responses from target groups. • Interviews – One-on-one data gathering. • Focus Groups – Group discussions on a specific topic. • Observation – Watching and recording behavior. • Experiments – Controlled testing and measurement. Primary Data Collection Methods
- 6. • Existing Databases – Government records, academic data. • Published Reports – Market research, industry papers. • Web Data – Social media, websites. • Sensor Data – IoT devices, machines. Secondary Data Collection Methods
- 7. • Web Scraping • Online Forms & Feedback Tools • Cookies & User Tracking • APIs (Application Programming Interfaces) • Social Media Analytics Tools Online Data Collection Techniques
- 8. • Google Analytics • Apache NiFi • Talend • Microsoft Power BI • Web Crawlers (e.g., Scrapy) Automated Data Collection Tools
- 9. • Define clear objectives. • Use ethical and legal methods. • Ensure data accuracy and consistency. • Regularly validate and update sources. • Secure data storage and handling. Best Practices in Data Collection
- 10. • Data privacy and consent issues • Incomplete or missing data • Technical barriers • Biased data sources • High volume of unstructured data Challenges in Data Collection
- 11. • Effective data collection is critical for successful analytics. • Combines multiple methods and technologies. • Ethical and well-planned collection ensures quality insights. Conclusion
- 12. Thank you!