What is Data?
Data refers to raw facts and figures that have not yet been processed or analyzed. When data is organized and given context, it becomes information.
Example: "25" is data. "The temperature is 25°C" is information because it has context and meaning.
Types of Data
Quantitative Data
Numerical data that can be measured or counted.
- • Temperature readings
- • Number of students
- • Test scores
- • Prices and costs
Qualitative Data
Descriptive data that cannot be easily measured with numbers.
- • Opinions and feelings
- • Colors and textures
- • Descriptions
- • Categories (e.g., "satisfied", "neutral")
Data Sources
Primary Data
Primary data is data you collect yourself, directly from the source, for your specific purpose.
- Surveys - Questionnaires distributed to gather responses
- Interviews - Direct conversations with individuals
- Observations - Watching and recording behaviors or events
- Experiments - Controlled tests to collect specific measurements
- Sensors - Electronic devices that capture data automatically
Secondary Data
Secondary data is data that has already been collected by someone else for a different purpose.
- Government statistics - Census data, economic reports
- Academic research - Published studies and papers
- News reports - Journalism and media coverage
- Existing databases - Company records, historical data
- Online sources - Websites, APIs, public datasets
Primary Data Advantages
- • Specific to your needs
- • Current and up-to-date
- • You control the quality
- • Know exactly how it was collected
Secondary Data Advantages
- • Faster to obtain
- • Often free or low cost
- • Larger sample sizes available
- • Historical data accessible
Data Collection Methods
Surveys and Questionnaires
Best practices for effective surveys:
- Use clear, simple language
- Avoid leading or biased questions
- Include a mix of question types (multiple choice, rating scales, open-ended)
- Keep surveys concise to improve completion rates
- Test your survey before distribution
Observations
When observing:
- Define what you're measuring beforehand
- Record data systematically
- Be consistent in your methods
- Consider whether your presence affects behavior
Automated Data Collection
Digital systems can collect data automatically:
- Web analytics - Tracking website visits and user behavior
- IoT sensors - Temperature, motion, light sensors
- Transaction logs - Recording purchases and activities
- Social media APIs - Gathering public posts and trends
Data Quality
Good data should be:
- Accurate - Free from errors
- Complete - No missing values
- Consistent - Same format throughout
- Timely - Current and relevant
- Valid - Measures what it's supposed to
Ethical Considerations
When collecting data, you must consider:
Privacy
- Only collect data you actually need
- Inform people about what data you're collecting
- Store data securely
- Don't share personal data without permission
Consent
- Get permission before collecting personal information
- Explain how the data will be used
- Allow people to opt out
- Be especially careful with data from minors
Bias
- Ensure your sample represents the population
- Avoid questions that lead to certain answers
- Consider who might be excluded from your data
- Be transparent about limitations
Key Terminology
- Data - Raw facts and figures without context
- Information - Data that has been processed and given meaning
- Primary data - Data collected firsthand for a specific purpose
- Secondary data - Data collected by others, used for a different purpose
- Quantitative - Numerical, measurable data
- Qualitative - Descriptive, non-numerical data
- Sample - A subset of a population used for data collection