### 1. Data Acquisition
Data acquisition involves the systematic collection of information from various sources to create a comprehensive dataset. This can be achieved through multiple methods, including:
- **Surveys**: Designing and distributing questionnaires to gather qualitative and quantitative data from targeted populations.
- **Software Tools**: Utilizing specialized software applications that can automatically collect data from online platforms, databases, or APIs.
- **Instruments**: Employing physical devices or sensors to gather real-time data, such as IoT devices for environmental monitoring or medical instruments for health data collection.
- **Web Scraping**: Extracting data from websites using automated scripts to gather information that is publicly available.
- **Third-party Data Providers**: Purchasing or accessing datasets from external vendors that offer curated data relevant to specific industries or research areas.

### 2. Data Sanitization
Data sanitization is a critical step in ensuring the integrity and reliability of the dataset. This process includes:
- **Identifying Inaccuracies**: Detecting errors in the data, such as incorrect entries, typos, or outliers that may skew analysis.
- **Removing Duplicates**: Implementing algorithms to find and eliminate duplicate records that can lead to misleading results.
- **Correcting Anomalies**: Investigating and rectifying any unusual data points that do not conform to expected patterns or distributions.
- **Standardization**: Ensuring consistency in data formats, units of measurement, and naming conventions to facilitate easier analysis.
- **Validation**: Cross-referencing data against trusted sources to confirm its accuracy and reliability.

### 3. Data Structuring
Data structuring involves organizing the cleaned data into a coherent format that supports efficient analysis. Key activities include:
- **Database Design**: Creating a logical structure for databases, including tables, relationships, and indexing to optimize data retrieval.
- **Data Modeling**: Developing conceptual, logical, and physical models to represent the data and its relationships clearly.
- **Schema Definition**: Establishing rules and constraints for data entry, including data types, field lengths, and mandatory fields.
- **Retention Policies**: Determining which data should be retained for future analysis and which can be archived or deleted based on relevance and compliance requirements.