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了解你的数据
了解数据的来源、其类型以及我们周围的数据存储方式。
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Three common data types
| Types | Purpose | Example |
|---|---|---|
| Databases | Stores structured data for efficient querying and management. | Customer relationship management (CRM) systems like Salesforce, where we might export reports as CSVs to import into Google Sheets for analysis. |
| APIs | Provides programmatic access to data from external services or applications. | Fetching live stock prices or weather data from a public API, which can then be imported into a Google Sheet using custom scripts (Google Apps Script) or specific add-ons. |
| Flat files | Stores data in a simple, portable format for easy sharing and storage. | CSV files containing monthly sales reports or Excel files shared by colleagues, which we can directly import into Google Sheets. |
| Streaming data | Captures real-time data for immediate processing and analysis. | Live sensor data from IoT devices monitoring temperature in a factory, often fed into a database or dashboard, from which we might then export aggregated summaries for Sheets. |
| Cloud services | Offers scalable storage and computing resources for various data types. | Data stored in Google Drive (like other Google Sheets files), or in cloud storage like Amazon S3 or Google Cloud Storage, which we can connect to Sheets using connectors or exports. |
| Manual input | Collects data entered directly by users or staff. | Survey responses entered into a Google Form, which automatically populates a Google Sheet in real-time. Or, data typed directly into cells by a team member. |
| Other sources | Includes diverse origins like public websites or third-party data providers. | Extracting product prices or tables of information directly from public websites using Google Sheets’ IMPORTHTML or IMPORTXML functions. |
1.
What risks do we run into if we misunderstand the type or source of our data?
0/500
2.
Can two datasets from different sources tell conflicting stories? How would you resolve that?
0/500
值得思考的问题