Characteristics of Structured Data and Data Collection Methods
"Structured data" is the most "clean" form of online data. It has no redundant file copies or data points, nor does it contain corrupted data. Structured datasets have been converted or collected into uniform formats (such as JSON, CSV, HTML, or Microsoft Excel). This information can be easily stored in data warehouses and analyzed through systems and algorithms to deliver practical value.
Key Advantages of Structured Data
Many companies prefer using structured data because it requires lower technical costs. Structured data contains no duplicate or incomplete data, corrupted files, or datasets with incorrect formats or mislabeled tags. Using structured data allows enterprises to focus on core business development rather than data collection itself.
Additionally, since structured data does not require further processing, the time from collection to application is shorter. This means companies leveraging structured data gain not only an informational advantage but also a temporal one.
Key Disadvantages of Structured Data
Like many things in life, the greatest advantage of structured data is also its greatest disadvantage. For example, a company collects stock trend data for its analysts and stores it in Microsoft Excel format. However, when inputting this data into its stock performance prediction algorithm, which requires JSON format, the company has to convert the data format, which can sometimes significantly impact business progress. Moreover, storing structured data can sometimes be complex, especially when dealing with databases.
Key Differences Between Structured and Unstructured Data
The key differences between these two data types lie primarily in how the data is packaged and who can use it:
1.Structured datasets have one format, while unstructured data has multiple formats.
2.Structured data is typically stored in data warehouses, whereas unstructured data is usually stored in data lakes.
3.Structured data can be used even by those without a technical background, while unstructured data requires processing by data experts to achieve broader usability.
Common Types of Both Data Types
A good example of unstructured data is open-source web data collected from social media sites, review/star ratings on e-commerce websites, and online forum discussions.
It usually appears in HTML or plain text form, which is difficult for machines to process. This is because algorithms or data models need to classify information before analysis. To achieve this, they require fields, tags, or attributes, which plain text files rarely possess.
Therefore, data scientists need to use technologies such as natural language processing (NLP) or manually tag metadata for further processing.
Structured data is more "straightforward" and can take various forms, such as geographic location data, corporate event dates, company names, and stock information (trading volume, security price changes, etc.). This type of data is easy to classify through machine learning, especially when there are logical numerical patterns to follow.
How to Collect Structured/Unstructured Data
Enterprises can obtain the required data points through multiple channels, whether for structured or unstructured information. Those with professional data teams may prefer to use Selenium and Puppeteer. Other companies may choose to purchase scraping proxy services or simply buy off-the-shelf proxies.
Professionals choosing the Selenium/Puppeteer path need to define target data and URLs, write custom code to perform data extraction, and format the data before it can be correctly analyzed.
Companies that wish to transfer the responsibility of data collection and structuring to third parties can consider the following two methods:
Method 1: Automated Data Collection
Companies are using web scraping APIs to automatically clean, match, synthesize, process, and structure target data.
For automated tools like web scraping APIs, the workflow is as follows:
1.Select the target website.
2.Determine the preferred collection frequency and data format.
3.Transfer the data to the selected destination (e.g., Webhook, email, Amazon S3, Google Cloud, Microsoft Azure, SFTP, or API).
Method 2: Off-the-Shelf Datasets
Datasets are becoming increasingly popular because enterprises no longer want to struggle with data collection. They prefer to be "customers" and opt for datasets that can be ordered according to their specific needs within minutes.