What is meant by Data Quality? Why is it important?
Every company relies on data to make informed decisions and keep up with the dynamic market pace. However, many businesses find that they are not using accurate information for decision-making and, hence, face the brunt of it in the market arena.
Therefore, improving and maintaining data quality is of utmost importance. But what exactly is data quality? Why is it important? And how to ensure data quality while web scraping unstructured data?
Let us delve deeper to have a better understanding of the same:-
Data quality refers to evaluating how relevant data is for meeting the goals of the concerned organization. As such, high-end data help make secure and best decisions that cater to the company’s goals.
For any organization, maintaining the data quality is necessary as it gives consumers the best experience based on accurate data. Collecting information and updating existing records provides a better understanding of the target customers.
It also helps keep them in contact using mailing information and phone numbers. This information enables enterprises to use resources efficiently. Maintaining data quality also aids in staying ahead of competitors.
What Type of Data Gets the Status of Quality Data?
No yardstick can point out and state one data as quality information and others as poor. Instead, measuring the quality of any data depends on locating and weighing its characteristics for applications that use the scraped information.
However, mentioned below are some of the major factors that give the status of quality data to some of the scraped information:
Accurate and Precise
This factor showcases the accuracy of the data that portrays the real-time condition without showing any misleading information. A firm can not get the required results when planning the next step of action based on inaccurate data. Furthermore, it will cause additional costs when the enterprise rectifies its decisions due to incorrect data extraction.
Complete and Comprehensive
The fundamental property of complete and quality data is that it does not possess incomplete or empty fields. Similar to inaccurate data, incomplete information causes firms to make decisions that affect their business adversely.
Validity/Data Integrity
Usually, a valid data set has information that is in the correct format with values being within the range. They are thus of the accurate type. It is called the data collection process and not the data itself. The information that does not reach the validation benchmark requires extra effort to get in the sink with the rest of the available database.
Consistent and Reliable
This property denotes that information from a source must not become contradictory to the same data from another system or source. For example, if in one source, the birth date of a renowned figure is 8th September 1985, in another, one may find that his birthdate is 8th October 1986. This inaccuracy in data would eventually result in extra costs. It would damage the reputation of the organization.
Timeliness
Timeliness means how updated the data is. In due course, the information accuracy in sources becomes old and unreliable. It is because it is the reflection of the past and not the present moment. As such, it is imperative to scrape information timely to get the optimum outcome. If the firm bases its decisions on old data, it would cause organizations to miss various opportunities.
Factors that Affect Data Quality
Several factors might affect the quality of the data scraped. Mentioned below are some of the most common ones:
Changes in Website Structure
Web pages constantly update their layouts and UIs to attract more visitors. Since a bot usually gets built per the structure of the webpage in current times, it needs to get updated frequently. If a website drastically has a structure change, it might get difficult for a web bot to scrape data from it further.
Requirement for Login
Some web pages need login first before extracting content from them. As such, when running through websites with a login requirement, your bot might get stuck, finding it hard to pull data from the site.
Wrong Data Extraction
When choosing elements from a complex page, it may become difficult to locate the needed information. It is because the automatically generated Xpath in bots may be inaccurate. In this scenario, inaccurate data might get extracted.
Limited Extraction of Data
Another ill-effect of locating incorrect data is that the web scraper cannot click on any intended button, like the pagination button to open a new page. In such cases, the bot might repetitively scrape the first page without moving on to the next page.
Incomplete Web Scraping
While scraping some websites like Twitter, they only load extra content when the page gets scrolled down. If it is not scrolled down and no data becomes visible, the crawler will not get the entire set of data.
As such, there exist several other factors that affect the quality of the data, and the mentioned are just some from the long list.
Ways To Ensure Data Quality while Web Extraction
There is a wide variety of metrics with which data quality measurement gets done. Let us look at some of them:
Automatic Monitoring System
Websites get updated regularly. Most of these changes may lead to extracting wrong or inadequate data. Thus, a completely automated system is required to keep track of the crawling jobs on the servers. This system keeps track of the scraped information for errors and inconsistencies.
It looks for three kinds of problems:
Mistakes related to the validation of data
Site modifications and,
Volume inconsistencies
High-end Servers
The server’s reliability determines how easily the bot works. It impacts the web scraping eCommerce information quality. As such, high-end servers running the crawlers must get used. It will prevent the bots from failing because of an instant high server load.
Cleansing of Data
The scraped data might have unwanted extra elements like HTML tags. This information gets called as being crude. In such a situation, the system that performs cleansing does a great job of removing the elements and cleaning up the extracted data.
Structuring
Structuring provides the data with a machine-readable syntax, which makes them appropriate for analytics and database systems. When the information gets structured, it becomes ready for use by database uploading or plugging it into an analytics system.
Number of Empty Values
Within a data set, empty values portray the data as missing or entered in the wrong set. These values record the data quality issues. As such, enterprises can count the number of empty fields present in a data set and then see the way these number changes over time.
Data Transformation Error Rates
Data transformation means obtaining data stored in one format and converting it into a different format. These errors are usually indicative of problems pertaining to data quality. Businesses can gain a better insight into the quality of their information by measuring the number of data transformation operations that somehow fail or take longer than expected to get complete.
Final Thoughts
With the growth of the internet and companies’ dependence on data and information, the Future of Web Scraping is full of new adventures and successes. With a data-driven approach, enterprises can improve their services and offer, giving better output and grabbing customers’ attention over time.
About the author
Mia Reynolds
Marketing Manager
Mia is a creative Marketing Manager who combines data-driven insights with innovative campaign skills. She excels in brand positioning, digital outreach, and content marketing to boost visibility and audience engagement.
