Biggest Obstacles of Data Parsing
Data parsing is a critical step in web scraping and web crawling. It enables you to take the data you have obtained from other sources and use it for your specific purposes. Yet, there are numerous data parsing obstacles you could encounter, and many of them can prove to be frustrating, time-consuming, and limiting.
If you encounter these obstacles in data parsing, know that they could also skew the accuracy of the information obtained.
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For all of these reasons, it is critical to know how to overcome such challenges. Understanding the challenges of data parsing post data collection helps you to know what to do when you run into an obstacle, such as all of the imperfections that are common in real-world data. Remember that to use this data for any reason, you absolutely must be sure it is accurate and thorough. To help you explore how to improve data parsing efforts, let’s first focus on the challenges many people face.
Why Data Parsing Obstacles Exist
If you haven’t done so yet, take a closer look at our “What is Parsing” tutorial to get an idea of what we are referring to when we talk about data parsing. In short, the obstacles most face are those that come after you have scraped the data. You have authentic but raw information to use, and you need to clean it up and optimize it so you can learn from it. The problem is, it’s real-world data.
That means it is very common for that data to have a number of problems that could influence your decision-making. That includes inconsistencies that may negate each other. There may be complexities to the coding, or there could be errors that create mistakes in your understanding. Knowing that imperfections are a common part of data parsing means you can take action to fix the problem when it occurs.
Here’s another concern. Sometimes, that data parsing obstacle comes from pre-collection. That means it’s already in the content before you even capture it. For example, if the data collection process you are using is automated, and then a website updates its defense strategies, your data is no longer accessible. That can lead to incomplete data within the larger data set, and that means skewed details. It also means that your data is likely incomplete or unclear. As your business utilizes data parsing, then this is one of the most important steps to remember: data may not be accurate or fully present. (This is a good time to mention one of our other guides on TLS Fingerprinting – It can give you tips on how to manage those potential obstacles by simply knowing when the problems exist.
The Most Common Obstacle of Data Parsing
Once you learn how to parse data, you will be ready to dive in and start using those methods to make better decisions for your business or to enhance your automated research process. However, it is likely that you will encounter at least some of the following data parsing obstacles. Let’s go through the most common and find out what can be done.
Errors and Inconsistencies: One of the most common problems associated with data parsing is the errors or inconsistencies that are, unfortunately, very much present in real-world data. Most of the time, the data you are putting into data parsing is raw. It is unstructured or, in some situations, semi-structured data. The result is that this data may contain errors and inaccuracies.
This is very common with HTML parsing. That is because most of today’s modern browsers are smart enough to overlook and fix mistakes when they are present. In fact, they can render HTML pages even when syntax errors are present. That’s a good thing for the web developer and site owner. However, for data parsing, it means that no one has adjusted or fixed those errors. Some of the most common concerns we see include:
- Unclosed tags
- Invalid HTML content according to W3C standards
- Special HTML characters
There are dozens of potential mistakes that can happen, especially with HTML pages. The best way to overcome these challenges is to utilize more advanced and intelligent parsing systems that will automatically detect these concerns and, like with browsers, address them properly.
Huge Amounts of Data: Here’s another data parsing obstacle many businesses face – the sheer size of the data they are trying to navigate. Big Data is an outstanding resource for your company, but just having tons of data isn’t beneficial. If you have Big Data challenges, you may run into numerous obstacles as you try to parse data. In some situations, this will lead to performance issues and slowdowns for your parsing tools.
This can lead to the need to parallelize your data processing. In short, that means you will need to parse these huge amounts of data in several input documents at the same time. This will help you save time, but it allows you to overcome the overload of too much data for your parser.
Parsing large amounts of data is never going to be a simple task. After all, much of that information is valuable and worthy of careful consideration. That doesn’t mean, though, that you can sit back and hope for the best. Advanced parsing tools can help propel you forward with this type of data.
Different Data Formats
Here’s another of the major data parsing obstacles most people will face from time to time. Formatting. Inconsistent formatting is a common concern because there are so many ways to format data. For example, varying date formats or mixed data types can create confusion and limit the overall accuracy of your parsing process.
Because data continues to evolve and grow, you are likely to often find data formatting issues as a component of the parsing obstacles you face. The best way to overcome this problem is to use a more robust parser and to ensure that you are keeping that parser up to date using the most modern methods and strategies.
Choose a parser that has the ability to import and export data in various character encodings. By making sure of this, you can be sure that the parsed data that you have worked so hard to get can be used where it needs to be, whether that is macOS or Windows, for example.
Poorly Constructed or Corrupted Files: Parsing poorly structured and corrupted files is another concern for most parsing tools. Even when you believe the data is accurate and in the format desired, mistakes can occur. This means that the file’s format is incomplete or inaccessible.
Data corruption can prevent you from parsing and interpreting data accurately. It often occurs when there are data transmission errors or the data manipulation methods used are inaccurate. Sometimes, storage problems can contribute. Missing or invalid values are another very common structural inconsistency that can make this challenging. You could find yourself with unexpected coding issues.
All of this can be very frustrating. You can and should expect some of these data parsing obstacles to interfere with the way you operate your business parsing project. Identifying the problem is the first step. Then, work to use an error-tolerant parser, one that can “fill in the gaps” when there is some type of inaccuracy and informs you of the complexities you are facing.
Consider, too, what you expect to happen when data is missing or incomplete, such as due to corrupted files. Many times, you will expect the system to use default values or null values. Note that this is something to consider as you start the process so that you can expect the resulting data.
Excel Sheet Complexities: Here’s a common obstacle to data parsing when you are using Excel sheets. First, humans likely created that sheet, which means formatting and code errors could exist. Second, you may find irregular hierarchies that get in the way of clarity. That could include irregular layouts with merged cells. There may be annotations to the content that you did not expect to find. In some situations, hierarchical column mismanagement can skew information.
It is important to have a parser that can anticipate and overcome these challenges. There is no real way to ensure that every bit of data on Excel sheets will be accurate and without flaw. Incomplete data entries, for example, can be hard to detect from just a visual view of that data.
How to Overcome Data Parsing Obstacles
Any of these data parsing obstacles can lead to complications. These obstacles can lead to parsing errors, loss of information, or inaccurate analysis if not properly handled. The first step is to recognize that errors and obstacles can occur. That way, you can plan for them and plan how you want to handle them. That will often be project-specific. Now, consider a few ways to overcome the obstacles of data parsing that are so commonly present.
Data Quality and Consistency: In short, start with good data. That seems like the obvious statement, but with pre-validation strategies in place, you can identify and fix errors early on before you try to parse that information. There is a variety of solutions available to help with this.
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For example, data cleaning tools have a built-in validation process. If they find those coding errors or incomplete tags, they can clean and fix the problem for you. This is often beneficial for missing values or duplicates, but can help with a range of inconsistencies overall.
Also, consider a data audit. If you consistently use data from a source, be sure that the data is up to date and that you are checking it for errors and inconsistencies. Do not assume everything is in place.
Tools and Techniques: There are a number of ways to overcome data parsing obstacles using various tools and techniques. For example, AI-driven parsers that automate much of the process can reduce some of the risk you are seeing. For example, learn how to apply machine learning to web scraping to help enhance the quality of the content you are receiving. We often recommend steps to incorporate AI in web scraping because it can resolve many of these concerns with speed. This can be particularly beneficial when you are engaging in non-standard formats.
Some advanced OCR tools are available that can accurately extract text from difficult situations, which can also be helpful. This includes tools that can ensure text extraction from images or scanned documents is more accurate.
Custom-Built Solutions: Another solution to the obstacles of data parsing comes in the form of custom-built solutions. Instead of using widely available and basic parsing tools, learn to build your own parser that can tackle any of the concerns you may have. Custom-built solutions can be tailored to the specific quirks of the data source, which allows for more accuracy when it comes to producing the final product.
Custom parsers can incorporate automation and parallelism and be designed to handle the type and amount of data you need to handle. You can also amplify results by using specific parsing libraries, incorporating robust error handling, and ensuring data privacy and security measures are up to date to meet current standards.
Let Rayobyte Help You With the Process
Data parsing obstacles are not something to get stuck on, but rather a common component of the data parsing world. Remember that you can create better results by using Rayobyte’s web scraping API to help you capture data with high levels of precision. You can also expect our proxy services to offer another layer of protection. Learn more about how Rayobyte works to help you get the most out of your data. That includes data parsing obstacles that aim to hold you back and limit your overall success.
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