Web scraping is an essential tool for building business connections, and data itself can be instrumental in achieving growth. But what happens after web scraping your raw data from other websites or applications? You are mostly left with raw web-scraped data. If not adequately cleaned and managed, raw data can result in inconsistencies, duplicates, and missing information.

Data matching is the process of comparing two distinct sets of data to find the relationship that unifies them. This can be done manually, semi-automatic, and automatically. The essence of data matching is to transform raw data into actionable insights.

This article will focus on the fundamentals of data matching for web scraping for businesses and individuals.

How Does Matching Web-Scraped Data Work?

In a world filled with undefined data waiting to be extracted, it is imperative to add value to your extracted information. Data matching enables businesses and individuals to spot patterns, improve data quality, and make wise decisions.

There are different types of data matching:

Exact data matching

This simple technique compares data fields that need to match in order for a match to happen. This includes, for example, matching email addresses or other unique identifiers.

Exact matching works best with organized data and well-defined properties. But may not perform as effectively when variations, typos, or incomplete matches are present,

Fuzzy data matching

When dealing with flawed real-world data, fuzzy matching algorithms give flexibility by handling typographical mistakes and incomplete matches. Fuzzy matching offers a similarity score comparable to a percentage instead of a binary match, which allows for more sophisticated decision-making and a greater degree of tolerance for poor real-world data.

These techniques assess the similarity of strings using algorithms to match even with small disparities. Fuzzy-matching might be helpful in finding possible matches in names, locations, or product descriptions that are subject to change.

Probabilistic data matching

This method depends on machine learning to determine data-matching records. It is particularly beneficial for matching large and complex web-scraped data. Most tools rely on multiple attributes and probabilities for potential matches.

Importance of Data Quality for Effective Matching

Businesses may improve decision-making, save costs, and increase customer happiness by putting a high priority on data quality. Ensuring the correctness and completeness of data requires using tools that will manage raw web-scraped data effectively. Here are some factors to consider when ensuring data quality for effective matching:

  • Data accuracy: This essential component of data quality verifies that the data is clean and consistent. Assessing accuracy entails calculating the degree of agreement between the data values and a reliable information source.
  • Efficiency: This describes how well the data contains all pertinent records and values without any gaps or omissions. It ensures that the dataset is comprehensive and has all the data required to serve its intended function, reducing computational time and resources.
  • Reliability: Gathering data accurately and in accordance with the organization’s set standards and requirements is crucial. In addition, the data must guarantee that all data values are inside the proper range and follow established forms.
  • Decision-making: Insights derived from data quality may be used for accurate decision-making, making the data valid even for future purposes.
  • Uniqueness: This is the lack of duplicate records in a dataset, even if those records are present in more than one place. Every entry in the dataset is uniquely identifiable and accessible from inside the dataset as well as from other applications.

How to Prepare Web-Scraped Data for Matching

Before matching your web-scraped data, here are some valid steps to ensure your data is optimized:

  • Data cleaning and standardization: First, you need to assess your data to identify and correct misaligned information, and other errors. You can also search for potential typos and inconsistencies. This helps build more robust and consistent data with no errors or duplicates.
  • Create unique identifiers: Then, you create and assign unique identifiers to make each dataset different from the other. You can achieve this by generating unique fields, using existing identifiers, or combining multiple fields to create a composite unique identifier.
  • Data formatting and harvesting: To ensure accurate matching, it’s essential to have consistency across datasets. This means standardizing data formats and structures. To achieve this, the data needs to be transformed into a consistent schema and naming convention. Additionally, any discrepancies in data types and units of measurement must be addressed.

Tools and Techniques for Matching Web-scraped Data

Beyond basic matching algorithms, various tools and technologies are required for data matching. Some data solutions help you prepare and clean your data.

When working with web-scraped data, it’s crucial to have the ability to handle unstructured data. Technologies like Crawlbase make it easier to extract structured data from web pages. In addition, Natural Language Processing (NLP) libraries such as spaCy or NLTK can be used to extract entities and relationships from text data. You can also look into open-source tools like Python’s Dedupe for fuzzy data matching, deduplication, and entity resolution.

You can combine most open-source tools with Crawlbase to get a maximized scraping experience.

Factors to Consider When Selecting a Data Matching Tool

Picking the right data tool to match your scraped data can be daunting as the market is saturated with a lot of data software, making it challenging to find the one that best suits your needs. However, here are some factors to consider:

  • Data volume and complexity: The data size and structure might play a significant role in the data matching tool you pick. When dealing with large datasets, you can leverage paid tools or combine them with open-source libraries to manage your data-matching needs efficiently.
  • Matching accuracy: With many data tools in the market, each application has its pros and cons. You can look into your level of desired accuracy to pick your best option.
  • Budget: This is a deciding factor in most cases. You need to consider the available budget for purchasing a new data tool and how its usage within your organization.
  • In-house expertise: There are teams with capable data professionals and engineers who might need little help from third-party tools to manage their data efficiently.
  • Data sensitivity: If you are in fear of a breach of sensitive information, you can rely on reliable data scraping tools like Crawlbase to reduce your level of exposure or rather stick with your in-house data team when necessary.
  • Scalability: The need for data-driven decisions is paramount in the current business landscape; that’s why you need to consider tools that can ensure potential future growth.
  • Integration requirements: Some tools may need to be compatible with your systems and workflows. You need to work towards tools that are flexible and can be integrated with your current framework with ease.

Best Practices for Data Matching

Data is a dynamic field constantly influenced by different scenarios. You need to consider the following to get the best out of data matching:

  • Data profiling and analysis: You need to determine the origin and format of data sets to ensure that your data is complete, accurate, and consistent. You should also evaluate the different data types and distributions to make profiling easier.
  • Develop a matching system: Consider outlining your data-matching workflow. This begins with creating rules for matching data records. You can choose conditions based on their characteristics to determine your data accuracy.
  • Refine your data: Test and learn different data experiments to assess its quality and accuracy. You can modify the matching systems based on the results and continue to iterate to optimize for better results.
  • Data validation: Consider verifying matched data manually or automatically. You use machine learning models to evaluate data quality. Also, you can establish quality control systems for continual monitoring and evaluation.

Challenges of Matching Web-scraped Data

  • Rise of data privacy concerns: Now more than ever, people are concerned about how their data is being processed, handled, and managed. This can become challenging when handling data of any kind. Third-party APIs like Crawling API can reduce this risk with their data compliant measures.
  • Managing substantial data amounts: Data matching is computationally challenging when dealing with large datasets, particularly those resulting from web scraping. To handle this difficulty, scalable infrastructure and practical algorithms are necessary.
  • Dealing with data from multiple sources: Imagine matching scraped data from multiple websites. This process may become cumbersome and result in insufficient data.

Final Thoughts

Data matching is an essential factor in deciding if your data is good or bad. Building a formidable data management system can be pivotal in ensuring efficient and accurate insights. This would empower your team to become more confident and effective when handling data.

Alternatively, you can take advantage of Crawlbase’s Crawling API to crawl and scrape unstructured data from multiple sources and turn them into ready-to-use insights for your organization. Want to learn more? Start your free trial today.