Businesses today have access to an enormous amount of data, with over 2.5 quintillion bytes of data getting generated every day. There is no shortage of web data and almost all businesses leverage it for valuable insights and improving performance. The data that is often painstakingly extracted, probably paying a considerable amount, begs several questions.
Is the data clean?
Are there any consistency issues?
Is the data reliable?
This is where web data integration (WDI) comes to play.
Web data integration is the process of extracting data from different web sources, which then goes through post-processing to become ready-to-use data. This data, which is ready for consumption, can be directly integrated into analytics, applications, AI platforms, business processes, and data warehouses. Business intelligence emerges as graphs, charts, alerts, and other forms of reporting.
In short, web data integration allows businesses to use the enormous volume of data available and capitalize on it by making effective business decisions.
Also Read: Is Web Data Scraping Legal?
Limitations Of Traditional Web Scraping
Web scraping is excellent for capturing the visible data from the web. But we require web data integration to capture the derived data, which is beyond the scope of legacy web scraping tools. For instance, while extracting data from an eCommerce website, you might have five fields – Product Name, URL, Product Description, Price, and Discount. From this data using WDI, you can derive the Sale Price. In effect, WDI can augment the data and consequently enrich it.
Legacy web scraping tools are not concerned about data integrity. Traditional web scraping outputs poor quality, unreliable, dirty data. Unclean data is inconsequential, as it is ineffectual in data analysis. While extracting real-world values could be missing in certain fields due to various reasons like incomplete extraction, corrupt data, or failure to load information. In such cases, WDI is capable of handling missing fields in machine learning datasets.
Also, traditional web scraping requires a lot of specialized skills. Most of the quality and consistency issues are dealt with manually by internal teams who have to be instructed on best practices and guidelines. On the contrary, web data integration requires little or no internal resources.
Web data integration is automatically maintained and guarantees rapid data delivery, whereas traditional web scraping is quite time-consuming and challenging to maintain.
Web Data Integration
Improving data for better decision-making is an integral part of web data integration. This includes renaming attributes, cleaning and validating data, removing duplicates, truncating data, removing special characters, and merging data from multiple sources into one new schema. Web data integration is far more comprehensive than web scraping. In fact, web scraping is a component of web data integration.
Here is a summary of how web data integration works:
Extract and consolidate both visible and hidden data (non-human readable output) from disparate web sources
Make the data richer and more meaningful by performing calculations and combinations
Cleanse the data
Normalize the data
Transform the data
Integrate the data not just via files but APIs and streaming capabilities
Extract data on demand
Analyze data with the change, comparison, and custom reports
Data Consistency
High-quality, consistent data is the foundation of transformational business strategies. Insufficient data can lead to misinformed decisions, which will affect your business adversely. When data is aggregated from multiple sources, there is a high chance of discrepancies, which leads to the creation of inaccurate and unreliable datasets.
Data Quality
Maintaining data integrity is one of the biggest challenges in web scraping. Since you extract from heterogeneous data sources, it is imperative to make sure that you are working with the best possible data quality.
Data quality has many definitions depending on the context. Generally, good data quality exists when data is suitable for the business use case at hand. Industry norms and quality standards of the business for which the data is being extracted should be considered while determining data quality.
Bad data may not be as harmless as they sound. Inconsistency and unreliability of data can lead to costly mistakes. For instance, IBM estimates that bad data costs the U.S. economy $3.1 trillion per year.
Data Quality Parameters
The quality of the data can be determined by inspecting some quality characteristics. For the data to be deemed high quality, it must meet all these criteria.
Accuracy determines whether the information is correct in every detail.
Completeness defines how comprehensive the data is. It is essential to capture the whole data, which is essential (such as customer names, phone numbers, email addresses, etc.).
Reliability is making sure that the information at hand does not contradict other trusted sources.
The relevance of data signifies whether the information collected is useful to the business objectives.
Timeliness refers to whether the data is current. It determines the freshness of time-sensitive data. Sometimes, the data is useful even after years, like LinkedIn datasets. Other times, it can become obsolete within hours.
Industries That Benefit From Web Data Integration
All industries that benefit from optimizing customer experience can make use of web data integration. Aggregating data from web sources helps Small and Medium Enterprises keep tabs on their customers’ feelings regarding their products. They can also track the effectiveness of advertising and brand messaging on their consumer base. This will help them in fine-tuning their offerings.
Closely watching competitor pricing will enable businesses to convert more leads and keep a larger percentage of their customer base. Moreover, businesses interested in expanding into new markets should invest in web data integration. WDI will help to identify gaps, make better business decisions, and to determine consumer demand.
Conclusion
Businesses these days are all about new patterns, insights, and values derived from data. And it is certainly not without reason. For today’s marketers, agencies, publishers, media companies alike, data is one of the most valuable resources available to them.
It is a common myth that web scraping is all about extracting data from the internet. In reality, that only covers 20% of the work. The real challenge is in cleaning and normalizing the data and making sure it is consistent and of high quality.
Providing structured, high-quality data analysis-ready as output gives data services companies an advantage over amateur web scrapers. Prior experience helps them in deciding which data to reject, what data quality parameters to consider, which filters to use, and much more.
Wish to avail web data integration services for making better business decisions? Contact Datahut to know more.