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Data Quality Focused Data Pipelines

Data quality is critical in any web scraping or data integration project. Data-driven businesses rely on customer data, it helps their products, provides valuable insights, and drives new ideas. If organizations expand its data collection, it becomes more vulnerable to data quality issues. Insufficient quality data, such as inaccurate, missing, or inconsistent data, provides a lousy foundation for decision-making. The only way to maintain high-quality data is by implementing quality checks at every step of your data pipeline.

ETL (Extract Transform Load) is the process of extracting, transforming, and loading the data. It defines what, when, and how data gets from your source to your readable database. Data quality relies on implementing a system from the early stage of extraction to the final loading of your data into readable databases.

Data quality ETL procedure:

Extract: Scheduling, maintaining, and monitoring are all critical aspects to ensure your data is up to date. You know what your information is at the extracting phase, and you should implement scripts that will look at its quality. This gives the system more time to troubleshoot closer to the source, and you can intervene before the data is changed.


Transform: Transformation is when most of the quality checks are done. No matter what is used, it should at least perform the following tasks

- Data Profiling
- Data cleansing and matching
- Data enrichment
- Data normalization and validation

Load: At this point, you know your data. It’s been changed to fit your needs and, if your quality check system is efficient, the data that reaches you is reliable. This way, you avoid overloading your database or data warehouse with unreliable or lousy quality data, and you ensure that the results have been validated.

What is high-quality data?

  • Accuracy data: The accuracy of data refers to the extent to which data is considered to be true, can be relied on and is error-free.
  • Complete data: Data is considered to be complete when it fulfills certain expectations of comprehensiveness in an organization.
  • Consistency: Consistency simply specifies that two data values retrieved from multiple and separate data sets should in no way conflict with each other.
  • Timeliness: Timeliness refers to how recent the event the data represents took place. Data that reflect events that happened recently are likely to show the reality. Using outdated data can lead to inaccurate results and taking actions that don’t remember the present fact.
  • Validity: This refers to how the data is collected rather than the information itself. Information is valid if it is suitable for the correct type and falls within the appropriate range.
  • Relevancy: The data you collect should also be helpful for the initiatives you plan to apply it for. Even if the information you get has all the other characteristics of quality data, it’s not helpful to you if it’s not relevant to your goals.


Conclusion:

Having a robust ETL tool supported by a great scraper is crucial to any data aggregation project. But to ensure that the results meet your needs, you also need to make sure you have a quality check system in place. At DQLabs, we try to eliminate the traditional ETL approach and manage everything through a simple frontend interface by providing the data source access parameters.

To learn how DQLabs manages the entire data quality lifecycle. Schedule a demo

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