Data ETL, or Extract, Transform, and Load, is the process of preparing data for analysis. It is a critical part of any data warehousing or data integration project. However, ETL can be challenging, and one of the biggest challenges is ensuring the quality of the data.
Data quality is essential for any data-driven organization. Without high-quality data, making accurate decisions, building effective models, or producing reliable insights is impossible.
This blog post will discuss the best practices for solving data ETL data quality challenges. I will cover defining data quality metrics, implementing data quality checks, correcting data quality problems, and monitoring data quality.
Defining Data Quality Metrics
The first step in solving data ETL data quality challenges is defining the quality metrics important for your organization. These metrics will help you identify and measure the data quality problems you must resolve.
Some standard data quality metrics include:
- Data Accuracy within known quality ranges
- Completeness of data records and sets
- Data consistency across the data set compared to historical data sets
- Timeliness measures. Acceptable timeframe for the data
The specific data quality metrics you choose will depend on the particular needs of your organization and analysis needs. However, it is essential to define a set of metrics that will allow you to measure the quality of your data.
Implementing Data Quality Checks
Once you have defined your data quality metrics, you must automate quality checks. These checks will identify and flag data not meeting your quality standards.
There are several different ways to implement data quality checks. You can use custom code, data profiling tools, or ETL tools with built-in data quality checks. Alteryx Designer Cloud has native data quality services to access a data set quality and create rules for remediation.
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Figure 1 - Alteryx Designer
The specific method you choose will depend on your organization's particular needs. However, automating data quality checks and resolution actions is essential to identify and flag data quality problems at scale.
Correcting Data Quality Problems
Once you have identified data quality problems, you need to correct them. Correcting data quality may involve cleaning the data, updating the data, or deleting the data. Cleaning data should be automated to enhance data consistency by correcting text case, removing whitespace, and removing punctuation. Machine learning algorithms from historical data sets can populate missing data and create synthetic data sets to enrich your analysis. With large enough data sets like time series data, suspect or incomplete data can be automatically deleted.
The specific methods you choose to correct data quality problems will depend on the particular nature of the issues. However, fixing data quality problems as soon as possible is essential.
Monitoring Data Quality
It is essential to monitor the quality of your data continuously. Monitoring data problems will help you identify new issues, improve existing data sets, and improve overall data quality. This process is imperative as you manage data at scale and acquire external data from direct business partners and data brokers (i.e., economic data, weather, traffic). Even with upfront data checks and automated data cleaning, continuously applying those learnings (i.e., schema changes and updated data quality rules) to your existing data sets is critical. Monitoring data quality in the system enables businesses to identify problems quickly, notify the company, and correct the data. Building new machine learning models and analytic data sets requires access to historical and recent data sets. Continuously evaluating and investing in keeping your existing data of the highest quality is necessary to maximize the value of your business.
There are several different ways to automate and monitor data quality. Upwards of 80% of these automation are custom-built today, augmented with low code tools like Alteryx to create data quality reports, dashboards, or alerts.
Conclusion
By following these best practices, you can help ensure that your data ETL process produces high-quality data. Quality data will allow you to make better decisions, build more effective models, and create reliable reports.
About Lydonia Technologies
Lydonia Technologies is a leading provider of data automation and analytics solutions. We help organizations extract, transform, and load data from various sources in various formats. We also provide solutions for analyzing your data, leveraging AI to identify and action high-value business insights quickly.
If you face data ETL challenges, contact Lydonia Technologies today. We can help you to overcome these challenges and to achieve your data goals.