Similar to how following a well-written baking recipe results in a mouth-watering dish every time, implementing data quality validation rules maintains the integrity and consistency of your organization's data every time it’s acted upon. Data validation rules act as checkpoints, verifying that data stored in your systems conform to the required standards. What do these rules look like and how do you implement them effectively? This blog post will provide examples and share best practices for keeping your data as pristine as the ingredients in a master chef’s pantry.
Types of Data Validation Rules
To better understand data quality validation rules, let's explore some common examples.
Just as you wouldn't pour milk into a measuring cup meant for flour, data type validation ensures that a specific field contains the appropriate type of information. For example, a name field should be a string, not containing any digits.
Range validation is like specifying the appropriate temperature for cooking your dish. It sets the boundaries for numerical values to ensure they fall within a specific range. For instance, a data field could require a number between 0-1000 or a transaction amount greater than $0.
Consistent expressions are like the standard abbreviations used in a recipe. To maintain consistency and avoid confusion, data entries must use a uniform format. For example, using either "Senior," "Sr.," or "Sr" for a job title.
Controlled Pick-List or Reference Data
This validation rule is similar to following a list of approved ingredients in a recipe. It ensures that data entries are selected from a pre-defined set of options, such as ISO-3166 country codes for location data.
Conformity to Business Rules
Adhering to business rules is like ensuring that your recipe's steps are followed in the right order. For instance, a return date must always be greater than the purchase order date, preventing illogical data entries.
Syntax validation is like the grammar of a recipe, ensuring data entries follow a specific format. For example, date fields should follow the DD-MM-YYYY format, and email addresses must include an "@" symbol.
Best Practices for Implementing Data Validation Rules
Now that we've explored the most common types of data validation rules, let's discuss some best practices for implementing them effectively.
Define Clear Data Standards
Establishing clear data standards is like having a well-written recipe. It provides a foundation for validation rules, ensuring consistency and accuracy. Work with stakeholders to define the rules that best suit your organization's needs.
Continuously Monitor and Improve
A master chef is always refining their recipe, and data quality should be approached similarly. Regularly review your data validation rules and update them as needed to accommodate changes in business requirements or regulations.
Train and Educate Users
Educating users about data validation rules is akin to sharing a recipe with a fellow cook. Training ensures that everyone understands the importance of data quality and adheres to the established guidelines. Regularly reinforce the rules and provide resources for users to reference.
Establish Data Governance
Just as a kitchen requires a head chef to oversee and manage operations, implementing data governance helps maintain data quality. Assign responsibilities and establish processes for data validation, ensuring that rules are consistently applied and updated as needed.
Test and Validate
It's essential to taste-test a dish before serving, and data validation rules should also be tested regularly. Verify that the rules are functioning correctly and make adjustments if necessary to maintain data quality.
Implement Data Cleansing
Occasionally, a chef may need to remove a stray ingredient from their dish. Similarly, data cleansing is the process of identifying and rectifying errors, inconsistencies, or inaccuracies in your data. Regular data cleansing helps maintain data quality and reinforces the effectiveness of your validation rules.
Automate the Validation Process
Just as a chef might use a mixer to streamline the cooking process, automating data validation can save time and reduce human error. The data observability platform Telmai can help you get started with data validation quickly with its real-time monitoring and alerting capabilities. You can schedule to run data validation on an hourly, daily, weekly, or monthly basis and automatically see alerts when your data validation falls outside expected norms.
Data quality is an ongoing process
Remember, data quality is an ongoing process that requires continuous monitoring, refinement, and adaptation. Regularly review and update the data validation process as needed; outliers and anomalies could inform you to incorporate more validation rules into your workflow.
With a better understanding of data quality validation rules and their importance, you're now equipped to embark on your own data quality journey. Bon appétit!
Data profiling helps organizations understand their data, identify issues and discrepancies, and improve data quality. It is an essential part of any data-related project and without it data quality could impact critical business decisions, customer trust, sales and financial opportunities.
To get started, there are four main steps in building a complete and ongoing data profiling process:
We'll explore each of these steps in detail and discuss how they contribute to the overall goal of ensuring accurate and reliable data. Before we get started, let's remind ourself of what is data profiling.
1. Data Collection
Start with data collection. Gather data from various sources and extract it into a single location for analysis. If you have multiple sources, choose a centralized data profiling tool (see our recommendation in the conclusion) that can easily connect and analyze all your data without having you do any prep work.
2. Discovery & Analysis
Now that you have collected your data for analysis, it's time to investigate it. Depending on your use case, you may need structure discovery, content discovery, relationship discovery, or all three. If data content or structure discovery is important for your use case, make sure that you collect and profile your data in its entirety and do not use samples as it will skew your results.
Use visualizations to make your discovery and analysis more understandable. It is much easier to see outliers and anomalies in your data using graphs than in a table format.
3. Documenting the Findings
Create a report or documentation outlining the results of the data profiling process, including any issues or discrepancies found.
Use this step to establish data quality rules that you may not have been aware of. For example, a United States ZIP code of 94061 could have accidentally been typed in as 94 061 with a space in the middle. Documenting this issue could help you establish new rules for the next time you profile the data.
4. Data Quality Monitoring
Now that you know what you have, the next step is to make sure you correct these issues. This may be something that you can correct or something that you need to flag for upstream data owners to fix.
After your data profiling is done and the system goes live, your data quality assurance work is not done – in fact, it's just getting started.
Data constantly changes. If unchecked, data quality defects will continue to occur, both as a result of system and user behavior changes.
Build a platform that can measure and monitor data quality on an ongoing basis.
Take Advantage of Data Observability Tools
Automated tools can help you save time and resources and ensure accuracy in the process.
Unfortunately, traditional data profiling tools offered by legacy ETL and database vendors are complex and require data engineering and technical skills. They also only handle data that is structured and ready for analysis. Semi-structured data sets, nested data formats, blob storage types, or streaming data do not have a place in those solutions.
Today organizations that deal with complex data types or large amounts of data are looking for a newer, more scalable solution.
That’s where a data observability tool like Telmai comes in. Telmai is built to handle the complexity that data profiling projects are faced with today. Some advantages include centralized profiling for all data types, a low-code no-code interface, ML insights, easy integration, and scale and performance.
Start your data observibility today
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Start your data observability today
Connect your data and start generating a baseline in less than 10 minutes.