What is Data Validity? Definition, Examples, and Best Practices
What exactly is data validity, and how can you ensure that you're working with valid data? In this post, we'll explore the definition of data validity, its importance in data analytics, and best practices for measuring and maintaining data validity.
How confident are you that the data you're working with is actually valid?
Valid data is crucial for both operational and decision-making purposes. When data is valid, businesses can make accurate and informed decisions that can ultimately impact the bottom line in a significant way. For example, a sales leader might make regional expansion decisions based on revenue data.
What is Data Validity?
Data validity refers to the degree to which business rules or definitions are accurately represented. In other words, data must be relevant and representative of the business metrics it describes. The opposite of valid data is invalid data, which can lead to inaccurate conclusions and negatively impact analytics.
Data validity is one of the ten dimensions of data quality, which also include data completeness, data timeliness, and data consistency, among others. Ensuring data validity is essential for maintaining overall data quality, which is critical for any data-driven business.
Examples of Invalid Data
Invalid data can be caused by a variety of issues, such as data entry errors, system glitches, or even intentional falsification. Here are a few examples of how invalid data can negatively impact business analytics:
- Data Entry Errors: Imagine you're a sales clerk that's accidentally scanned an item twice. This issue make its way into the downstream warehouse, inflating the total revenue number for the day.
- System Downtime: Using the example above, the POS system has gone down, leading to an inability to get revenue numbers for the day, leading to incorrect revenue numbers for the month.
- Intentional Falsification: In the final scenario, a VP of Sales is responsible for the monthly revenue numbers, and manually changes an input to give the appearance of hitting the numbers. In this case, the reporting numbers given to the board may show success, but contain invalid data.
How do You Measure Data Validity?
As with any aspect of data quality, it's essential to have metrics in place to measure data validity. Here are some real-world metrics that data teams commonly use to measure data validity:
- Completeness rate: The percentage of expected data that is present in a dataset.
- Accuracy rate: The percentage of data that is correct.
- Timeliness rate: The amount of time that elapses between the occurrence of an event and the data's inclusion in the dataset.
By tracking these metrics over time, data teams can identify trends or issues that may need to be addressed.
How to Ensure Data Validity
There are several best practices that data teams can follow to ensure data validity, including:
- Use data validation rules: Implement a set of rules that data must meet before it can be input into a system. This can include things like field length requirements or data type limitations.
- Role of anomaly detection: Utilize anomaly detection tools to identify data points that fall outside the expected range. This can help identify data quality issues quickly.
In conclusion, data validity is a critical aspect of data quality that data teams must prioritize. Ensuring data validity can help businesses make informed and accurate decisions that can ultimately impact the bottom line.
Data observability tools like Metaplane improves data validity initiatives by continuously monitoring and validating data quality from the warehouse down to usage in the business intelligence tool, continuously retraining based on your actual data and processes.