29 Mar 5 Metrics To Assess Data Quality
Understanding the quality of your data is vital for any business. If the quality of data isn’t up to scratch, insights and decision making can be skewed.
There are a few key metrics that can assess whether the accuracy, completeness, consistency, reliability and time frame of your data are intact. These factors contribute to the quality of data, and how usable it will be.
In this article, we’ll explore the key metrics to help you assess data quality.
What is Data Quality?
Data quality refers to the condition of your data based on factors like accuracy, completeness and reliability. A high quality dataset is usually achieved by proper data management practices such as validation, verification, scrubbing and cleansing.
Assessing data quality is an important step, as it will help you understand whether your data is producing meaningful insights, or whether there’s more work to be done. Data quality can be assessed in a number of ways, however there are a few key metrics that are broadly accepted to best help you understand the accuracy and completeness of your data.
With these metrics, you’ll easily be able to assess the overall quality of your data while also determining the resources needed, skill level required, and type of data output.
5 Metrics For Data Quality Success
If you’re wanting to audit your data to assess data quality, here are some of the key metrics to look for:
Completeness: Assessing the presence and accuracy of data in all fields.
Completeness is an important metric to measure when assessing data quality. This metric ensures that all fields have data present, and that the values are both accurate and correct.
Ensure that your dataset contains all necessary information, with no gaps or missing fields, in order to properly assess completeness.
Integrity: Verifying the relationships between different pieces of data are correct, consistent, and complete.
Integrity is an important data quality metric to consider when assessing the accuracy of your data. This metric ensures that relationships between different pieces of data are valid, consistent and accurate.
To ensure the integrity of your dataset, validate the relationships between fields. For example, ensure that when the value of one field changes, the related fields also update to reflect this change in values. This way you can verify that the links between different pieces of data are correctly established with no discrepancies present.
Accuracy: Ensuring that the data returned matches what’s expected within a defined tolerance.
Accuracy is a critical element of data quality. It ensures that the data returned match what’s expected from the query, within a defined tolerance. This metric requires an understanding of the underlying data elements and business rules to know the expected outputs when querying for certain values.
Additionally, it requires knowing what type of data is most accurate and its accuracy level when compared to other available sources. Evaluating accuracy involves comparing the data we receive to what we expect, if there is a discrepancy, further investigation into the issue should occur.
Timeliness: Evaluating how up-to-date or current a set of data is when compared to its validation time period.
Timeliness is the measure of how current and up-to-date a dataset is when compared to its validation time period. The validation time period should be pre-approved by the company that owns the data as it would act as the standard timeline in which an enterprise can confidently accept or reject specific datasets.
Data timeliness should be monitored on regular intervals in order to determine whether or not a specific dataset is still accurate and valid for its intended purpose since it may become outdated after a certain point.
Validity: Checking that the values found in each field meet the criteria specified by relevant standards or regulations.
Validity ensures that data found in a field meets the criteria specified by the standards or regulations established by the company or institution. It is important to ensure that all of these validation criteria are properly documented and updated regularly, as not knowing what should be expected within a dataset can lead to inaccurate results.
Additionally, the process of validating data through manual checking can be time consuming, requiring a high degree of effort across functional teams to assess, review and transform/fix.
Improving Your Data Quality Can Have Many Benefits
Did you know that low quality data can be the cause of data transformation failure rates? Equally, low quality data can be responsible for your email open rates being low.
The fact is, data quality can be the underlying cause of a lot of business-wide problems, from finance and sales through to customer service and marketing. When the quality of data is improved, the whole business is then able to make decisions based on accurate data, and that’s a game changer!
Want to learn more about improving your data quality and using data to your business advantage? Let’s discuss your data today!
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