Vast amounts of data are created each day and businesses employ in-house data analysts or a data analysis company to inform decision making and create marketing strategies. While data analysis is usually highly effective, it will only give good results if the data is valid.
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To be valid, data must be representative of the business metrics, rules, and definitions it describes as well as also being relevant. If the data is invalid, it can result in the wrong conclusions, leading to poor outcomes in decision-making and marketing.
Causes of Invalid Data
Causes of invalid data include data entry errors caused by mistakes, system glitches, or downtime which may mean that insufficient or wrong data has been collected or possibly intentionally falsified.
Ensure Data Validity
Using data validation rules about what gets input into a system can help reduce the risk of human error and so improve the validity of the data.
Make sure that data is coming from a good source. You may have your own in-house staff who can check this, or you could employ a data analysis company like https://shepper.com. By using reputable data sources, you can be more confident that the data is valid.
Use anomaly detection tools that can recognise if there are data points that fall outside the expected range. Take a look at Data Camp to learn more about how these work and can be used.
Measuring Data Validity
By putting metrics in place, you can assess the validity of the data you are using. These can include the completeness rate which checks whether the expected percentage of data is being found in the data set and the time that elapsed between the event that generated the data and the point it appeared in the data set.
You should also check the accuracy rate of the data as the greater the percentage of data that is correct, the better the validity. These metrics need to be tracked over time but will show if you need to be concerned about your data validity.