Want to enable first-class analytics? Then ensure quality data or data which is clean, relevant and useful. How do you do that? To ensure that your data is clean, relevant and useful, you need to perform data validation. Performing data validation before running any data analysis can help you get better results. Often, a business would use a set of data for years only to discover that the data that it had been using was wrong all along, causing the business to lose trust in the data warehouse environment.
The above demonstrates exactly why businesses need to take the quality of their data more seriously and perform data validation. If you don’t want to learn the importance of data validation the hard or costly way, then you need to take measures that ensure the quality of your data.
Often a topic of great importance, data validation is required to maintain the integrity of databases. Having valid data is the rule rather than the exception since information is constantly being updated, queried, deleted or shared around. With data validation, databases can be made more consistent and functional, allowing them to provide more value to users.
While validation checks may be part of your standard process, they may not be enough for you ensure the quality of your data. So, how can you effectively perform data validation to improve your data quality? Following are some recommended data validation techniques that you can follow.
Tracking Data Issue
By tracking all your data issues from a centralized system, you can identify recurring issues, disclose riskier areas, and ensure the application of appropriate preventive measures. For effective tracking of data issue, you need to make it easy for everyone in your organization to input and report the issues. This is something a data management company such as ZE can help you with.
An important data validation step is validating data upfront before it is added to the data warehouse. Data profiling is a part of this. While it can take you some time to integrate new sources of data into your data warehouse, this step will benefit you greatly in the long-term. By performing this step, you can enhance the value of your data warehouse and the information stored there.
You will be able to identify data issues quickly and efficiently if your data integration flows are designed to ensure data quality. Additionally, you can solve the issues quickly if you perform checks along the way. For example, building strong stop and restart processes into your workflow will allow you to trigger a restart to find what caused the issue in case you find an issue in the loading process. Additionally, it will enable you to automatically deal with common environmental and performance challenges. Moreover, it will trigger data quality checks that only occur at the sub-task level during the processing period.
While you cannot eliminate data integrity issues, you can minimize them by using the above data validation techniques. Another way to ensure proper data validation is taking help from a data management company such as ZE. For more information, get in touch with us!