To be a Proactive Data Practitioner :
1. The Validate begins with data for every pipeline
Data frequently comes from a single source before moving through several systems. The only method to stop bad quality data from replicating and spreading across your data pipelines is to assess its quality at the source. Give the validation of data before allowing it to circulate freely within your organizational culture. As soon as anything becomes a part of your core operations, you should verify to make sure it is structured properly, is duplicate-free, and is pertinent to your company plan.
2. Validation of Data upon Ingest
The value of automatically verifying data when it enters your environment might be enormous for your company profile. Here is a straightforward validation of data that help prevent problems in the future:
. Validating dates: Make that all dates are written in the appropriate format, such as dd/mm/yyyy. Depending on the data you wish to get, you may also authorize past or future dates.
. Value verification: You can compile a list of acceptable responses, such as state or nation names or phonetic codes. In this manner, you only receive data that is pertinent to you.
- Reasonable Value: Ensure that only pertinent data is accepted in all form fields. Don’t accept “na” as a surname, for instance.
3. Carry out Data Quality Monitoring
You wouldn’t let a bull unsupervised in a porcelain store. The same guidelines also apply to your data. Even if you pledge for validation of data at intake, you must continue to check on its consistency throughout time. This ensures the overall health, relevance, and use of your data. But make sure to check on the facts influencing your business choices. If you cast your net too wide, you face the danger of information overload and can overlook significant data quality concerns in mission-critical services. Prioritizing and classifying information are required to find mission-critical information. Prioritize all of the information first depending on factors like:
- Impact on income and productivity.
- Recovery time after a backup.
- Performance appraisal of the application and data preservation.
- The necessity of security.
Then you may say no to such date or both. Important: You would very instantly detect the data harvesting. Sensitive: You wouldn’t realize this data was gone for several days. Not critical Even if you were unaware that the information was missing, it would have no effect on your company. Concentrate on the information you would consider to be either sensitive or critical. Anything else is not significant enough to warrant further observation.
4. Ensure your Reporting is Timely and Effective
Reporting on validation of data migrations and integrations is rarely done well. You need to give stakeholders timely information so they can react quickly to critical issues. The best reporting pipelines assign tasks so everyone knows the next steps. Some common report examples are :
Data quality reports.
Effort reports
Resource utilization reports
When delivering these reports, ensure they contain only relevant and actionable information. Don’t cover up the signal with too much noise. If business leaders can’t prioritize data quality actions, you will struggle to make the strategic changes you need to succeed.
