Blogs

What can I clean next?

Spring 2021 is looking a lot like Spring 2020. At home, locked down and looking for projects to do around the house. My home is my sanctuary. It provides the foundation for every other aspect of my life. Data is much the same. It is a foundation on which business decisions are made, efficiencies are identified, and growth can be achieved. In the same way that cracking foundation or rot in your walls need attention, bad quality of data also needs ongoing maintenance and attention. Bad quality data can have disastrous effects on your business including inaccurate reports and wrong business decisions or an inability to meet regulatory standards.

Data quality can be defined as the process of conditioning data to meet the specific needs of business users. Accuracy, completeness, consistency, timeliness, uniqueness and validity are the chief measures of data quality.

However, data cleansing is not your run of the mill spring cleaning initiative. It is not a one-time activity, nor can you expect to cleanse all of your data without clear direction or specific use case. It is like trying to rebuild your whole house when you should instead think about repairing the foundation for ensuring a solid structure, or cleaning your windows for better visibility, or painting a focal wall to add beauty to your home. Our recommended approach to data cleaning is to start with the end in mind. Identify an analytics use case and then work backwards to determine the datasets for that use case. This gives you manageable cleansing activities with quick results and improved ROIs. Wondering how to get started? Here are our steps that we can help apply to your business use case:

1. Identify the analytics use case (not a data cleansing use case)
2. Identify datasets for that use case.
3. Decide how much cleansing is required. Data shows that it does not need to be 100%.
4. Choose a Data Quality toolset backed with machine learning. One that can provide cleansing suggestions.
5. Define cleansing rules for new data refreshes.
6. Check on analytics accuracy.
7. Rinse and repeat.

 Ready to get started? We bring solid experience in data engineering, data governance and data quality. Let us help you build a better foundation for the analytics that drives your business decisions and instils confidence in your stakeholders.

More from this Author