When an organization decides to perform a data-cleaning exercise, they have the option of building their own data quality system or use existing tools. There is nothing wrong with custom-building your own system, but this option may prove time-consuming and uneconomical in the long run. What’s more, your developers are likely to make mistakes along the way, prolonging the project even further.
Going for an existing tool, on the other hand, will cost you less and will be available for use almost immediately. In addition, the tool will be easier to understand since people are already using it, and there is probably tons of information about its use on the internet.
Whichever option you go for, ensure you are getting everything you need in one place. Versatility is essential when there is more than one function you need to perform with the raw data you have in your system. If you have to use more than one tool, there is a chance you will face5 compatibility issues along the way, which may call for manual entry of data in some stages. That would not only be too inconveniencing but also increase the risk of costly mistakes as you handle the data.
The best tool is the one that can handle data management functions other than data quality enhancement. A platform that has all the inbuilt features to make autonomous execution without any manual or configuration. Given the innovation in AI/ML world, this is possible. Pick a tool that has scalability, governance and automation from end to end is made possible or at the least provides integration and compatibility with other tools in the data ecosystem.
Why AI/ML Based Data Quality makes sense?
Impacts of Poor Data Quality
Steps to collect High-Quality Data
How to Build Your Data Quality Team
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