Data Quality

This is the bottom line of the project. Everyone likes to invest in the design of the final product, to display beautiful dashboards and to develop high quality loading processes, but at the end of the day the data is the most crucial component. Since we are helping managers make decisions based on the displayed data we need to ensure it is of the highest quality.

The Challenge

Processes that end on time and without error indicate nothing about the quality of the data presented to users. For example, if ETL processes are run on empty tables they will most likely end successfully and on time without any warning of something going wrong. That same result could be obtained if the process did not run at all. And these are just rudimentary examples. It is important to note that the amount of failures that can occur vary and are unpredictable. Failures can stem from a change in the data structure, change in work assumptions that were relevant during development, unexpected data from operating systems, corrupted data written for files and more.

However, if we were to consolidate the failures to one attribute it would be that all possible points were unknown during system development. There is no need to try to guess, there will always be surprises. It is not recommended to leave a data system without controls.

Why Us?

In order to address data quality, we have developed Quilliup, a data quality control system. Today this system is integrated with most of the company’s local and abroad customers, including two of the world’s largest banks. We believe that data controls are an essential part of the process. If you are already using a system, we have an organized methodology for implementing broad controls to protect the data quality in the most effective way. This can improve the project without the need for the resources of the development team. Overall, moving the system with the scheduled controls is straightforward.

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