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Data Quality and Data Integrity

Growing up I would have never thought that I would find so much interest and enjoyment in validating data; specifically, in data quality and data integrity.  My interests and strengths were in the arts and to be quite honest I don’t even think I mentioned the word “data” for the first 25 years of my life let alone understood what data quality and data integrity meant.  Even if I had been told their meanings at a younger age I was never in a position to understand their definitions in a practical way.


As I write this blog I am trying to think of any instances throughout my adolescence that would have predicted my interest in this area.  I’m coming up empty handed for anything obvious.


Regardless, it is something that I enjoy now and it is something that I am very good at.


Data quality isn’t optional.  It is a must. 


It is essential that our data is unambiguous and accurate to ensure that our day-to-day operations remain productive and straight forward.  This level of quality prevents costly errors and when analyzed allows us to make informed decisions. 


Data integrity is essential.  Full stop.


In our business there is no excuse for a lack of data integrity and as part of my job I am tasked with reviewing, correcting and enforcing data integrity across a number of REM’s systems; project databases, client datasets and financial systems.


Providing high quality data that is accurate and consistent across all systems can be a bit more time consuming but it is worth the extra effort.  Incorrect and disordered data can have very negative ramifications and can take much longer to fix (if any issues that arise can be fixed at all).


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