Data normalization

Data normalisation is the process of organising data in such a way as to be as effective and reusable as possible. Redundancies such as duplicate records can seriously impede the efficiency of a database, and so the removal of unnecessary duplications increases the usefulness of stored data exponentially.

What is data normalisation? 

How many times does a piece of unique information need to appear in a database? Does having multiple records of a name, an address, or any other kind of unique identifier or data record help or hinder the efficiency of a database? If a record is in need of changing, and there are multiple versions of that same set of information, then all of them will need updating.  

This builds in inefficiencies, and in its most basic form, simply wastes valuable disk space and creates entirely unnecessary maintenance workloads. Data normalisation mitigates this by ensuring that all data is handled as efficiently as possible, removing duplicate data to make analysis more effective, and grouping data logically, making sure that related data is stored together. 

Why is this so important? Well, if we want to maximise the value of the data we import and create together, it has to be useable. Without normalisation, the chances are that the vast majority of the data we have will go unused, creating nothing but used disk space, and offering no benefit to anyone. Given how critically important data is, this makes no sense whatsoever. 

Maximising data’s value 

So Exfluency takes the time to ensure that our data is normalised in such a way as to maximise its use–in fact, data normalisation sits at the heart of our entire workflow. The way we build value into all we do is by ensuring that data works for, not against us. 

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