Inconsistent Data : 3 Common Causes

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Today's post is inspired by the cake I would be making for my mom today.

Consistency is a term that denotes uniformity, evenness, and regularity in maintaining a constant state.

Imagine a cake that wasn't mixed well before baking. Some parts might have lumps of baking soda, while others have too much sugar.

If this happens, you probably won't enjoy eating the cake, and you might even avoid it altogether after the first bad experience.

Now, let's talk about consistency in the world of data. Picture you're collecting data about ice cream flavors. 

In one table, you write "Cookies and Cream," but in another table, you write "Cookies & Cream."

Even though these mean the same flavor, a computer would treat them as two different things when creating a report.

Here are three common ways in which inconsistent data is created:

Manual Entry Errors

Typos, incorrect values, and misinterpretation of data can lead to inconsistencies across datasets.

Integration Issues

When merging data from various sources or systems, inconsistencies may arise due to differences in data formats, structures, or coding standards. 

Incomplete Updates

If a record is updated in one database but not in another, it can lead to disparities and inaccuracies in the overall dataset.


In conclusion, inconsistent data jeopardizes decision-making and erodes trust. Addressing these challenges is vital for maintaining a reliable data environment.


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