What is “dirty data” and how can you tackle it?

The concept of “dirty data” and the way to approach it can be daunting. Because “dirty data” is a problem that burdens data-driven companies of all sizes and industries on a daily basis. The time and energy it takes to extract actionable insights from disjointed data leads to inefficient ad hoc analysis and declining trust in corporate data. In our short blog update, we’ll show you five simple tips on how to say goodbye to dirty data.

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What is “dirty data” and how can you tackle it?

The term “dirty data” refers to data that contains erroneous information. In general, a distinction can be made between five types of “dirty data”:

  • Incomplete data
  • Duplicate data
  • Incorrect data
  • Inaccurate data
  • Inconsistent data

The reasons for the emergence of the phenomenon of “dirty data” are manifold – among other things, it is due to incorrect data entry or improper methods in data management and data storage. Regardless of the cause, “dirty data” costs companies millions of dollars every year. Unfortunately, keeping databases clean is not as easy as picking up garbage on the side of the highway.

That’s why it’s extremely important to prevent “dirty data” if you don’t want to go through the often tedious process of data cleansing.

In the infographic above, we present 5 simple tips that you can use to put a stop to the emergence of “dirty data”.


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