Dirty data is costing you

4 ways to overcome common data prep barriers

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If you’ve ever analyzed data, you know the pain of digging into your data only to find that the data is poorly structured, full of inaccuracies, or just plain incomplete. You’re stuck adapting the data in Excel or writing complex calculations before you can answer a simple question.

Even those who aren’t directly performing data preparation tasks feel the impact of dirty data. The amount of time and energy it takes to go from disjointed data to actionable insights leads to inefficient analyses and declining trust in organizational data.

This eBook explores the causes and effects of dirty data and offers insights into how to clean, maintain and reliably analyze your data. 

Why Dirty Data Happens

Human error, disparate systems and changing data requirements are all common causes of dirty data.


How to prevent inaccuracies & miscommunications in data preparation.

data silos

Creating consistency and collaboration within the data prep processes to prevent data loss.

Rigid & time consuming

Remove the data preparation task from IT to allow for faster, more efficient processes & analysis.

Competing interests

Who is analyzing the data and to what end? Do competing interests result in flawed processes?

Tableau Dirty Data is Costing You eBook cover