Dirty data isn't useful: Are you cleansing it right?

Get Your Data in the Right Shape for Analysis

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Digging into your data only to find that it is poorly structured, inaccurate or even incomplete is a constant challenge in analytics.

Data prep is an unavoidable step in the analytics process.

There are common problems with manual data cleansing though:

  • It’s rigid and time-consuming
  • It requires deep knowledge of organizational needs
  • Data can be cleansed differently depending on who deems it dirty
  • Data prep silos create redundancy in workflows

Visit this web page for a collection of resources and information about what a proper data prep tool can do for your analytics efficiency.

Vendor:
Tableau Software
Posted:
24 Jan 2019
Published:
31 Dec 2018
Format:
HTML
Type:
Resource
Language:
English
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