47. Three practical ways to fast-track the data cleansing process

Auditing with data: for Performance Auditors and Internal Auditors that use (or want to use) data

Sisällön tarjoaa Risk Insights, Yusuf Moolla, Conor McGarrity, Risk Insights, Yusuf Moolla, and Conor McGarrity. Risk Insights, Yusuf Moolla, Conor McGarrity, Risk Insights, Yusuf Moolla, and Conor McGarrity tai sen podcast-alustan kumppani lataa ja toimittaa kaiken podcast-sisällön, mukaan lukien jaksot, grafiikat ja podcast-kuvaukset. Jos uskot jonkun käyttävän tekijänoikeudella suojattua teostasi ilman lupaasi, voit seurata tässä https://fi.player.fm/legal kuvattua prosessia.

3y ago 9:41

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In this episode we discuss three simple ways to improve the data cleansing process - easier, faster and higher quality.

Find the optimal data source (e.g., don't use a poorly structured dataset)
Cleanse iteratively - you may not need all of the fields/columns
Import iteratively - start with a subset of the data and build on that as needed

About this podcast
The podcast for performance auditors and internal auditors that use (or want to use) data.
Hosted by Conor McGarrity and Yusuf Moolla.
Produced by Risk Insights (riskinsights.com.au).

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#MBA #Business #Internal Audit #Performance Audit #Data In Audit #Data Governance In Audit #Auditviz #Visualization In Audit #Machine Learning In Audit #Full Population Analytics For Audit #Government #Risk Insights

47. Three practical ways to fast-track the data cleansing process

Auditing with data: for Performance Auditors and Internal Auditors that use (or want to use) data

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published 3y ago

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MP3•Jakson koti

In this episode we discuss three simple ways to improve the data cleansing process - easier, faster and higher quality.

Find the optimal data source (e.g., don't use a poorly structured dataset)
Cleanse iteratively - you may not need all of the fields/columns
Import iteratively - start with a subset of the data and build on that as needed

59 jaksoa

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47. Three practical ways to fast-track the data cleansing process

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47. Three practical ways to fast-track the data cleansing process