Fixing Dirty Data - How to clean Data by classification and Normalization | Susan Walsh

Sep 30, 2024

40:02

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In the first ever English Episode of UNF#CK YOUR DATA host Christian Krug interviews Susan Walsh, the classification guru, on how to clean your dirty data.

But firstly, what is dirty data and why does this pose a problem?

Data in your company systems, like CRM or ERP, can have all sorts of issues. Duplicates, near duplicates, formats and so on.

So the records which should match, don’t. Or your numbers are off.

Basically, you can’t rely on the data in the system to make decisions. Like sending a mail or a leaflet. Potentially even an invoice. Or know who your real number one customer is.

To help you deal with this mess, Susan has created a framework, which helps you cleaning up your data. You have to normalize and classify your data. First agree on a common format an fit the data to it. Afterwards you can give the data a meaning by classifying it.

So you can further process the data and base your decisions on it.

Sad news for all the AI enthusiasts out there: This still requires an awful lot of human knowledge. No speeding up the process.

On the other hand this step is crucial for your AI success. As only good quality training data will lead to great AI results. Regardless, which use case you tackle first.

But cleaning data one is not a lasting solution. It’s a continuous effort and it hast to start at the very source where people enter the data into the systems.

So data quality is a process and mantra.

Find in this episode:

- Why data sometimes is so dirty

- How a COAT method can help you clean data

- Why data quality is not an AI topic

- Susans plans on a new framework

▬▬▬▬▬▬ Profiles: ▬▬▬▬

Zum LinkedIn-Profil von Susan: https://www.linkedin.com/in/susanewalsh/

Christian at LinkedIn: https://www.linkedin.com/in/christian-krug/

Unf*ck Your Data at Linkedin: https://www.linkedin.com/company/unfck-your-data

▬▬▬▬▬▬ Book recommendation: ▬▬▬▬

Susans book recommendation: Buy back your time - Dan Martell