We’re excited to announce our new iData Academy course, Data Cleansing – The Basics, by Susan Walsh the Data Classification Guru. To help you understand what the course contains, we’re outlining some of the important learnings from the course in this post.
Why is Data Cleansing So Important?
Improving your data quality is important for many, many reasons, not least because of the overall increase in productivity that arises when you don’t need to spend time fixing data.
The different reasons for looking at cleansing your data can be broken down into two distinct categories, so we will look at each:
Regulatory – new data privacy laws such as the General Data Protection Regulation require an increase in the accuracy, integrity and completeness of your contact data over older laws such as the Data Privacy Act. It also helps if you have all of your data in one place so that it can easily be sent to anyone that asks for the records you hold on them. There are various other laws that look at data too, but GDPR is the most famous.
Operationally – at your business, every single employee in every single department will be using data either directly or indirectly every day. Typically when we think about the Data part of Data Quality, we’re thinking specifically about contact data quality IE Email, Address and Mobile data. Anything that could be used to contact someone. These are the types of data typically used in business, but there are other types of structured data that we think about when we talk about data quality, such as Customer ID, Credit Card numbers and more. Typically, you won’t hear much about unstructured data such as raw image files because they’re handled in a different way.
How is Data Cleansing relevant to you?
You can hear us talk about this in our webinar The Opportunity of Data Quality, where Alex talks about being a data-driven person around the 5 minute mark.
Clean or cleansed data is actually relevant to every connected person in the world.
When you move house and you update your address on Amazon, that is literally you improving Amazon’s data quality on your contact data by improving the accuracy and integrity of your address record.
When people talk about dirty data, typically they mean out of date or otherwise unusable. The importance of a high level of data quality varies from organisation to organisation, but typically from important to business-critical.
Imagine if your university offer letter didn’t arrive, because the university had the wrong address for you.
Excel is actually pretty good at Data Cleansing manually (If you have the time):
Small business owners – for instance someone with a booking-only hair salon in a small town might only keep a small list of emails and names for bookings, without the need to store more data such as date of birth or payment details – can simply keep an excel list of their customer’s data.
Even if, for instance, the salon owner spots a typo or duplicate in the email records, fixing that is easy enough in Excel, and the latest versions are powerful enough to deal with thousands of records to cleanse data.
Contact us for more information: Marketing@intelligent-ds.co.uk
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As data quality can be complex and time-consuming, it’s often difficult to know where to start. We’ve put together this helpful checklist to point you in the right direction.
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