@@firza6674 please Can I have your contact? I would like to ask some questions and get some information from you because I just got a job as a Data Quality Analyst too
Good video and we particular like the section on data profiling at 13:01 as this is something we have been working on for many years in our tools, and by using this method you can quickly discover patterns and meaning in your data, and to check the quality of it by analyzing formats, types, completeness and value counts. Great stuff 👍
At around 14:20, for the data quality example, in the data assessment results, you mentioned Validity failed results as 2. But the date looks valid as 2/30/1968 which is correct according to the rule. Please correct me if I a wrong.
There are many connections between data quality dimensions. Should an analyst include number of blank values in both Completeness and Validity? Should he/she include blank values in duplicate values?
If the field is blank, or Null, then you measure that in your completeness metric. If the value in the field is "blank" and that value is not a valid use of that field, such as if you had a product name field and the value was "blank", then you wouldn't measure that as completeness, you would measure that as Invalid.
this is so well explained, i came here knowing nothing about data quality, and now i think im ready for my job interview. wish me luck guys
i got the job yall😭
Me too.
@@firza6674 Congratulations❤
@@firza6674 please Can I have your contact? I would like to ask some questions and get some information from you because
I just got a job as a Data Quality Analyst too
Simply, short, sweet and on point! Loved it!
Thank you so much for putting this together, it's so helpful for my Data Quality Analyst interview prep.
Very well structured and presented. Thank you!
Good video and we particular like the section on data profiling at 13:01 as this is something we have been working on for many years in our tools, and by using this method you can quickly discover patterns and meaning in your data, and to check the quality of it by analyzing formats, types, completeness and value counts. Great stuff 👍
Nice information..hope will have more video's in future..Thanks
this info is still useful, thank you so much!
DOB cannot be future date, this also rule we can apply on DOB column.
Thanks for the video though, it really helpful and i am learning a lot :)
Well explained and presented. Thanking you
Very Good Video ,Thanks
Hi This is very informative and i learn very good about DQ, can you please share this presentation for us? Thanks
Wahoo great lesson
Wow.. perfect
very good video! I have been looking for something like that. Thanks
Very well explained and clarified.
Good👍
Thank's
Good and easy !!!
Hi. Thank you for this very informative presentation ❤ may i have a copy of the slides? Thanks so much.
Thanks. I am struggling to learn this.
Thanks!! Very Nice!! Please remove background music...
At around 14:20, for the data quality example, in the data assessment results, you mentioned Validity failed results as 2. But the date looks valid as 2/30/1968 which is correct according to the rule. Please correct me if I a wrong.
:O :O :O How could I miss such a silly point. Sorry. And, Thank you. :)
@@tenniswillington I did it too :) haha
where can I find the quiz, please?
There are many connections between data quality dimensions. Should an analyst include number of blank values in both Completeness and Validity?
Should he/she include blank values in duplicate values?
If the field is blank, or Null, then you measure that in your completeness metric. If the value in the field is "blank" and that value is not a valid use of that field, such as if you had a product name field and the value was "blank", then you wouldn't measure that as completeness, you would measure that as Invalid.
you didn't even bother reading the text?