354 Posts
This question came in earlier for you, Gary. Why is data quality so often overlooked by organizations?
8 Replies
19 Posts
I believe that many companies are actually "thrilled" that they have data. So many do not and when they do have it, they believe that it MUST be right! However, as our Six Sigma training teaches, data must be accurate or we will off checking things that are irrelevant or incorrect. Just because it exists, does not mean it is correct.
664 Posts
For one, I have a hard time convincing the right people that it is worth the effort. We are not manufacturing and not automated, so collecting data and ensuring data integrity are both difficult.
23 Posts
So, you work in a non-automated service environment? is this correct? If so, what are some of the data review methods you use (to analyze your measurement systems or the integrity of your data)?
664 Posts
I work in an integrated logistics company. Basically technical publications. We produce reports and tech manuals, but each product is unique. We have tech writers, desktop publishers, logisticians, illustrators. Sometimes we call it engineering services, but technically we are neither service or manufacturing.
19 Posts
Specific challenges I've faced in collecting data are the following:
(1) Pencil-whipping. "Checking the box" that the product passed rather than specifically showing what the measurement was.
(2) Lack of training for tools. One plant manager learned that over half of his folks did not know how to use a measuring tape even though he expected them to complete measurements on their products every 2 hours.
(3) Having operators simply regurgitate what the label says measurements should be rather than measuring the product to confirm that the data was actually "as specified."
(4) Accessing the data. I may have a database that tells me that I had 11 rejects off of a line, but it doesn't tell me what those rejects are. I may have another database that tells me what all the rejects that I had were... but it doesn't tell me the line that they came off of.
This last one is a HUGE struggle when it comes to big data. You lost 10% of your product, but you don't know why... or, you lost 10% due to scratches, but you don't know what product they were on. This is a REAL problem when trying to do root cause analysis.
(1) Pencil-whipping. "Checking the box" that the product passed rather than specifically showing what the measurement was.
(2) Lack of training for tools. One plant manager learned that over half of his folks did not know how to use a measuring tape even though he expected them to complete measurements on their products every 2 hours.
(3) Having operators simply regurgitate what the label says measurements should be rather than measuring the product to confirm that the data was actually "as specified."
(4) Accessing the data. I may have a database that tells me that I had 11 rejects off of a line, but it doesn't tell me what those rejects are. I may have another database that tells me what all the rejects that I had were... but it doesn't tell me the line that they came off of.
This last one is a HUGE struggle when it comes to big data. You lost 10% of your product, but you don't know why... or, you lost 10% due to scratches, but you don't know what product they were on. This is a REAL problem when trying to do root cause analysis.
23 Posts
Amanda: what are some of the data review methods you use (to analyze your measurement systems or the integrity of your data)?
