5 horror stories of bad data
From widely varying approval scores to inflated ‘impressions,’ we look at realm of lies, damn lies, and statistics. So, shed that prom dress and start mining relevant numbers.
Warning: I’m getting up on my Bad Data soap box here. Still, all the soap in the world is not going to clean up the gigantic pile of dirty data that has been landing on my desktop in the past few weeks. Each day has brought another round of, “You have got to be freakin’ kidding me!”
Yes, a certain amount of dealing with bad data is not that unusual around here. One of my jobs as “Measurement Sherpa” for my clients is to make sure that the data on which they are basing decisions is valid, accurate, and reliable. So we spend a lot of time digging in the dirt that is modern data.
Here are a few horror stories that you might recognize from your own quest for reliable, relevant data:
1. Four vendors, four very different sets of data
For one client we are monitoring four vendors to see which one has the most accurate sentiment analysis tool. My conclusion: Who the hell knows?
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