- by kq4ym, Data Doctor
- 12/4/2017 12:04:09 PM
Yes, it is going to be an interesting journey to watch as analysts try "being stewards for data ethics," while those at the top, looking for higher profits and sales, may not be so eager to follow a more correct way of doing business.
- by SethBreedlove, Data Doctor
- 11/30/2017 2:05:16 AM
@ Louis, I think there are and will be a lot of non-commericial uses of analytics, though other nations may lead the U.S. on that. The reason is that in the U.S. the ideology is that people are pretty much motivated only by money. Which is something that is false. You have beta testers who test products for free or write programs and others that do things just to be a part of something greater.
- 11/29/2017 10:44:47 PM
I agree Tom, I think the profit motive really detracts from Analytical Integrity. This is the elephant in the room that none appears to want to acknowledge or at least not very often.
- 11/29/2017 10:32:36 PM
@Seth @tomq While I appreciate and support your position of noncommercial Anayltical initiatives, I would be surprised to see this happening anytime soon. I would love to be wrong here though.
- 11/29/2017 10:27:54 PM
Thanks Seth for the information from the Analytics Guild. The issues raised here are very concerning. Companies trying to find the "sweet spot" for individual production can tread into unwelcoming waters and I don't see the necessity to do so quite frankly.
- by rbaz, Data Doctor
- 11/29/2017 5:34:32 PM
Tom, bias is definitely intertwined within the process, so recognize that it will always be and take steps to manage it as best possible. As long as human intuition remains a bar in measuring quality of what the data is showing, manipulation of the data becomes enticing.
- by SethBreedlove, Data Doctor
- 11/29/2017 9:19:08 AM
I would love to see more funding for analytics for non-commercial purposes such as studying the environment.
While I believe that analysts being moral shepherds, unfortunately, that is not always the case. Here is an example that was written by the Analytics Guild.
"With the rise of data technology, we have entered a new era of performance management and employee engagement. We can now regularly and easily measure employee engagement and track engagement over time. These engagement metrics can be used to predict when an employee becomes disengaged at work, ultimately leading to churn or poor work quality. Ideally, these metrics would be used by employers to improve the quality of the work environment, build up team cohesion, and disrupt the spiral toward disengagement.
But better work environments are expensive. What if the environment was just good enough to keep an employee from churning, but not good enough to make them feel engaged? How costly is that? Instead, it might be cheaper to use employee engagement metrics, coupled with employee retention metrics to determine where the average minimum employee engagement mark rests. Instead of producing a better outcome, companies could use data technology to produce a work environment that is "just good enough" to stay and work, but not good enough to hit satisfaction marks. The worries over employee metrics at companies like Amazon are just beginning of the race to the bottom."
- 11/29/2017 9:17:35 AM
I agree with the idea that the quality, types and modeling of data today is a far cry from just a few years ago. I also agree that it is easier to make logical decisions- the question is whether that is happeing. I run into cases all the time where people tryo to manipulate data to prove their own points and bias is still fetured in many decsions. Hopefully we are moving in the right direction and will continue to do so.