From Newsgroup: comp.lang.cobol

On 23/05/2018 8:07 AM, Kellie Fitton wrote:

Hi Doc Trins O'Grace,

You are absolutely right -- the trouble is knowing your inputs.
I must ask my self are the data good and make sure the data used
as well as the processes that generate and organize it are of
the highest quality and fully understand them. I don't want to
spend long time and resources only to find a bug in the data.

You actually can't find a bug in the data; data just is, and it is what
it is. You an find data that falls outside expected parameters, but it's
still just data. (Marshall McLuhan had a lot to say about this when he
was formulating Information Theory; he even suggested that the "value"
of the data was proportional to its "unexpectedness"...)

You find bugs in the algorithms used to process the data.

One can still get problems even with the best data. Garbage in,
garbage out. Predictive analytics are risky by nature, they are
valid as long as the input data are also valid.

Sadly, no, they are not.

You can have "perfect data" all validated to be within the expected constraints and the predictions derived from it can still be "wrong".

Certainly, there is more probability of the results being "righter" if
the data is "good", but that probability is asymptotic and will never be "Certainty".

Hence the rules I gave you previously... :-)

Pete.
--
I used to write COBOL; now I can do anything...
--- Synchronet 3.20a-Linux NewsLink 1.114

From Newsgroup: comp.lang.cobol

In article <fmkbvdFmonU1@mid.individual.net>,
pete dashwood <dashwood@enternet.co.nz> wrote:

On 23/05/2018 8:07 AM, Kellie Fitton wrote:

[snip]

One can still get problems even with the best data. Garbage in,
garbage out. Predictive analytics are risky by nature, they are
valid as long as the input data are also valid.

Sadly, no, they are not.

Mr Dashwood, it seems that folks no longer study the Hawthorne effect.

DD
--- Synchronet 3.20a-Linux NewsLink 1.114

From Newsgroup: comp.lang.cobol

On 25/05/2018 6:12 AM, docdwarf@panix.com wrote:

In article <fmkbvdFmonU1@mid.individual.net>,
pete dashwood <dashwood@enternet.co.nz> wrote:

On 23/05/2018 8:07 AM, Kellie Fitton wrote:

[snip]

One can still get problems even with the best data. Garbage in,
garbage out. Predictive analytics are risky by nature, they are
valid as long as the input data are also valid.

Sadly, no, they are not.

Mr Dashwood, it seems that folks no longer study the Hawthorne effect.

DD

Sorry Doc, not sure of your allusion here. My position has been
consistent throughout the thread (whether it was observed or not... :-)):

"Don't trust the results of analytics."

(This could change in a few years but, for now at least, that's my
position on it.)

Pete.
--
I used to write COBOL; now I can do anything...
--- Synchronet 3.20a-Linux NewsLink 1.114

From Newsgroup: comp.lang.cobol

In article <fmp4gfF3fonU1@mid.individual.net>,
pete dashwood <dashwood@enternet.co.nz> wrote:

On 25/05/2018 6:12 AM, docdwarf@panix.com wrote:

In article <fmkbvdFmonU1@mid.individual.net>,
pete dashwood <dashwood@enternet.co.nz> wrote:

On 23/05/2018 8:07 AM, Kellie Fitton wrote:

[snip]

One can still get problems even with the best data. Garbage in,
garbage out. Predictive analytics are risky by nature, they are
valid as long as the input data are also valid.

Sadly, no, they are not.

Mr Dashwood, it seems that folks no longer study the Hawthorne effect.

Sorry Doc, not sure of your allusion here. My position has been
consistent throughout the thread (whether it was observed or not... :-)):

"Don't trust the results of analytics."

Is that an assertion based on experience? Seriously... the Hawthorne
effect is that the results you get might not be the results you seek;
having studied this might prevent someone from posting that 'predictive analytics are valid as long as the input data are valid'.

DD
--- Synchronet 3.20a-Linux NewsLink 1.114