[User-committee] User Survey Data Analysis
Shamail
itzshamail at gmail.com
Wed Nov 18 13:14:40 UTC 2015
Hi Lauren,
> On Nov 18, 2015, at 5:58 PM, Lauren Sell <lauren at openstack.org> wrote:
>
> I doubt anyone would argue against more data analysis :)
>
> I’d like to ask the independent analyst (who helped produce the report) to dig into production stage deployments with low NPS scores and see if there are any other correlations.
>
> Jon and I also threw out a few ideas on the list - release version, cloud size, projects used - are there any other factors you are specifically interested in analyzing?
Throwing a few other categories to spark discussion: [Industry/organization type], [workloads], [consumption model (DIY, distribution partner, CSP, etc.)], and size of organization.
Any thoughts on how many dimensions we should analyze in this round... can we choose 3-5 or would that be too many?
>
> Cheers,
> Lauren
>
>
>> On Nov 17, 2015, at 2:50 PM, Roland Chan <roland at aptira.com> wrote:
>>
>> That's exactly the sort of analysis required.
>>
>> Distribution of the data is only needed if the people who currently have access to it are unable to perform the analysis for some reason.
>>
>> First though, the committee must resolve that the analysis is needed. Once that is done we can investigate what needs to happen.
>>
>> Do we have agreement on whether further analysis is warranted?
>>
>> Roland
>>
>>
>>> On Wed, 18 Nov 2015 02:28 Jonathan Proulx <jon at csail.mit.edu> wrote:
>>>
>>> Perhaps it would be valuable to take a couple more cuts of the
>>> deployment data (size, release, projects used etc) by promoters and
>>> detractors?
>>>
>>> That might be informative without requiring further distribution of
>>> raw data. Are there any other specific comparisions people think
>>> might be useful?
>>>
>>> Remembering that this is a vouluntary survey mostly announced through
>>> chennels frequented by people involved in the community to some extent
>>> I'd expect to see a relatively strong positive bias in results, so if
>>> anyone has actionable ideas on how to get deeeper I'm for it, but I'm
>>> also not sure I put too much importance on that metric by itself for
>>> reasons others have mentioned.
>>>
>>> -Jon
>>>
>>> --
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