[Esip-esda] Next ESIP ESDA Telecon - August 21 (this Thursday) at 3 EST

[Kempler, Steven J. (GSFC-5860) via Esip-esda]

Hi,
Hope you are all doing well.

Reminder:  Our next ESIP ESDA Telecon will be Thursday, August 21, from 3 to 4 EST.

After our discussion at Frisco (see notes at:  http://commons.esipfed.org/node/2352), let's review the outcomes of the meeting noted.  2 prime outcomes that I saw was:

1.  We need to address providing more information about the scope of Data Analytics, for folks who are learning about this new field
2.  We need to continue to refine, and further define, what data analytics means in Earth Science

Other thoughts?

Agenda:

1.  5 min - Welcome back from Frisco

2.  20 min - Guest Speakers:  George Djorgovski, Cal Tech, who is interested  in the roles of computation in knowledge discovery.

3.  30 minutes – Discussion:  Descriptive Analytics

We have identified 5 types of Data Analytics.  Let's fully characterize each, one at a time.  Thus, at this telecon, let us  address the first type, Descriptive Analytics,.  This working discussion is intended to flush out what  exactly Descriptive Data Analytics means in Earth science  research.

Please think about the following and come to discuss:
- What Descriptive Analytics can mean in practice, and bound our 'definition'.
- Possible (or real) examples or exemplary situations
- Issues faces in implementing/utilizing Descriptive Analytics
- Potential Solutions?
- What kind of data users would utilize Descriptive Analytics

Reference:

Types of Data Analytics

Descriptive Analytics:  You can quickly understand "what happened" during a given period in the past and verify if a campaign was successful or not based on simple parameters.
Diagnostic Analytics:  If you want to go deeper into the data you have collected from users in order to understand "Why some things happened," you can use … intelligence tools to get some insights.
Discovery Analytics:  The use of data and analysis tools/models to discover information
Predictive Analytics:  If you can collect contextual data and correlate it with other user behavior datasets, as well as expand user data … you enter a whole new area where you can get real insights.
Prescriptive Analytics:  Once you get to the point where you can consistently analyze your data to predict what's going to happen, you are very close to being able to understand what you should do in order to maximize good outcomes and also prevent potentially bad outcomes. This is on the edge of innovation today, but it's attainable!

Also:  Descriptive Analytics --> hindsight

Comments always welcome.  More to come.

Looking forward to talking with you all.
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