[ESIP-all] TODAY: ESIP ESDA Telecon - Thursday, November 20 at 3 EST
Kempler, Steven J. (GSFC-5860) via ESIP-all
esip-all at lists.esipfed.org
Thu Nov 20 11:40:46 EST 2014
Hope you are well. Our next ESIP ESDA Telecon will be Thursday, November 20, from 3 to 4 EST.
* WebEx: https://esipfed.webex.com/ , 23136782
* Telecon: 1-877-668-4493, 23136782#
1. 10 min – Recap of our last telecon on Diagnostic Analytics
2. 20 minutes – Discussion: Descriptive and Predictive Analytics (and expand our current Data Analytics Type Comparisons Table. For current table see: http://wiki.esipfed.org/index.php/Earth_Science_Data_Analytics/2014-10-23_Telecon)
3. 20 minutes – Planning ahead discussion:
- Winter ESIP Meeting ESDA Planning: Sessions; Suggestions for guest speakers
- Are we starting to learn enough to write a paper on the Types of Data Analytics Utilized in (the various phases of) Earth Science
4. 10 minutes - Open Mic – Thoughts, Ideas
Please think about the following discussion points:
- What Diagnostic Analytics can mean in practice, and bound our 'definition'.
- Possible (or real) examples or exemplary situations
- Issues faces in implementing/utilizing Diagnostic Analytics
- Potential Solutions?
- What kind of data users would utilize Diagnostic Analytics
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.
Discoverative 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!
Comments always welcome. More to come.
Looking forward to talking with you all.
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