Earth Science Data Analytics/2014-11-20 Telecon
ESDA Telecom notes – 11/20/14
ESIP Host (Erin), Steve Kempler, Chung-lin Shie, Suhung Shen, Tiffany Mathews, Ethan McMahon, Robert Downs, Brand Niemann
CONGRATULATIONS TO ERIN!
1 – Recap of our last telecon on Diagnostic Analytics
2 - Discussion: Discoveritive and Predictive Analytics
3 – 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 - Open Mic – Thoughts, Ideas
Thank you all for attending.
Next, in our movement to review the various types of Data Analytics, with the objective to clarify and specifically define, one by one, each type of data analytics, we discussed Discoveritive, Predictive, and Prescriptive Data Analytics.
As a reminder:
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.
Discoveritive 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!
The following Discoveritive Data Analytics definitions were offered:
- Tell me something that I don't know" is the definition of data mining - discovering unexpected patterns and relationships in data. (http://online-behavior.com/emetrics/data-discovery-1073) - Four types of discovery analytics: visual discovery, data discovery, information discovery and event discovery (http://www.information-management.com/blogs/3-major-trends-in-new-discovery-analytics-10024769-1.html)
The following Predictive Data Analytics definitions were offered:
- Encompasses a variety of statistical techniques from modeling, machine learning, and data mining that analyze current and historical facts to make predictions about future, or otherwise unknown, events
- Combines techniques from statistics, data mining and machine learning to find meaning from large amounts of data…and predict where you’re going. Predictive analytics is the practice of extracting information from existing data sets in order to determine patterns and predict future outcomes and trends. - Predictive analytics does not tell you what will happen in the future. It forecasts what might happen in the future with an acceptable level of reliability, and includes what-if scenarios and risk assessment (http://www.webopedia.com/TERM/P/predictive_analytics.html)
- Predictive analytics is the branch of data mining concerned with the prediction of future probabilities and trends. (http://searchcrm.techtarget.com/definition/predictive-analytics)
- While regression analysis is commonly used, there exists another class of methods that deserve proper mentions. E.g. Bayes Network, Artificial Neural Net, Decision Tree, Support Vector Machine, etc. More importantly, the non-linear analysis aspect and the probability based approach that underpin many of the aforementioned methods.
Bonus: The following Prescriptive Data Analytics definitions were offered:
- Prescriptive analytics goes beyond descriptive and predictive models by recommending one or more courses of action and showing the likely outcome of each decision
- Prescriptive analytics goes beyond predicting future outcomes by also suggesting actions to benefit from the predictions and showing the decision maker the implications of each decision option.
Providing examples, use cases, and additional understanding is highly encouraged.
Please contact Steve
We next talked about our two sessions at the Federation Meeting, in January
1 - Earth Science Data Analytics 101:
Purpose: To ‘educate’ ESIP community on what Earth Science Data Analytics means, and provide exemplary use cases.
Cluster Goal: Bring in speakers to provide their Data Analytics Use Cases to stir innovation juices that can generate ideas/techniques/collaborations/etc. that can facilitate/aid usage of data analytics
- Introduction to Earth science data analytics – (15 min)
- 3 or 4 use case speakers (10-15 min each) I have 2 already…any suggestions
- Current Data Analytics technologies useful in Earth science (15 min)
- Panel – Q&A (all speakers)
Excellent suggestions were made to ensure speakers targeted our interest in learning how they perform data analytics in their research.
2 - Earth Science Data Analytics 201:
Purpose: To scope a study that would meaningfully benefit the ESIP and broad community; Develop an outline for the study
Cluster Goal: Discuss: Publish our findings; Generate a library of Data Analytic methodologies
Discussion and work breakout. This is where we will further discuss and develop a more detailed outline for a paper that describes Earth science data analytics methodologies. It seems the bulk of our research at this time would be gathering and characterizing use cases. This lead to the possibility of creating an Earth science data analytics library of such methodologies.
More to come. Be a part of some ground breaking work
The following was provided to initiate discussion of such a paper:
1. Take what we learn, refine, and define about the different types of Data Analytics
- Descriptive Analytics - Diagnostic Analytics - Discoveritive Analytics - Predictive Analytics - Prescriptive Analytics
2. Associate exemplary Earth science use cases to each type
3. Associate Data Analytics techniques/tools to each type
4. Associate user categories to each type
5. Describe skills and expertise needed for each type
- Currently, we talk about our expertise and experience, but they seldom seem to connect to each other
- This will help us, the industry, and hopefully, educators, focus their understanding and interests regarding Earth Science Data Analytics.
REMINDER: AGU sessions that pertain to Data Analytics:
- Teaching Science Data Analytics Skills Needed to Facilitate Heterogeneous Data/Information Research: The Future Is Here - Session ID#: 1879
- Identifying and Better Understanding Data Science Activities, Experiences, Challenges, and Gaps Areas - Session ID#: 1809
- Advancing Analytics using Big Data Climate Information System - Session ID#: 3022
- Big Data in the Geosciences: New Analytics Methods and Parallel Algorithm - Session ID#: 3292
- Leveraging Enabling Technologies and Architectures to enable Data Intensive Science - Session ID#: 3041
- Open source solutions for analyzing big earth observation data - Session ID#: 3080
- Technology Trends for Big Science Data Management - Session ID#: 2525
- No telecon. Face to face January 7 at the Federation Meeting in Washington
- Agenda: See planned sessions above