Exploring Data | Inspiring Innovation
Shenzhen | April 29, 2016
The Design of JMP®
John Sall, Co-Founder and Executive Vice President, SAS
JMP was created in response to various forces, situations and opportunities. It evolved to address needs in unique ways. It got very good in certain areas. This is our story of how it all came together over the last 27 years from release 1 to release 12. What were we thinking?
Data Analysis in High-Tech Product Research and Development – Reliability Assessment of Sub-Healthy Products and Systems
Feng-Bin Sun, Senior Staff of Reliability Engineering, Tesla
We are entering an era of big data. Data-driven innovation has enormous economic value, with Big Data product and service sales exceeding $18 billion in 2013 and expected to reach $50 billion by 2017. This presentation will start with an overview of the data analysis trends in many leading innovation companies, especially in product design, development, qualification, production, and maintenance. As a special example of new trends in data analysis, the authors look at the product reliability quantification under sub-healthy conditions – a fractional failure-based reliability assessment methodology. In real-world practice, the sub-healthy condition (fractional failures) can be encountered when:
- Performance degradation has crossed the pre-specified threshold but hasn’t yet manifested as a macro failure (when it ceases to function physically).
- The corrective actions are partially effective (greater than 0 percent, but less than 100 percent).
- The failure analysis cannot duplicate the field failure symptom due to failure diagnosis limitation, etc.
Examples are given using JMP software to illustrate data collection, failure classification and fractional failure determination, data entry format, life distribution parameter estimation, reliability quantification, and field risk prediction. It is believed that this talk will be beneficial to a wide audience, including reliability practitioners, theorists and management.
Data Science is Sexy, but Numbersense is Priceless
Kaiser Fung, Author, Numbersense and Numbers Rule Your World
Analyzing data is like running an obstacle course. The path is laid with trapdoors, dead ends and diversions. The best analysts count on a keen sense of direction as they navigate data. This numbersense is the priceless asset in data science. I will describe some recent analyses that went off track, and discuss how to prevent falling into such traps. Troubleshooting using statistical know-how is the easy part; sensing fault lines and piecing together root causes prove to be much more challenging. Examples will be drawn from diverse fields, such as predictive analytics, A/B testing, advertising technology, Web scraping and Open Data projects.