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Data & Analytics Applied to Test Strategy

Data & Analytics Applied to Test Strategy. Brad Waggle. Board Test Workshop. September 9th, 2014. Here in Texas, everything’s big, so we just call it data. Michael Dell … http://www.dell.com/learn/us/en/19/power/ps2q14-20140230-michael. Agenda. Context Data usage behavior shift

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Data & Analytics Applied to Test Strategy

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  1. Data & AnalyticsApplied to Test Strategy Brad Waggle Board Test Workshop September 9th, 2014

  2. Here in Texas, everything’s big, so we just call it data. Michael Dell … http://www.dell.com/learn/us/en/19/power/ps2q14-20140230-michael

  3. Agenda • Context • Data usage behavior shift • Facilitating the shift • Technology Stack • What can we do now that we couldn’t do before? • What can’t we do yet but are working on?

  4. Context • Test / Supply Chain Complexity • 1000s of testers, 10’s-of-thousands of product IDs, 100’s of thousands of component part numbers • Billions and Billions of rows of data (over years and years) • Test and Repair • 6.1B • Measurement • 45.5B • Component • 65.8B • And GROWING FAST!

  5. Data usage behavior shift • In the past • Small product sample • Short time frame • Solve point problems • Lack of learning • Going Forward • All products (n = all) • All time frames • Solve strategic problems • Learn and prevent problems

  6. Facilitating the shift • Migrating from: • Excel based aggregation / manipulation • Limited custom web pages • IT driven support model • Traditional database architecture • To: • Best in class front-end analytics (Spotfire) • User driven support model / self-service • Big data architecture Test Repair Qualification Field Product Cost Component

  7. Facilitating the shift • Migrating from: • Excel based aggregation / manipulation • Limited custom web pages • IT driven support model • Traditional database architecture • To: • Best in class front-end analytics (Spotfire) • User driven support model / self-service • Big data architecture Test Repair Qualification Field Product Cost Component

  8. Technology Stack • Big Data • Cloudera Hadoop • Hive / Impala • Traditional database • MSSQL • Spotfire Analytics • R Statistical Programming (TERR)

  9. What can we do that we couldn’t do before?

  10. Use big data and analytics to: • Detect technology & quality trends • Consistently apply strategies to products in the NPI Phase • Apply statistical models at scale

  11. Detect Technology & Quality Trends Drove a major shift in test strategy Multi-year view of a design / component margin Trend for 1 electrical test parameter (65 products, 6 years, 100M measurements)

  12. Consistently apply test strategies to a new product Drives the most cost effective test solution up front in the NPI cycle Launch Lean vs. launch and optimize Test strategy dashboard that facilitates choosing the best test optimization strategy for a new product

  13. Apply statistical models / algorithms at scale Determine component variability vs. design margin with higher confidence Reduce non-value-add tests Cpk analysis to determine critical measurement parameters

  14. What can’t we do yet but are working on?

  15. Use big data and analytics to: • Leverage unstructured / semi-structured data • Perform predictive modeling

  16. Leverage Unstructured, Semi-Structured Data Enhance ICT test log

  17. Predictive Modeling Mock up of linear regression attempting to correlate component types to overall product failure rates

  18. Spatial Analytics

  19. Questions?

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