Playing Nice in the Product Playground #StrataHadoop
Intuit's Anu Tewary, Lucian Lita and Jonathan Goldman talk about how data scientists, engineers and product managers can work together to create innovative data products at O'Reilly Strata +Hadoop World 2015. Bios: Anuranjita Tewary is Director of Product Management at Intuit. She was a founder at Level Up Analytics, which was acquired by Intuit. Her previous roles have been data scientist at LinkedIn, and product management at AdMob and Microsoft. Anu is the founder of The Technovation Challenge, a global programming and entrepreneurship program for girls. The program is in its fifth year and has had over 3,500 participants from over 40 different countries. Anu holds a PhD in Applied Physics from Stanford and BS degrees in Physics and Math with Computer Science from MIT. Lucian Lita is Director of data engineering at Intuit. Previously founder of Level Up Analytics (now Intuit), lead engineering, analytics at BlueKai (now Oracle), data scientist at Siemens healthcare. Received his PhD in computer science from Carnegie Mellon. Jonathan Goldman is Director of Data Science and Analytics at Intuit. He co-founded Level Up Analytics, a premier data science consulting company focused on data science, big data, and analytics which Intuit acquired in 2013. From 2006–2009 he led the product analytics team at LinkedIn which was responsible for creating new data driven products. While at LinkedIn he invented the People You May Know product and algorithm which was directly responsible for getting millions of users connected and more engaged with LinkedIn. He received a PhD in physics in 2005 from Stanford where he worked on quantum computing and a BS in physics from MIT. To view the presentation, visit: http://youtu.be/kkTXGtHrWAw
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Playing Nice in the Product Playground #StrataHadoop
- 1. 1#AJLPlayNice Playing Nice in the Product Playground data scientists, engineers, and product managers working together to create innovative data products Jonathan Goldman @jrgoldman Anu Tewary @anutewary Lucian Lita @datariver Intuit Data Engineering & Analytics
- 3. 3#AJLPlayNice product vision business impact success measures effective architecture scalability & robustness metrics & monitoring not sure what this product does, but look at the 2% lift I can get from this model... ooh, ooh, a Dirichlet prior is what this needs!! is this good for an ICML or KDD paper? [ 1 ]
- 4. 4#AJLPlayNice product vision business impact success measures effective architecture scalability & robustness metrics & monitoring not sure what this product does, but look at the 2% lift I can get from this model... ooh, ooh, a Dirichlet prior is what this needs!! is this good for an ICML or KDD paper? Data scientists navel gazing in a corner?! [ 1 ]
- 5. 5#AJLPlayNice product vision business impact success measures rapid experimentation simple models first right metrics let’s write a new streaming framework for the weekly dashboard! we’re not meeting our SLAs, let’s write a faster json parser! let’s write an optimized distributed graph database for our data scientist. [ 2 ]
- 6. 6#AJLPlayNice product vision business impact success measures rapid experimentation simple models first right metrics let’s write a new streaming framework for the weekly dashboard! let’s write a faster json parser in Clojure! silver bullet: graph database, fp, lambda arch [ 2 ] Engineers reinventing the tech wheel?!
- 7. 7#AJLPlayNice rapid experimentation simple models first right metrics forget A/B testing, my gut tells me this is the way to go... revenue impact? Who cares! Build it anyway! no time to instrument! Let’s go to market and we’ll do that later - I’m sure that the numbers will look good! [ 3 ] effective architecture scalability & robustness metrics & monitoring
- 8. 8#AJLPlayNice rapid experimentation simple models first right metrics forget A/B testing, my gut tells me this is the way to go... revenue impact? Who cares! Build it anyway! no time to instrument! Let’s go to market and we’ll do that later - I’m sure that the numbers will look good! [ 3 ] effective architecture scalability & robustness metrics & monitoring Product in a bubble?!
- 10. 10#AJLPlayNice 3 Three Steps to Risa** 21 ** Risa is to Nirvana as Spark is to Hadoop
- 11. 11#AJLPlayNice 321 Build an Awesome Team
- 14. 14#AJLPlayNice find the right mix minimum prod ds eng good good good target great good good prod ds eng great good good prod ds eng great good great prod ds eng
- 15. 15#AJLPlayNice form pods around product personalization & reco pod real time data capture & stream proc. pod business search pod real time commerce graph pod
- 17. 17#AJLPlayNice 321 Solve a Big Problem
- 18. 18#AJLPlayNice Solve a big problemidentify big problem
- 21. 21#AJLPlayNice 321 Get Out of the Way
- 22. 22#AJLPlayNice time trust the team to become experts
- 23. 23#AJLPlayNice anyone can represent the team
- 24. 24#AJLPlayNice your role as a coach?
- 26. 26#AJLPlayNice Three Steps to Risa** 3 2 1 awesome team (pods) solve a big problem (pods) get out of the way (pods) ** Risa is to Nirvana as Spark is to Hadoop
- 27. 27#AJLPlayNice Most companies are not there yet
- 28. 28#AJLPlayNice Example 1: Multinational banking and financial services company Took a “technology first” approach: wanted to build a hadoop cluster, because they had heard they should No product vision, but tremendous (!) possibilities Not connected closely with business needs No data science build an awesome team solve a big problem engage prod ds eng good tinytiny
- 29. 29#AJLPlayNice Example 2: Large media company Excellent engineering team Good product team, but not data driven Good metrics and beginning data science. Did not iterate quickly; data and product were too decoupled build an awesome team solve a big problem engage ? prod ds engamazing tiny good
- 30. 30#AJLPlayNice Example 3: Large advertising firm Data-driven product team, but limited vision Engineering team not product focused. Could not iterate quickly Non-existent data science build an awesome team solve a big problem engage good tiny ok prod ds eng
- 31. 31#AJLPlayNice Example 4: Attempt at Introspection An awesome team with data, product and engineering working together Solving hard problems – for individuals and small businesses Good metrics in place, but not there yet – more work to be 100% eyes on, hands off. build an awesome team solve a big problem engage
- 32. 32#AJLPlayNice 3 2 1 awesome team (pods) solve a big problem (pods) get out of the way (pods)
- 33. 33#AJLPlayNice Jonathan Goldman @jrgoldman Anu Tewary @anutewary Lucian Lita @datariver
- 34. 34#AJLPlayNice Jonathan Goldman @jrgoldman Anu Tewary @anutewary Lucian Lita @datariver
- 35. 35#AJLPlayNice Jonathan Goldman @jrgoldman Anu Tewary @anutewary Lucian Lita @datariver