Great data leads to great insights which leads to great products.
Vitaly Gordon, senior products data scientist, talks about the culture, people and tools that have helped LinkedIn become the world’s leading professional social network and one of the most visited sites on the web.
Please take my words with a grain of salt. No everything you will learn today, you should run to implement and not everything you do that doesn’t appear here is wrong. I’ve just came to tell you about some of the stuff I found interesting that made LinkedIn successful in the development of data products
What are data products (in the context of this talk) – something that involves algorithms and some consumer web facade. For example dashboards and visualization are not because they don’t have algorithms and HFT algorithms and missile guidance systems don’t have this consumer web façade
Metrics - Explain the shift in metrics as well, from eyeballs (bottom page counters) to segmented eye balls (e.g. Google analytics) to web funnels (i.e. looking at more metrics than just views) to multi dimensional engagement (don’t know if it is that different than the previous).Timeline – Explain the involvement of data products from Amazon’s “People who viewed this …” through Google Ads (don’t know if falls under the previous definition through PYMK to Endorsements. Also explain that data products are not just cool, but they are very valuable to your business
Metrics - Explain the shift in metrics as well, from eyeballs (bottom page counters) to segmented eye balls (e.g. Google analytics) to web funnels (i.e. looking at more metrics than just views) to multi dimensional engagement (don’t know if it is that different than the previous).Timeline – Explain the involvement of data products from Amazon’s “People who viewed this …” through Google Ads (don’t know if falls under the previous definition through PYMK to Endorsements. Also explain that data products are not just cool, but they are very valuable to your business
The power of 3
Almost every element on the LinkedIn pages is a data productEverything should be data driven – from the idea conception, through iteration until the successFocus is really important – The attention span of your customers is limited, for every feature you add, think what you need to remove
Everything should be data driven – from the idea conception, through iteration until the success
Everything should be data driven – from the idea conception, through iteration until the success
If you could only do one thing, what would it be? -- Steve JobsShortly after Jerry Yang became the CEO of Yahoo, he invited Steve Jobs to address the company's leadership. Among many insightful things that Steve shared that day, the one that continues to have the most profound influence on me was his discussion regarding prioritization. Jobs said that after he returned to Apple in 1994, he recognized there were far too many products and SKUs in development so he asked his team one simple question: If you could only do one thing, what would it be? He said that many of the answers rationalized the need to do more than one thing, or sought to substantiate bundling one priority with another. However, all he wanted to know was what "the one thing" was. As he explained it, if they got that one thing right, they could then move on the next thing, and the next thing after that, and so on. Turned out the answer to his question was the reinvention of the iMac. After that, it was the iPod, the iPhone, and the iPad, and the rest, as they say, is history.Interestingly enough, years later I heard Jobs speak at All Things D and he explained that the company had actually been working on the iPad before the iPhone, as he had long written off pursuit of the phone as being prohibitively challenging given the carrier landscape. However, once a window of opportunity opened up to successfully bring a phone to market, he hit the pause button on the tablet, and only returned to it once Apple got the iPhone right. Pretty mind blowing to think that a company as large and successful as Apple, and someone as prodigiously talented as Steve Jobs, would temporarily shelve something as important as the iPad for the sake of focus, but that's exactly what he did.
The power of 3
Really, it is. Give examples that show that this stuff is taken seriously (like Jeff’s all hands and Jim’s getting sent back to do his homework for not putting it first on his roadmap)Famous Conway’s diagram. Explain the day to day a data scientist at LinkedIn and why is it important to have those skills. Give some examples of people backgroundsHow to hire good data scientists – by using real examples you both test for truly needed skills and do some selling while interviewing, maybe give examples of interview questions
Really, it is. Give examples that show that this stuff is taken seriously (like Jeff’s all hands)
Joe Adler – Author of R in a NutshellGloria Lau – Associate professor at StanfordMonica Rogati - Wall Street Journal & The Economist to NPR & CNN to Real Simple & (yes!) Howard Stern.Daniel Tunkelang – Chief Scientist of Endeca that was sold to Oracle for > $1BDaria Sorokina – Creator of additive groves and competitor for the national heritage health prizeMatheiu Bastian – Co-founder and technical lead at GephiTalk about the day to day work of those people
The power of 3
Most companies don’t have an exact replica of their production cluster in their development environment, explain why is it crucialInfrastructure increases the productivity and let’s data scientists to focus more on actual data scienceMaybe refers more to culture, but people are really curious why LinkedIn open sources so much. Explain the benefits in hiring and retention and mention few of those projects
Developing data products without real data is like learning swimming from a book
Infrastructure increases the productivity and let’s data scientists to focus more on actual data science
BDFL – Benevolent Dictator for LifeBenevolent - נדיב
I don’t know if those are the main 3 takeaways from the talk