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DataDrivenNYC: #OpenSource business models, #databases, man-machine collaboration, text markup

Posted on March 16th, 2016

#DataDrivenNYC

03/18/2016 @AXA Equitable Center, 787 7th ave, NY

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The two speakers and two interviewees were

Eric Colson @StitchFix (provides fashion to you, but you don’t pick anything) spoke about their hybrid system of selecting fashion for customers. In their business

  1. You create a profile
  2. You get five hand-picked items
  3. You keep what you like, send back the rest

This means that they need to get it right first. Computers do preference modeling better than humans and humans can better understand the idiosyncrasies of other humans, so StitchFix uses both

They first send the customer’s request for clothes to a recommender system and give the output of the recommender system to a human to provide a check and customize the offering.

Next Matt Turck interviewed Eliot Horowitz @MongoDB. Mongo was started in 2007 as the founders struggled with the database needs for their new venture. Eventually they decided that the db was the most interesting part of the platform so they make it open source to friend, friends of friends, etc.

It was initially implemented using a simple storage engine. But, as the user base grew they needed a more efficient storage engine. So in Summer 2014 they acquired WireTiger – (developers formerly from Berkeley DB) to create the new storage engine. They released it in March 2015 as version3.0,

In Dec 2015, they released version 3.2 which added encryption, an improved management deployment tool and a join operator.

In Dec 2015 they also started to sell packages such as a BI connector (e.g. connect to Tableau) and Compass (graphical viewer of the data structure).

In 2016 they will add a graph algorithm in version 3.4.

Eliot then talked ways to monetize open source

  1. Consulting and support
  2. Tools – e.g. BI connection or Compass – developers not interested, but business are
  3. Cloud services – manage DB, backups, upgrades, etc.

He also mentioned that the open source matters (they release using the AGPL license), so Amazon can’t resell as a supported service.

He said that their priorities are

  1. Make current users successful
  2. Make it easy for people migrate to Mongo
  3. Develop products for the cloud, which the future of databases

Next, Kieran Snyder @textio talked about their product helps users create better recruiting notices. She noted that the effectiveness of staff searches often depends on the effectiveness of emails and broadcast job descriptions. Their tool highlights words that either help or hurt the description.

They tagged 15 million job lists according to how many people applied, how long did it take to fill, what kinds of people applied, demographic mix, etc. They looked for patterns in the text and developed a scoring system within a word processor that evaluates the text and marks it up

  1. Score 0-100. In this market with a similar job title, how fast will this role be filled
  2. Green = phrases that drives success up
  3. Red = drives success down – e.g. term over used
  4. Look at structural – e.g. formatting (e.g. best if 1/3 of content is bulleted). E.g. percent of “you” vs “we” language.
  5. Model gender tone – blue vs purple highlighting
  6. Look for boilerplate

Textio has also been used to detect gender bias in other texts.

The best feedback is from their current clients.

They retrain the model every week since language evolves over time: phrases like ‘big data’ may be positive initially and eventually become negative as everyone uses the term.

Lastly, Matt Turck interviewed Peter Fenton @Benchmark, a west coast venture firm that has backed many winners.

In a wide ranging interview, Peter made many points including

  1. He looks for the entrepreneur to have a “dream about the possible world”
  2. This requires the investor to naively suppress the many reasons why it won’t work
  3. He looks for certain personality attributes in the entrepreneur
  4. “Deeply authentic” with “reckless passion”
  5. Charismatic with an idea that destroys the linear mindset
  6. In the product he looks for
  7. Identify attributes that are radically differentiated
  8. Product fits the market and the founder fits the market
  9. Is the structure conducive to radical growth
  10. Open source as an investment has two business models
  11. Packaging model – e.g. RedHat
  12. Open core – needs
    1. Product value
    2. A big enough market to get to gain platform status. If you want mass adoption, go as long as possible without considering monetization.
  13. Business models
  14. What is the sequence in your business – do you need mass adoption first
  15. Once mass adoption is achieved you need to protect it.
  16. The biggest issue is how to deal with Amazon, who can take open source and offer it on their platform
  17. Need to license in the right way.
  18. Amazon just needs to be good enough
  19. To succeed in open source you need a direct relationship with developers to protect your business model
  20. Azure has some advantages
  21. New Nvidea GPUs so they have a performance advantage
  22. Machine learning + big data = can create a new experience, but
    1. In the NBA there is no longer an advantage in the analytics – since everyone hires the same people. Data is the advantage.
    2. If you have a unique pool of information, you can add analytics.
  23. Capital is harder to get now.
    1. One of the main issues is that some business are so well capitalized that they currently don’t need to consider their burn rate. This mispricing forces all competitors into the same model of hypergrowth at the expense of sustainable growth and infrastructure improvement.
    2. Unlike 1999-2000, the bubble is driven by institutional money. Therefore it will take longer for the bubble to burst since institutions can hide the losses for longer (they do not mark investments to the equity market prices).
    3. When valuations eventually drop, many private companies will not go public, but will be acquired by larger companies.
  24. It takes years for an opportunity to be exploited: it took 5 years to go from a camera phone to Instagram. It took years to go from GPS to Uber. It is unclear whether blockchain is in gestation and what it will give rise to (bitcoin is the best application now)

 

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