A detailed report on the KDD2013 panel with 4 leading data scientists and entrepreneurs Ron Bekkerman, Oren Etzioni, Usama Fayyad, and Claudia Perlich, discussing the major issues in Big Data and Data Science startups.
One of the most interesting sessions at KDD2013 conference on Data Mining and Knowledge Discovery (Chicago, Aug 2013) was a panel
Data Scientists Guide to Making Money from Startups, with well- known entrepreneurs and Data Scientists
- Ron Bekkerman, then Chief Data Officer at Carmel Ventures Fund
- Oren Etzioni, @Etzioni, then Professor at U. Washington, Founder of several startups
- Usama Fayyad, @UsamaF, Entrepreneur, Tech/Online Investor, Exec Chairman for Oasis_500
- Claudia Perlich, @Claudia_Perlich, then Chief Scientist at Media6Degrees (now dStillery)
The panel was moderated by two top researchers: Foster Provost (NYU) and Geoff Webb (Monash U.).
Big Data Journal has just published an extensive report on this panel:
A Data Scientist’s Guide to StartUps
Abstract
In August 2013, we held a panel discussion at the KDD 2013 conference in Chicago on the subject of data science, data scientists, and startups. KDD is the premier conference on data science research and practice. The panel discussed the pros and cons for topnotch data scientists of the hot data science startup scene. In this article, we first present background on our panelists. Our four panelists have unquestionable pedigrees in data science and substantial experience with startups from multiple perspectives (founders, employees, chief scientists, venture capitalists). For the casual reader, we next present a brief summary of the experts’ opinions on eight of the issues the panel discussed. The rest of the article presents a lightly edited transcription of the entire panel discussion.
Here is a quick summary of the main issues that arose and were discussed by the panelists.
- The motivations for being involved in startups are not all about money. They include the excitement caused by the speed and unpredictability of events; the opportunity for realworld impact; the benefits of working in a small, focused team with a “cando” attitude; the rewards of being an integral component of something big, interesting, and worthwhile; the thrill of creating something big from nothing, and, of course, the potential of substantial financial reward.
- The risks are low because current demand for data scientists is so high and, no matter what happens, you will gain valuable data science experience. Also, you can negotiate remuneration to balance equity (i.e., potential longterm profit) against salary (i.e., certain current income). It is critical to negotiate a good deal when you join any company.
- The financial rewards are arguably greatest for the founders. Once a startup is reasonably established, joining it may be no more beneficial financially than joining a very established company. On the other hand, an established startup can provide many of the same nonfinancial rewards (see point 1), as well as better worklife balance.
- The greatest critical success factor is the team. A great team can make something from very little. A poor team is unlikely to succeed no matter how good the vision. The most critical member of the team is the chief executive officer (CEO). The team must be coupled with an idea that addresses some real pain or major opportunity. To get major venture capital funding, the business plan should be for a $1 billionplus business.
- If you want to assess the success prospects of an established startup, an excellent indicator is who is funding it. If it is funded by a top venturecapital firm, then you know that it has been assessed as a good bet by an informed and likely competent team.
- Now is a very challenging time to hire data scientists to startups (and elsewhere). One strategy for companies is to make yourself publicly visible as a top datascience company, as topnotch data scientists benefit considerably from working with other topnotch data scientists. When assessing potential staff, look for passion, vision, and excitement.
- It is not necessary to have a PhD in order to get a rewarding data science position and to succeed at it. A PhD definitely adds substantial value, but it is not clear that on average this is any greater than the value of 5 years of focused industry datascience experience. One of the key factors either way is the mentorship a great PhD with a great advisor is hard to beat in terms of skill set, critical thinking, and independence; these also can be developed in an industry position with a great mentor. On the other hand, you are unlikely to gain great skills if you go straight from a master’s degree to leading a data science project. Data science is a craft, and, as with most complex crafts, one learns best by working with topnotch, experienced practitioners.
- Founding a data science startup requires both topnotch technical and business leadership. If you want to be a technical founder, then it is essential to partner with a great businesssavvy cofounder.
“The greatest critical success factor is the team. A great team can make something from very little.”
Read the full paper at
online.liebertpub.com/doi/full/10.1089/big.2014.0031
Author: Gregory Piatetsky