Archive for the VLAB Category

VCs Tell How They Invest (VLAB Event, January 26, ’10)

Posted in Entrepreneurship, Venture Capital, VLAB with tags , , , on January 27, 2010 by Shankar Saikia

THUNDER-LIZARDS! HEAT-SEEKING MISSLES! SEX (almost)! These were some of the topics discussed at today’s VLAB event titled “How my company leap-frogged with the help of funding partners”.

Today’s panel included the following VCs:

Howard Hartenbaum – August Capital
Josh Kopelman – First Round Capital
Mike Maples – Maples Investments
Dave Strohm – Greylock

Ravi Belani from Draper Fisher Jurvetson moderated a great session, most of which had a “hot seat” format – a few lucky attendees got to ask the panel for advice on actual VC-related scenarios.

Here are some of the nuggets from each panelist:

1. Howard Hartenbaum – August Capital

VC as partner: A relationsip with a VC is like a marriage – you will be spending a lot of time with your VCs and so get to know them well.

Beat the big : VCs like ideas that involve beating big traditional companies.

2. Josh Kopelman – First Round Capital

Don’t cross the stream together: He used an analogy from a film (Ghostbusters? ) and basically meant that during fund-raising do not tell a VC which other VCs you are talking to .. or you will get the worst possible term sheet.

Lower start-up costs/time: it takes less money to start a company and less time to test it out – both are great (e.g., $600K and 6 months now versus $6 million/6 years before).

3. Mike Maples – Maples Investments

Thunder lizards: VCs invest in companies that can take advantage of “disruptive tectonic” forces in the market and become huge – like thunder lizards (Mike’s movie choice = “Godzilla”)

Project Big: When you pitch a VC tell them how big an impact your product/service will have. For example, at Motive their opening slide said “we will cut data entry costs by $25 billion!”

4. Dave Strohm – Greylock

Alignment: VCs and entrepreneurs’ goals should be aligned and investing in common stock (without liquidation preferences etc.) is one way to align interests. He said that unfortunately most deals do not involve common stock.

Where are the women? : He asked why there weren’t more women in the VC business. Also, when Mike referred to an entrepreneur as “he”, Dave quietly admonished him and urged him to say “he or she” … But, to Mike’s credit his firm’s other partner is a woman and she sourced their best deal yet – so kudos to Mike.

Here are some of my own insights:

Grey Matter: These VCs seem soooo smart – have you checked out Ravi Belani’s profile?

Grey Hair: My own hair is greying  but I couldn’t believe how many in the crowd had more grey hair than me! My guess is that some of the grey haired folks (except for me) were angel investors. With lower capital requirements for starting tech companies (because of cloud computing, open source etc.) I think the number of VC firms is going to decline, meaning that if you want to start a company angel investors are going to be your best (and perhaps only) shot at funding.

Silicon Valley Special: The valley is truly a special place for tech innovation, especially for businesses focused on intellectual property . I doubt if any other place can compete.

My final thought: Tech is just one avenue of entrepreneurship. There are several other sectors where you can create disruptive change – health food, sports, healthcare, travel, housing …… are just a few that I can think of …

What do you think? Do you agree with the VCs or with me?

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Data Mining: Online Examples

Posted in Data mining, Online, VLAB with tags on January 21, 2010 by Shankar Saikia

Roughly three years ago, I received the following nugget of wisdom regarding entrepreneurship: do something at the “intersection of your interests, skills and where the market is going.” This came from a presenter at VLAB who gave the same advice to her son as he was heading to college and felt that the advice was appropriate for aspiring entrepreneurs as well.

With so much going on in the glamorous worlds of mobile, social networks, music etc., why would one pick something as boring, mundane, prosaic ( fill in your favorite synonym for “boring” here) _______ as data ??? For me it’s because I’m a numbers guy, I like the worlds of planning, forecasting etc. and it appears that data analysis is emerging as an area of growth – for me data mining is at the intersection of my skills, likes and where the market is going.

What is data mining? My informal definition is that data mining is the process of getting some benefit, whether economic or non-economic, from information. A simple example, courtesy of Roger Magoulus from OReilly Media,  is Amazon.com’s listing of each book’s “Amazon.com Sales Rank” – that single piece of data helps buyers make decisions.

Everyone is aware of the tremendous growth of social networks. Anyone who uses LinkedIn has probably noticed the “People You May Know” box – that’s a case of LinkedIn using information on connections between your contacts and the contacts’ contacts  – another example of data mining. If you do a search for “data mining” on a job site like Simply Hired, you may find social networking companies like Yelp and Facebook advertising for data mining experts. Today I noticed that Simple Hired itself has added a neat capability – it can show your Linkedin contacts next to a job listing – the value being that you can ask your LinkedIn contact to possibly to give you a referral – another example of using data for your benefit.

What about those “other” companies?  Can a “normal” company, not just a social networking site, also mine online data? Sure. Take a look at this chart that shows trends for Google searches for rental car companies:

Trends in Google Searches

In this case each rental car company can investigate why there were relatively more searches for Enterprise Rental Car – was it because Enterprise advertised more in a specific location? This is an example of data mining of external information (i.e., information that does not reside within the corporate technology systems). You can view the actual chart here, and even drill into specific locations (for example, there were more searches for Dollar Rental Car in Hawaii).

Hal Varian, an economist who works for Google, recently said that statistics may be a good career choice in the future: “… The ability to take data—to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it—that’s going to be a hugely important skill..”

I hope these examples gave you a better understanding of data mining as it pertains to online data. What do you think?

VLAB: Data Exhaust Alchemy Event (January 19, 2010)

Posted in Data mining, VLAB on January 19, 2010 by Shankar Saikia

DATA, DATA EVERYWHERE: COOL PROBLEMS TO WORK ON

I attended the VLAB event “Data Exhaust Alchemy: Turning the Web’s Waste Into Solid Gold” at Stanford GSB today. It was great – Bishop Auditorium was packed. Here’s a very brief summary of what I learned from each speaker:

1. Roger Magoulas, Director of Market Research, O’Reilly Media: He related the story of how Amazon.com, by simply adding the popularity ranking of books, was able to add a lot of value for customers – a great example of using the data exhaust.

2. JB (Mike John-Baptiste), CEO, PeerSet: Peerset has developed algorithms that mine web data to help advertisers target the right audience.

3. Mark Breier, General Partner, In-Q-Tel: The venture arm of the CIA has invested in the following companies: Visible Technologies, Palantir and Fortius One. There are many security-related applications of the data exhaust.

4. Jeff Hammerbacher, Vice President of Products and Chief Scientist, Cloudera: He left Facebook because he felt that he did not understand consumer technologies such as online advertising. Cloudera makes the open source version of Hadoop, which uses the Mapreduce algorithm developed by Google.

5. Dr. DJ Patil, Chief Scientist and Sr. Director of Product, LinkedIn: He preferred the word “ecosystem”  (over the phrase “data exhaust”) to describe the data created on the web. He mentioned that with every passing day there are fewer people who are not on Linkedin.

6. Pablos Holman, Futurist, Inventor, Security Expert, and Notorious Hacker: He was AWESOME. He stressed that, from a security perspective, everything that we do online and using mobile devices is in the cloud. He showed a cool demo of how our credit cards are NOT that secure.

My overall opinion is that the speakers and their respective organizations were working on some very difficult and exciting problems related to the growing volume of data. A point that Richard made was that mining the social graph (e.g., our Facebook friends and the things we do, like etc. as recorded on Facebook) is very challenging. The good news is that companies like LinkedIn have been able to extract value from its data, and added cool capabilities such as recommending people we can connect to.

Bottom line:  did the meeting give me any ideas for products or services that I can sell to my enterprise customers? ….. Yes.