Strategyzer and the Kauffman Foundation have created a great series of short YouTube videos about using the Business Model Canvas (about which I wrote an intro in Four Reasons for a Business Model).
The example presented is for a consumer oriented business, but this tool is incredibly powerful for all kinds of endeavors – new companies, new ideas in old companies, non-profits and more.
Here are the links to the six-episode series – each only 3 or 4 minutes.
There is a verse in the Bible which includes the exhortation “I call heaven and earth as witness today, that I have set before you life and death, blessing and curse; therefore choose life!”
I have always loved that verse because of the very active emphasis on the word choose. The command is to choose, not to live, or live well, but to choose.
The reality of our lives lies between belief that we have some control over our world, and fear that we are at the mercy of random chance. What we always have, however, is choice about how to hold, frame, and behave in a world where control and random chance play against each other like streams of water carrying a leaf.
I recently came across this amazing commencement address by David Foster Wallace from 2005. I recommend listening to it – it captures these themes so perfectly – and it is worth taking the 20 minutes of uninterrupted time to do so.
(Link here for those for whom the embed doesn’t show.)
Since this was triggered by me quoting a religious text, I suggest, along the lines of Alain de Botton’s “Atheism 2.0” TED Talk, that you listen to Wallace’s “This is water” every year, perhaps when Deuteronomy 30:19 is being read in the annual cycle of readings in Synagogue.
Seven years ago yesterday, on July 3rd, 2006, I bought my new bike and started cycling (and blogging).
It doesn’t seem “just like yesterday”… it really does feel like a few years since I first bought my bike. So far, according to my Garmin GPS I have ridden almost exactly 8000 miles (7998.95 to be exact). I have loved every mile (well, most miles).
People have been complaining about the pace of modern life for as far back as you want to look. My recommendation is to go out cycling with friends. This picture was from our 5th annual Martha’s Vineyard ride from a few weeks ago.
PS: Happy 4th of July to my fellow Americans.
Doug Laney is the original creator of the 3V’s definition of Big Data – referring to volume, velocity or variety of data that is hard to handle with traditional data management tools and techniques. In August last year I proposed a better definition of Big Data as Data growing faster than Moore’s Law. Many others have talked about extending the 3V’s definition of big data, and one of the additions is to insist on a fourth V: “Value”. In my personal view this is somewhere between irrelevant and dangerous. Any data may or may not have value, and that value is highly context sensitive. If you want to know the weather tomorrow, then knowing the stock market closing price from 1897 is of no value. The beauty of big data is that while most of it may be irrelevant, the patterns that can emerge are of real interest and value. Furthermore, the value of big data may not become clear until long after it is created (only once we had collected uncountable tweets from the early years of Twitter did someone realize you might find information relevant to stock prices buried in the stream of “valueless” data).
D. Robinson posted a great article in December called Big Data- The 4 V's - The Simple Truth; Part 4 - Making Data Meaningful. This talks about the need for Veracity (is the data reliably recording what is going on) and the problems of Variability (where a system may record different values for the same physical activity on different occasions). However, even these are not defining characteristics of big data, but are interesting attributes of any data collections.
Instead, let me offer some other extensions to the 3V’s definition. You don’t need all of these to have big data, but the more you have, the more likely it is you are dealing with big data.
Eric Paley of Founder Collective wrote a great blog post earlier this month: "Bonuses are toxic at startups” (which was later republished on CNN Money).
In my years working in VC (the first time I have used this phrase), I found myself in complex situations about bonuses with the leadership of several startups, but had not stepped back to think about the underlying patterns. I agree wholeheartedly with Eric’s analysis.
As many of my friends know, I recently joined the team at Optum Labs as Chief Operating Officer, located in Cambridge MA, working for the newly appointed CEO, Dr. Paul Bleicher. Also as many of you know, I was fortunate enough to be a co-founder with Paul at Phase Forward. Having worked together before, I was particularly pleased when he asked me to consider this opportunity.
Years ago, when I first joined Sigma Partners venture capital, everyone (including me) talked wryly about me “going over to the dark side.” (This is an industry wide joke; look at Mark Suster’s “about me” box on his blog, and what Andrew Manoske wrote in his article “Joining the Dark Side: Why I left engineering to become a VC.”) Even if Mark Suster and Andrew Manoske are still on the dark side, I am back on the operating side of the world – by corollary, back working for the Jedi knights, and remembering how to build my own light sabre.
I have not until now written publicly about my recent efforts to raise a micro-VC fund, which I had called Big Data Boston Ventures (BDBV). That was because the SEC has not yet published new regulations (required by the JOBS Act) to allow for a more relaxed approach to marketing for VC funds. While I was raising a fund I couldn’t talk about it – now I have stopped that effort, I can.
Big Data Boston Ventures was conceived as a micro-VC fund that would invest in seed stage companies which fit into the big data theme. I have been writing about big data in this blog for a while, and the idea that I am bullish on this should surprise no-one.
Many of my friends in the entrepreneurial and investing community have said (at least to me) that my fund concept made lots of sense, that it was needed, that it was the right theme, that I would be a sought after investor/mentor (and hence get the opportunity to participate in great deals), and that, therefore, the fund would be successful. The LP community (those who invest in VC funds) are caught in a series of dynamics that mean they are just not interested in taking the kind of risks that such a fund might offer. Many GPs (VC general partners) with a better track record, and a more conventional (and in that way stronger) pitch story than mine have been finding it tough to raise new funds. This was all the more so for me as a solo GP going out with a first fund.
I had not actually given up on my fundraising when this new opportunity presented. I was making very slow progress, but progress nonetheless, and had certainly planned for another few months to try and get launched. However, when Paul called about Optum Labs, I was immediately intrigued, and quickly became very enthusiastic.
People know I am a very interested observer of healthcare and life sciences (HC/LS), and have made a point of staying current on trends in the industry and related IT. Ever since the inception of BDBV, people have suggested I focus on healthcare and life science (HC/LS) inside the big data theme, or even consider a pure HC/LS IT fund. My consistent response was that I believe that while there are great HC/LS startups that could fit into a fund with a broader big data theme, there just are not enough to justify a narrow industry-focused seed stage fund. This answer provides the background for my response when Paul called. Optum Labs provide the opportunity and challenge for me to work at the center of innovation in HC/LS. Optum Labs is a small startup inside a large, well respected organization partnered with leading players in the industry.
I plan to stay in touch with early stage startups in big data, and especially in healthcare and life sciences. I hope to continue to do some mentoring and be involved in other ways in the start-up community. Optum Labs is based in Kendall Square (One Main St) so I will still be close to the action.
In an article on the website of Tools Journal, about the HP Moonshot line of servers, a typo has the name of the HP CEO written as Meh Whitman (meh defined here). I am jaded about HP, but am I that jaded? Here is the screen shot in case they fix it (they will!).
It is spring, just about, in New England. There are daffodils blooming in our yard, and I have been riding each Sunday since the beginning of April. The last few weeks have seen some major professional changes (more on that in a future post), and I have found riding on Sundays to be a great antidote to any stress that might have been creeping in.
Since spring brings frogs in the wetlands, I thought I would share something sent to me by Dean Landsman. Call this one the first geek joke of spring.
An engineer was crossing a road one day, when a frog called out to him and said, "If you kiss me, I'll turn into a beautiful princess."
He bent over, picked up the frog and put it in his pocket.
The frog then cried out, "If you kiss me and turn me back into a princess, I'll stay with you for one week and do ANYTHING you want."
Again, the engineer took the frog out, smiled at it and put it back into his pocket.
Finally, the frog asked, "What is the matter? I've told you I'm a beautiful princess and that I'll stay with you for one week and do anything you want. Why won't you kiss me?"
The engineer said, "Look, I'm an engineer. I don't have time for a girlfriend, but a talking frog, now that's cool."
However, that pales in comparison to the amount of data that will soon be generated by things – sensors, machines, monitors, switches and so on. Scroll down the infographic (from 2011) below, from CISCO, to see the immensity of big data that will soon be flowing from the Internet of Things.
As you can see, when farmers attach health monitors to cattle, each one sends 200MB of data per year. Apparently the total number of cattle in the US was 89.3 million. If every head of cattle had such a monitor that would result in over 16 petabytes of data collected annually.
A petabyte is about a billion megabytes. That’s big data.
Another example on the graphic, and written about last year, is that we could track every heartbeat of every person, using wearable heart monitors.
When we used to talk about “Data” (with a capital D) we might imagine research data from scientific endeavors, or corporate data tracking sales orders or factory output. Data was tracked because it was intrinsic to the activities that are central to our daily toil. Now data is extrinsic, coming from outside – whether generated by people (YouTube videos and emails) or machines (energy sensors, health sensors, traffic sensors).
The fact that we are interested in, and able to utilize, data coming to us from outside our immediate sphere of influence is what defines the age of big data.
| A while ago I wrote a piece on “How to Score an Investment for your Startup”. Following that theme you can now read “Presenting to Win”, my latest article written for Scalable Startups project at UC Berkeley. |
This infographic is already, no doubt, out of date. It is from last June, but was probably out of date in July! People ask where big data comes from. This is a partial answer – as this is “just” data generated on the web and related web-facing activity. However, it starts to give you a feeling about where all this big data (data growing fast than Moore’s law) is coming from.
My friend Judah Levine sent me a link to this great article in InfoWorld “Big data's pitfall: Answers that are clear, compelling, and wrong”.
This reminded me of an article which I really enjoyed from The Atlantic “The Data Vigilante” which is about small data as much as big, but clearly is relevant for the big data world.
All this is to say that big data amplifies the problems of garbage-in, garbage-out, but introduces other more complex problems too. Big data, almost by definition, requires statistical analysis (it’s too big to just look at). How many of us, however, really know enough statistics to know the right analysis to conduct. The chart below from the Psychology Department web page at Muhlenberg College shows some simple examples.
If you read about big data proving this, that, or the other result, can you get a feel for whether any of these problems might be obfuscating the truth?
A few months ago I was invited to be interviewed about big data by Anders Høeg Nissen of the Danish Broadcasting Corp when he was visiting Boston. Anders is the presenter of Harddisken, a weekly hour-long program about technology, which first started broadcasting in 1994.
Earlier today, his program on big data was broadcast, and I already received one email from someone who heard the show (I am duly flattered). This week’s web cover page is online here, in Danish, but if you have Chrome or another similar browser you should be able to get Google to translate the blurb for you.
The podcast of the broadcast is available in Danish, and the segment that includes some of the interview with me (speaking English, please!) runs from 12:50 – 19:27. There is more Danish than English, so feel free to skip that. In fact, as I now ponder that my 15 minutes of fame happened in Danish, I can wryly hope that about England’s “crush” on Danish TV might perhaps extend to radio. (See the recent article in the New Yorker - subscription needed for full article.)
Who wants to know whether your startup will be around for the long term? Everyone.
“Investors want to invest in a company that will be around long enough to have a chance of making money. Customers don’t want to spend time and effort bringing on a new product or service that might go away. Employees (and their spouses) want to know the paycheck and benefits are at least somewhat stable (no matter how many stock options you give them).”
Check out my article on demonstrating some hope of longevity for the Scalable Startups project at UC Berkeley.
