Mentors: tips for early stage startups

Here are is the outline I use when coaching early stage startups starting accelerator or incubator programs as they consider how to get the most from their mentors.

I was once voted a top mentor for startups, and have enjoyed mentoring at TechStars, HealthBox, MassChallenge, and in many roles in my VC years and now at OptumLabs. Feedback welcome!

Goal: How to Optimize the Mentoring Experience for You.

If the smartest person in the room about
YOUR business is not YOU, we are all in trouble!
Mentoring is about making YOU smarter.

First start off looking at the Mentor Manifesto from David Cohen (et al) at TechStars: in it's original form and or as updated but very similar.

If your mentors do not seem to be tuned into this manifesto, think about how you are engaging with us (it takes two to tango), and then consider providing us feedback to get us on track.

Dos, Don’ts & Don’t You Knows

• Mentors don't have all the answers (or even ANY)

• If the smartest person in the room about YOUR business is not YOU, we are all in trouble! Mentoring is about making YOU smarter.

• Don't optimize; Don't optimize yet

• Show all your mentors all your ideas - don't whittle things down too early or you don't get complete responses from all the mentors. Maybe the first mentor hates it, but the next couple will love it - but not if you don't show it to us
  
  

• Don’t sell; don’t sell all the time

• You don’t have all the answers, either (although you have some)

• Honesty is the best way to get to know a potential, mentor, adviser, or investor

• Much better to be open and have constructive disagreements than to hide the concerns, the unknowns, the challenges

• Sell your passion and vision, but don’t be promotional or defensive

• Adjectives are fine; save the superlatives

• Mentors work for YOU (and for ourselves)

• Ask us for things

• Leverage our strengths - be aware of our gaps

• Remind us gently of our offers to help / make intros

• GIVE FEEDBACK about mentors - through the accelerator/incubator staff is fine!

• If we don’t perform, fire us!
  
  

• Don’t create special presentations just for your mentor meetings

• (I *hate* PowerPoint)

• Weekly emails are good; attachments are BETTER

• We *might* read them

• We will more likely refer back to them
  
  

• Prepare for your Mentor meetings

• Send the Agenda (questions, issues) ahead of time

• Even if we don’t read it, it helps you organize your thoughts

• Use Mentor Judo 

• Mentors want to be nice

• Set up the question so that to be nice we have to give the real news you want

• “I know we need to round out the team. What skills do we need to hire for?” 

• “The pitch doesn’t feel right to me yet. What suggestions do you have?”

• Embrace the Egos, Opinions and Whiplash

• Embrace it; it forces you to own your decisions

• Make it a virtue: it shows you the panoramic 3D view of the possible solution space

• Manage inflow; avoid distraction

• Don’t take egos seriously (it’s bad enough we mentors do)

• Are we Mentors or Investors?

• (Some programs explicitly do not allow cross-over ... so this may not be relevant)

• Mentors (like everyone) like to be nice, to say yes, to avoid saying no

• The right kind of investor is the right kind of mentor, and vice versa

• Clear communication about the bad stuff as well as the good is a great way for both sides to learn if we want to be in an investment relationship

• Understand that even before someone is considering investing we might subconsciously provide advice which keeps you on track to be heading in our direction... “you should definitely be planning for a series A”

• This is similar to mentor vs possibly hiring … even before we are sure we want to work full time with you, we might be subconsciously saying “you should definitely hire someone with a profile <like mine>”

• Don’t ask “are you going to invest in my business” at the start - the answer is always going to be no or maybe

• Instead ask what kind of investments we like to do when we do invest (so you know what our biases are when funding strategy discussions arise)

• If we fit your investor profile then later in the program let us know you are hoping to raise money, would like us to consider investing and are available to answer questions
  
  

• Team Dynamics: Forming, Storming, Norming, Performing

• In a mentor situation, mentors don’t want to touch the third rail personality issues (it’s hard, and we want to be nice)

• About half of you will face team issues

• use the safe space and excellent support to face it

• SO PUSH IT IN OUR FACE - tell your mentors what is on your mind about your team

• Mentors can be available to ALL the team
  
  

• Confirm explicitly “we both agree you will be our mentor” and a schedule for meetings or contacts

• There is a separate discussion to be had about mentors -> advisors -> board members. My advice is that for the duration of the program (and maybe longer), keep it fluid and informal. There will be time enough to formalize these relationships when things are a little more certain.

Common, or at least periodic, problems with Mentors

• This is somewhere in the middle of our priority list 

• We want to be NICE (and don't have money in the deal, so we can be, or want to be) 

• We want to be right

• We are drop-in visitors, and some inconsistently, and are way behind the plot 

• We don't get what you are really doing 

• We don't know your team or team dynamics 

• We have the wrong time frames

• Mentor vs Investor

• Always think we are right but change our minds (180 degrees) from week to week

• Egos and opinions

• Force fit our models on your startup 

• Have favorite techniques (useful but possibly irrelevant) 

• Have favorite fads (irrelevant but possibly useful) 

• Mentor whiplash (PS: not a problem - harness it!)

Help make peace in the middle east

Please contact your congressperson and senator in Washington and ask them to support the BIPARTISAN bill creating a fund for peace building in the middle east.
See the Kids4Peace video explaining why this is so important:

A bipartisan coalition of members on the left and right is a bit of a miracle (and the product of lots of hard work)... As is the range of endorsers, which includes AIPAC, J Street, AJC, Americans for Peace Now, ADL, Churches for Middle East Peace, JFNA, Alliance for Peacebuilding, the Jewish Council for Public Affairs, IAN, New Israel Fund and more.

Consumerisation of Health Care

I recently featured in a corporate video about the consumerisation of health care. I appear in the last minute or so (only a two minute video).

Click here to see it.

I think it provides an interesting flavor of some of the thought leadership at Optum (I work for OptumLabs, and Optum subsidiary), and some insight into major themes in healthcare today.

The backstory is that I spent some time being made up for a corporate video shoot a few months ago. 

The make-up took a few minutes, and then they interviewed me for an hour or two.

From that, they suggested some of the footage might be included in a corporate video.

The next thing I hear, a friend Moshe Cohen noticed the following on CNN Politics (phone screen shot). Thanks, Moshe!

Looking at UK and US Healthcare Systems

Here is a guest post I wrote for Healthblawg's 10th anniversary (with thanks to David Harlow for inviting me to contribute): US and UK Health Systems - Not So Different?

Happy 2015

Happy 2015… and here are some new year short pieces for you to look at.
Julie Zhuo, who I don’t know, has a blog I just found but already love … here are two great posts that really speak to me, especially having spent so much time in startups and now at a big company: Start-ups versus Big Companies and also So You Think You Want to Manage?

My colleague and mentor-extraordinaire, Brad Feld, recently suggested: If You Want A Response, Ask Specific Questions. This is something I second very strongly to anyone approaching someone whose time they would like, but whose time is likely to be constrained (hint, hint – to folks approaching me in similar generic ways).

On Father’s Day: Lessons from my Daughters

For a Father’s Day blog series, I was asked by a friend to write about being the father of daughters… here it is: http://jwa.org/blog/lessons-from-my-daughters

Happy Father’s Day!

Birds singing, sun shining, wheels turning

I am so looking forward to starting riding again. Friends of mine cycle through the winter, and maybe cycling today, but I prefer roads free of snow banks, even when melting (prone to icing in the shade). Today is a foretaste of spring to come, and bike rides to enjoy.

Meanwhile, kudos to the town of Concord, MA for surveying non-residents as well as residents on pedestrian and cyclist access and safety. Take the survey!

Great Business Model Canvas introduction

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.

1. Getting From Business Idea to Business Model
2. Visualizing Your Business Model
3. Prototyping
4. Navigating Your Environment
5. Proving It
6. Telling Your Story

Choose Life

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.

This is water–David Wallace Foster

(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 of cycling

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.

Characteristics of Big Data

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.

Bonuses at Startups

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.

Back from the Dark Side

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.

Freudian slip? HP Moonshot: meh

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!).

Spring has sprung

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."

Big Data and the Internet of Things

I recently wrote about the amazing amount of data being produced by people on the internet or mobile web. For example, every minute, YouTube gets 48 hours of video uploads, and nearly a quarter of a billion emails are sent… yes that’s what people do every minute.

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.

Presenting to Win

“Whether you are presenting to investors or to startup competition judges, you are faced with some amount of random behavior.”

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.

Lots of data, all the time

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.

 
Infographic by Visual News

Big data doesn’t mean right answers

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?

15 minutes of fame … in Denmark

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.)

Tak og farvel.