The Fundable

mHealth and Data Liquidity

2012-12-14T08:30:00.000-05:00

Healthcare Needs APIs More than Apps

This post was written with my colleagues Joris Van Dam, Translational Sciences Strategic Project Leader at Novartis Institutes for Biomedical Research (NIBR); and John R. Walker, director of NIBR IT for Novartis.

As frequent attendees of mobile health conferences, we’ve been excited by the momentum and creativity we’ve seen in mobile health solutions (Health Apps). But we've also been concerned.

We’re excited because of the number of Health Apps being developed for patients and consumers in different disease areas, for wellness, prevention and care. As big believers in the potential of Health Apps to improve outcomes and lower healthcare costs, we’re glad to see this market really take off.

At the same time, it seems there’s an App for everything – and anything. Yet there’s very little talk of interoperability or data exchange – heck, even about preserving my personal health data when I want to exchange my App for a new one, when I need to re-image my phone, or when I want to buy a new phone.

It seems like in the rush to capture the value of mHealth, we’re launching and creating a staggering amount of new health data silos – making health data less liquid and shareable when we need just the opposite. This is a big concern, because in the longer term, data liquidity is the key to sparking innovation, improving outcomes, and reducing healthcare costs.

At the recent 2012 mHealth Summit, it was encouraging to see that this approach is starting to shift. It seems that more and more healthcare companies are recognizing that even though their patients or their customers need these Apps, perhaps jumping head-first into Apps development isn’t their best bet. Instead, maybe healthcare companies should let “the market” develop these Apps (they seem to know what they’re doing!) while they support the market – with funding, with infrastructure, and with content (liquid data).

Getting It Right

One of the great examples at mHealth Summit was presented by Aetna, a 160-year-old health insurance company. Rather than joining the Apps Race, Aetna has developed and published the CarePass platform. The platform provides a set of APIs that allows you to build your own Apps, plus it provides interoperability among your App and all the other Apps developed using CarePass – including the 20+ Apps that Aetna has developed and acquired itself, such as iTriage. Update of January 24, 2013: Check out this great video interview with Aetna Vice President Martha Wofford.

AT&T and Qualcomm Life also presented their platforms, with APIs that connect to a variety of different health and wellness devices. These APIs allow you to build Apps that connect to these devices, yet also thereby become independent of the devices. For example, you could switch from one fitness sensor to another, buy a different wireless weight scale, or use a new blood-pressure monitor while the App preserves your health data.

Joris Van Dam

Entirely in the same vein, earlier this year Athena Health, the Watertown, Mass-based provider of cloud-based Electronic Health Record (EHR) systems, launched their “More Disruption Please” program. Participants in the program receive access to the API of Athena Health’s EHR system, a platform to build Apps. They can even get access to start-up funding and launch support for their Apps in the network of Athena Health customers.

Allscripts launched a similar developer program for their EHR. And in 2009 Cerner launched its uDevelop platform, providing their customers with a platform and APIs to develop new Apps, and an App store to share these Apps with each other.

Of course, the largest, and perhaps most influential, organization to get it right is the ONC, which has been setting the tone for improving data liquidity in healthcare, and thereby stimulating healthcare innovation in Health Apps development, through an array of policies and programs. These include health information exchanges, meaningful use criteria for EHR adoption, Project Blue Button, and the Health Data.gov platform – to name just a few.

Many of these programs also provide funding for you to develop Apps with their APIs. And, if not, there is always Health 2.0, which announced in May 2012 that it had already awarded more than $1 million to mobile health initiatives through its Developer Challenge series.

Where to Go from Here

There will always be a new App, a better App, an App that fits my health needs better as they evolve over time – and that’s OK. Yet all this tremendous activity and investment in Health Apps will only really and truly pay off, for individuals and their individual health needs as well as for a sustained impact on the healthcare system overall, if those Apps are built on, and contribute to, an underlying layer of liquid health data.

John R. Walker

As patients and consumers, we need to be able to switch from one device to the other, carry our health data from one provider to the other, switch insurance companies as we take new jobs, exchange one App for the other and so on – as our personal healthcare needs evolve over time. And those personal healthcare needs will really and truly only be served if our health data moves with us, as opposed to being caught in the latest fad and locked in the latest App.

For mHealth to pay off, we need APIs more than we need Apps. It’s great to see that so many companies are getting it right. A year ago, we felt both excited and concerned at the prospect of mHealth. Today, we’re just pumped.

Open Patient Consent and Clinical Research Data

2012-11-27T08:15:00.000-05:00

Overcoming Lingering Concerns

In my last several posts, I wrote about how crucial it is to increase the liquidity of clinical research data ― particularly clinical trial data ― and how we can achieve improved data liquidity with patient-centric systems and software.

But there’s a remaining ― and not insignificant ― challenge: patient consent.

Before sharing their health information, people want to know it’s going to be secure and beneficial to do so. Until you’ve got people who are willing to share their data, it’s tough to justify the investment in building secure systems. A classic chicken-and-egg problem.

This is a very similar dynamic to e-commerce back in the 1990s. People were afraid to enter their credit card numbers into a Web site. Many people were even saying that no one would ever trust personal financial data to the Web. Today, you can take a picture of a check with your iPhone, deposit the check electronically and throw the physical check away. The convenience of electronic financial transactions via the Web far outweighed the security risks (both real and perceived).

I believe that we’re going through a similar transition with electronic health data that we went through with personal financial data back in the 1980s and 1990s. It may take a decade or two for people to be comfortable sharing their anonymized and aggregated medical information to benefit research...but maybe not. In my humble opinion, the benefits of portability of our medical information now far outweigh the security risks/concerns.

Some of the benefits are very pragmatic:

Portability: When you switch doctors, you can bring your medical history with you electronically
Accessibility: When you have an emergency and the ER team needs your history immediately and will want to search for all your allergies quickly
Reference: When your doctor asks you when you had your last immunizations

You might recall that some high-profile early efforts at personal patient record systems failed ― specifically, Google Health and Microsoft HealthVault. I think of these not as failures, but as invaluable experiments that helped us all learn what works and doesn’t work in managing and sharing health data securely and efficiently. And perhaps most importantly, these experiments began to socialize the ideas of medical information being represented electronically and of patients owning their medical records.

Meet Project Green Button

Project Green Button is an important experiment in creating data liquidity and sharing medical information.

You may have heard of Project Blue Button. This was a fantastic project launched by the US Department of Veterans Affairs, enabling VA patients to download a copy of their own health data from the VA’s systems ― by clicking on a Blue Button. This is a really great example of empowering patients to own their own medical data and improving the portability of their medical information.

Now let’s think about it the other way around.

Research has suggested that if people were presented with a Green Button (see picture above) that provided a single-click way to share their data with researchers, more than 80% of people would press it. My close friend and trusted colleague John Wilbanks gave a great TED Talk about this a few months ago and has been doing ground-breaking work on open consent, the philosophy behind the Green Button.

I’m very hopeful that John will be successful in his mission to empower patients to share their own data to benefit research – after all, it is their data – and that most people, when asked, will be willing to share their anonymized information to benefit research.

The team at the LIVESTRONG Foundation, led by Director of Evaluation and Research Ruth Rechlis-Oekler, Ph.D, did a great study a few years ago about cancer patients’ and survivors’ willingness to share their information in the interest of improving research. The results were compelling: the majority of patients and survivors WERE willing to share their de-identified and aggregated health information with researchers in the interest of improving health care for others.

So, the willingness is there. All we are missing are the systems to enable this. And the time to create those systems (similar to what I described in my last blog post) is NOW. One of the most interesting companies working in this area is Avado; founder Dave Chase is one of the thought leaders in this space and has fantastic vision for where the industry needs to go over the next 10 years.

Wanted: A Trusted “Zone”

In order to prime the pump of online personal health data, we need patient-controlled solutions and a trusted zone where we can connect patients securely with their data. This trusted zone would be a place where:

Each patient has a “dashboard” through which he can get access to all data about his own health, regardless of where the referential data is located.

Each patient can determine whether and with whom to share pieces of his health data – with his family doctor, relatives or loved ones.

Each patient can track his own health, and record and track his own experiences.

The result would be something that feels like a simple dashboard: a single environment, available to the patient, where he can access, share, and manage all his relevant health information.

Once we can “free the data” in a trusted environment like this, it’s possible to bolt on a whole battery of cool apps that you can’t even anticipate today ― and that you don’t even have to develop yourself.

With today’s rapid app development technologies, we can build apps for physicians and for many other use cases, and simultaneously make the apps accessible via browser-based applications.

A great example is the LIVESTRONG Cancer Guide and Tracker for iPad, which collects and combines patient-reported data for patients living with cancer. We currently have a pilot project under way to connect the Tracker app with traditional clinical systems and a secure Web-based application, enabling the patient and his doctor to collaborate more effectively during office visits. We're using the fantastic SMART Platform developed at Children's Hospital in Boston under the leadership of Zak Kohane and Ken Mandl.

A Call to Action

So, here are a few action items:

First, we in the biopharmaceutical industry should ask ourselves: “What data can we ‘free up’ about our products and our studies to better support patients and physicians?" Let's start with the basic inclusion/exclusion criteria for our existing studies that we already share with our clinical partners. As I mentioned in my previous post, if we would just increase the liquidity of this data in the biopharmaceutical industry, we could have a HUGE impact on the efficiency of research through the ability for the right patients to find the right studies at the right time.

Second, we should support the idea of open consent by supporting the Consent to Research project (http://weconsent.us/). Let's all give the flexibility to patients to support research with their own medical information and, in the process, radically improve the efficiency of the healthcare system by simplifying the complex spider web of consents that our dysfunctional healthcare system has created. The current system of consents does not protect patients, but rather confuses them.

This blog post came out of a presentation that I recently delivered at Rev Forum, a conference sponsored by Lance Armstrong’s LIVESTRONG Foundation and Genentech. I have worked with LIVESTRONG and various biopharmaceutical companies on new health care information products and apps that take advantage of data liquidity to help patients combat cancer and other difficult diseases.

Better Systems for Clinical Data Collaboration

2012-11-19T08:30:00.000-05:00

Innovation in Systems and Software

In my last two posts, I wrote about the need for more liquidity in clinical research data. As a foundation for sharing this new more-liquid clinical research data, we need more patient-centric systems, where patients can create, consume and maintain relevant medical information.

However, in the average hospital, most patient data is generated and organized in the clinic, and typically stored in a variety of different legacy hospital systems. Pretty illiquid by definition. Therefore, we need fresh approaches to sharing that data across hospital systems ― and then across multiple hospitals.

Fortunately, innovation in systems and software is beginning to happen on this front.

Several organizations ― including Dana-Farber Cancer Institute, the LIVESTRONG Foundation, and Boston Children’s Hospital ― are working to build a reference implementation. This is a technology model that would describe how a hospital could publish the information contained in its systems easily, securely and efficiently to other institutions.

Another very interesting technology is the SMART Platform, developed under the leadership of Dr. Zak Kohane and Dr. Ken Mandl at Children’s Hospital in Boston. The SMART Platform and i2b2 Analytical tool are truly a step in the right direction.

These new technical approaches provide the ability to build cool new apps very quickly – apps that combine data reported by the patient in an interface like the LIVESTRONG Cancer Guide and Tracker iPad app alongside data collected by traditional clinical systems. Using the same approach, we can build apps for physicians and many other use cases and simultaneously make the apps accessible via browser-based applications.

Let’s think about what else we can do as an industry – and encourage many people and companies to start writing new apps quickly.

In my next post, I’ll talk about big remaining challenge to the liquidity of clinical research data – patient consent – and how we win patient trust and cooperation.

This blog post came out of a presentation that I recently delivered at Rev Forum, a conference sponsored by Lance Armstrong’s LIVESTRONG Foundation and Genentech. I have worked with LIVESTRONG and various biopharmaceutical companies on new health care information products and apps that take advantage of data liquidity to help patients combat cancer and other difficult diseases.

Improving Data Liquidity in Clinical Research

2012-11-13T08:30:00.000-05:00

Empowering Patients and Doctors

In my last post, I wrote about the need for data liquidity in clinical research – and the need for biopharmaceutical companies and healthcare institutions to take the lead by freeing up data about their studies, clinical trials and drugs.

To accelerate clinical research efforts for diseases like cancer, we need two things. First, stakeholder institutions (like biopharmaceutical companies and healthcare institutions) need to free up their data. Second, we need new systems and software that can share and manage that data – securely and at scale – across our complex healthcare ecosystem.

Start-ups are being formed every day that are pushing the envelope on applications and technologies that take advantage of health care data liquidity. In Boston/Cambridge alone, we have three start-up incubators dedicated to health information technologies, each with 10+ start-ups. That’s more than 30 top-tier, vetted start-ups focused on health information technologies being developed at any given point and time. For example, check out:

Rock Health
Health Box
AthenaHealth Accelerator

Pharma companies have long been proponents of “free your data” – as long as it means freeing data from patients, from claims clearinghouses, from pharmacies and so on. Pharma companies have been less enthusiastic about freeing their own data, about their studies, clinical trials and drugs. But the logjam is starting to break.

As I mentioned in my previous post, recently GlaxoSmithKline (GSK) publicly announced it would make detailed data from clinical trials available to researchers. The company’s detailed patient data (data that forms the basis of trials of approved drugs as well as discontinued investigational drugs) would be made accessible to researchers. Researchers’ requests for access will be reviewed by an independent panel of experts, and the patient data will be anonymous.

Last year, one of the large biopharmaceutical companies – in collaboration with the electronic health record company Cerner – began an initiative to build an open interface that will enable sponsors of clinical research studies such as Novartis, Genentech, GSK or Pfizer to publish inclusion/exclusion criteria about their clinical studies to specific clinical partners – in much greater detail than what’s available on the National Institutes of Health's ClinicalTrials.gov today.

The goal of this project is to create a electronic mechanism to ensure that all eligible patients are identified for appropriate studies via their doctors. This mechanism would be able – without changing any data privacy – to flag patients’ records when they are diagnosed or when new studies or updates to studies become available; and to dynamically notify doctors and match patients with new or evolving studies based on patients' clinical profiles and the inclusion/exclusion criteria of the trial.

Under this new electronic standard for study information, study eligibility criteria are expressed in a standardized, machine-readable format. Any EHR system can ingest the study data automatically as new studies are created and as existing studies change. With this more-liquid data, providers can then match the inclusion/exclusion criteria against the health records in their systems.

A provider configures its systems to flag records of potentially qualifying patients using a form of research-study-recommendation engine. The provider runs and tunes this engine so that the next time a clinician pulls up a patient’s health record (or the patient’s health record changes), the EHR system will suggest to the doctor that his patient may qualify for a clinical trial – both local and not-so-local trials (think truly global patient recruitment with little or no extra effort).

In addition, when a new trial is published, doctors with patients can be notified that there is a new trial of possible interest and eligibility for specific patients.

Through this type of simple standard for study information exchange – one that empowers doctors and is run by providers – doctors and patients could be automatically made aware of trials regardless of where the study is being run or when a new study starts.

Using this simple standard, Cerner has worked with various large biopharmaceutical companies to build and test end-to-end Proofs of Concept – in the process successfully demonstrating that this approach can work very well with relatively little extra effort on the part of the study sponsors, the providers or the biopharmaceutical companies. There is no additional risk of information privacy, since these criteria have been published previously to the providers, and the patient data does not have to be shared at all. The standard just enables getting better data on studies to providers in a more targeted way.

In short, starting to improve liquidity of clinical research data just requires leadership from pharma companies and cooperation from health care providers to prime the pump and adopt standards – and perhaps the encouragement of trusted brokers such as LIVESTRONG to bring the parties together.

Increasing the liquidity of data in ways like this could improve enrollment in studies, especially for rare diseases with small patient populations. Doctors and patients would have a proactive monitoring system that reminds them about all relevant research studies – especially new studies in rare indications – regardless of geography or the distractions in their daily lives.

The Need for Data Liquidity in Clinical Research

2012-11-07T08:09:00.002-05:00

Much of the content in the next few posts was developed jointly with my close friend and trusted colleague Joris Van Dam. Joris is truly a superstar and is doing fantastic work around the world related to eHealth and improving the liquidity of data in healthcare.

In the course of creating new drugs and therapies, organizations in the biopharmaceutical and healthcare industries amass huge amounts of clinical trial data. Unfortunately, much of that data remains locked up in individual IT systems, making key data unavailable to the many of the participants in clinical trials: physicians and their patients. As our society intensely seeks cures for cancer and other diseases, this is nuts – and completely unnecessary. It's time for a change. The data required to empower researchers can be shared securely and appropriately.

In the Information Age where we can do so much via the web, our smartphones, our iPads and the "Cloud," we shouldn’t accept word of mouth as the best tool for patients to find the right studies. It’s clearly not. Nor should we accept data illiquidity as an obstacle to timely, broad availability of information about clinical trials.

The Story of Melissa

Meet Melissa. Melissa isn’t her real name and that isn’t her real picture above, but this is a true story.

Earlier this year Melissa was diagnosed with one of the deadliest forms of cancer. Despite her predicament, Melissa is one of those patients (like many of those involved in the LIVESTRONG community) who decided to take an active interest in her own care.

She wanted to proactively explore any and all kinds of treatment opportunities, including experimental treatment in a clinical research study.

Melissa was smart enough to understand that clinical trials offer no guarantee of improving her condition, let alone a cure. But the opportunity to participate in a clinical trial would give her hope. It would give her the ability to fight, and the satisfaction, that through her disease, she might be able to contribute to better treatment and ultimately perhaps even a cure, if not for herself then potentially for others like her. It would give her the feeling that her pain and suffering mattered and that she could make a difference in the world.

Melissa believed that it was important for her to have the opportunity to join a clinical research study. So she spent a lot of time on the Internet, educating herself about her disease and treatment opportunities. One day, using the U.S. National Institutes of Health’s Clinicaltrials.gov, she found a study for which she appeared to qualify ― one being run not too far from her home.

Unfortunately, Clinicaltrials.gov didn’t list the name or contact number for the investigative site. It just said that it was a  study being run by a large biopharmaceutical company and that she could call the main switchboard number. She called and they really couldn’t help her ― so Melissa was stuck. Next, she turned to a clinical-trials matching web site and asked if there was anything they could do to help her.

Fortunately, by chance the team at the research group of the large biopharmaceutical company happened to know the person who runs that matching web site. That person connected Melissa with someone who offered to help coordinate.

The folks at the biopharmaceutical company went into their clinical trials database to identify the study manager. Then, they contacted the study manager, who went to the matching web site asking if it was okay to share their contact details with Melissa. A few weeks and many phone calls later, Melissa finally had a screening appointment at the clinic. Within a short time Melissa ― through her own perseverance and a lot of luck ― was screened and enrolled in the study.

Now the shocker in this story is that...

....the study investigator was Melissa’s own doctor!

This was the very doctor who had diagnosed Melissa just a few months earlier.

It’s tempting to think that this doctor dropped the ball. But in fact he hadn’t. He’s an extremely competent and compassionate physician, not to mention a great study investigator. He just had a lot going on, and the timing of the start of the trial had been off a bit with the timing of Melissa’s diagnosis.

It might also be tempting to say that the biopharmaceutical company was at fault for not listing the investigator’s contact details on Clinicaltrials.gov. But the clinic in question is based in Europe, where regulations are such that pharmaceutical companies have to obtain explicit consent from each individual investigative site before its contact details can be listed on Clinicaltrials.gov. The company just hadn’t dealt with all that red tape yet and there are no systems set up for information to flow more easily.

So, this situation was no one’s fault in particular, but rather a matter of circumstances, bad timing and the lack of data liquidity.

Time for Big Pharma and Healthcare Institutions to Step Up to the Plate

Clinicaltrials.gov was an important milestone and a catalyst when it was launched. On the back of clinicaltrials.gov emerged a slew of applications that help patients and doctors navigate the data, and find studies that are particular to a condition.

These include:

The Fox Trialfinder for Parkinson's Disease from the Michael J. Fox Foundation

Corengi for diabetes

YourTreatmentChoices.com

More recently, there are new smartphone apps such as TrialX and CoActive.

This is what data liquidity is all about: Making data appropriately available to encourage an ecosystem of applications that help patients and physicians ― and ultimately help drive down the cost of health care and improve outcomes.

But Clinicaltrials.gov was launched 12+ years ago ― 7 years before the first iPhone. We now need to go much further and faster in liberating clinical research data, and I believe that the large biopharmaceutical companies and healthcare institutions have the opportunity to take the lead.

GlaxoSmithKline (GSK) recently announced that it will open up access to its clinical trial data as appropriate to support open collaboration among researchers. (You can read more about this decision in this Wall Street Journal article here.)

To accelerate clinical research efforts, we also need new systems and software that can improve liquidity of clinical trial data across the complex healthcare ecosystem.

By increasing the liquidity of clinical trial data this way, we could both improve the lives of patients and reduce overall health care costs.

The information technologies exist. Attitudes toward information-sharing are changing.   And cost reduction and better outcomes are compelling motivators.

In my next posts, I'll talk about some specific initiatives that could have a big impact on the liquidity of data in the healthcare industry as well as a number of issues related to consent, where my great friend John Wilbanks is leading the charge. Check out his TED talk.

This blog post came out of a presentation that I recently delivered at Rev Forum, a conference sponsored by Lance Armstrong’s LIVESTRONG Foundation and Genentech. I have worked with LIVESTRONG and various biopharmaceutical companies on new health care information products and apps that take advantage of data liquidity to help patients combat cancer and other difficult diseases.

RISD Entrepreneur Mindshare Celebrates Artrepreneurship

2012-11-06T08:00:00.000-05:00

Encouraging Entrepreneurship for Artists

My wife Amy Palmer and I were honored to sponsor the 2nd annual RISD Entrepreneur Mindshare. RISD President John Maeda has provided incredible leadership and thoughtful guidance in developing the next generation of top-tier "Artrepreneurs," and the event was truly inspiring.

This year's conference had more than 150 participants including RISD students, alumni and faculty as well as folks from the Providence and Boston entrepreneurial communities.

The conference kicked off with Joe Gebbia RISD '05/ID/GD, Co-Founder of Airbnb, who gave a profound talk about how to be successful as an artrepreneur.

There was some great press coverage of the event:

RISD Joe Gebbia says to "take the next step"

At RISD, Design Meets 'Scrappy' to Create Artrepreneurship

RI School of Design holds innovation conference

Joe was followed by a two-day visit by Enrique Allen, co-founder of The Designer Fund, who met one-on-one in small groups with RISD students and alumni. RISD students were the first audience given access to the brand-new free e-book published by the Designer Fund, Designer Founders.

The next big event at RISD is the Artrepreneur Bootcamp in collaboration with the New York Foundation of the Arts in January followed by a full-day hands-on Art of Business Bootcamp in April.

There is a vibrant and visible student entrepreneurial community at RISD, thanks in large part to the support of RISD President John Maeda as well as the entire RISD Staff – especially Greg Victory, who is the voice of entrepreneurship at RISD. GREAT JOB, GREG!

Upstart is in Beta - Check It Out

2012-11-05T08:39:00.000-05:00

Help Support the Next Generation of Entrepreneurs

I posted previously about a great new company called Upstart that I’ve been helping to get off the ground. Now Upstart is in Beta. I’ve already backed more than a dozen outstanding Upstarts – and you should consider doing the same.

Upstart is Kickstarter for people: a crowd-funding platform that allows college grads to raise capital in exchange for a small share of their personal income over 10 years. It aims to make it easier for grads to pursue what they really want instead of following a traditional job path, by matching the Upstarts with mentors and giving them a modest amount of economic freedom (to retire student debt or fund the first step of their dreams).

At this point, Upstart has launched its first pilot class of funded students (that's one of them, Ian Shakil, above), and also attracted more than 30 leading universities into the Upstart network. The company has also generated a great deal of positive press for its approach to addressing a major problem.

Now that Upstart is featuring its next batch of profiles, I wanted to invite you to join me in supporting the next generation of great entrepreneurs.

You can invest as little as $100 in an Upstart. I’d recommend diversification by spreading your investment among five or more Upstarts to maximize risk/return. The great part about this is that you get returns that are better than T-Bills AND you are supporting specific young people who are pursuing alternative career paths – the kind of paths that usually lead to the most value creation in our economy (NOT investment banking or consulting).

You can browse some of the new Upstart profiles here: https://beta.upstart.com/upstarts

You can register to be a backer here: http://beta.upstart.com/register?code=b4d9ad169139872384acd56b10a9ff1d

About Upstart

Upstart was founded earlier this year by my close friend Dave Girouard, who was president of Google Enterprise for eight years (cloud apps, Gmail, Calendar, Google Docs, etc). Dave brought over a few members of his team from Google. Upstart was seed-funded by Kleiner, NEA, Google Ventures, First Round, and Mark Cuban, and the company and its team are off and running.

You can find some recent press on Upstart here:

Upstart - Empowering The Next Generation of Entrepreneurs

2012-08-08T09:00:00.000-04:00

Today the team at Upstart announced what we’ve been up to over the past three months.

The Upstart mission is inspiring (see Founder/CEO Dave Girouard's blog post here), the team is world-class, and the culture of the company is…well…a ton of fun. I’m especially thrilled to be joined as a “Backer” by two close friends and trusted colleagues in Boston, Frank Moss and Jim Doughert y. Their leadership in supporting young people who want to turn their passions into careers as entrepreneurs is exemplary. I’m also thrilled to be working yet again with the fantastic partners at Kleiner Perkins, NEA and Google Ventures.

Briefly, Upstart is a new approach to funding and mentorship. Using a crowdfunding model, it allows college grads/would-be entrepreneurs in virtually any field to raise capital in exchange for a small share of their income over a 10-year period. Upstart aims to provide a modest amount of risk capital, paired with guidance and support from experienced backers, to help grads pursue less-traditional and more-inspiring careers.

I believe that Upstart has the potential to supercharge the US Innovation Economy - as Dave says "When politicians say we need more entrepreneurs, what they mean is that we need more people creating jobs, rather than taking them." I couldn't agree more - it's time to do the heavy lifting required to create more entrepreneurs so that they can do the heavy lifting required to drive job growth over the next two decades. Upstart believes that one of the key factors in creating more entrepreneurs is early intervention in their career development. Some of the key principles that are driving us include:

● Innovative and ambitious young people should be empowered to pursue their passions when they are young. If we don’t empower them when they are young, they risk being numbed by the bureaucracy of the larger organizations that they often join for lack of a viable alternative path as entrepreneurs. It’s not that big companies are bad. It’s just that young people who have a high risk/return profile can quickly lose their edge and passion as they succumb to the broader interests of a large organization vs. pursuing something that they care about deeply. 65% of our job growth over the past 2 decades has come from companies with less than 500 people - over the next 2 decades it's the Millennials/Generation Y that will create those companies and create the bulk of jobs that our country needs so desperately - we need to empower them as much as possible. It's time to bet on Generation Y.

● It’s fundamentally valuable for our economy to balance the recruiting machines of large organizations with a social networking-based system that facilitates young people who want to follow more independent, highly individual paths. This generation just wants to connect with people who could be their mentors in pursuing their interests and passions. Mentors just want to connect with inspiring young people. Upstart makes those connections easy and automatic.

● Not every young person has a high risk/return profile, but many more young people will pursue entrepreneurial interests if they have a little bit of financial flexibility at the right time. Modest amounts of financial support as young people graduate from school, along with some strong support and encouragement from great mentors, can go a long way. I know because I've had fantastic support from many great mentors during my career.

● Mentorship is just as rewarding for the mentor as for the mentee – if only we can make the right connections. What’s been missing is a system to connect potential mentors and mentees around shared interests and affinities. I’m a software guy who is interested in the life sciences, so I’m naturally prone to want to mentor smart, young, enthusiastic people who share those interests. But I’m also passionate about rugby, so anyone who is involved in rugby always gets more of my time than those who don’t ;) When young people who share my interests ask for my help - I'm compelled to help them - because I get way more back than I give.

● Most mentors who have the financial resources – when provided with the opportunity to earn a return similar to bonds – would be thrilled to invest their own money in promising young people with similar passions and interests. Upstart matches mentors with the young talent who will power the growth of our economy over the next 20+ years – and does this at scale. I can't think of a better investment - definitely better than T-Bills.

● To scale entrepreneurship in the US, we need to scale our ability to empower and coach young people who are capable of taking risks and executing on their passions. I’ve spent a lot of time starting companies from scratch. In my experience, the older and more successful people get, the more they are prone to take for granted the value of a small amount of coaching and financial resources early in the career of a budding entrepreneur. Small amounts of time and money directed strategically and without friction at scale, can have a huge positive effect on our economy.

● Most entrepreneurs need help, coaching and advice in order to achieve their missions. Most people are not the kind of superstar entrepreneurs that the media popularizes every day: Steve Jobs, Bill Gates, and so on. At the same time, these young people contribute the bulk of the job creation through the number of companies they start and the never ending flow of their ideas, energy, passion. A big part of what Upstart provides to young entrepreneurs is a network that can fill the gaps in their experience, knowledge and contacts so they can reach their full potential.

I’m honored to be partnering with my great friend and trusted colleague Dave Girouard as the Founding BOD Member at Upstart and thrilled to be the #1 Backer.

Let’s take the “post-industrial reins” off our brightest, ambitious young people and empower them to leverage the Information Economy to change the world for the better. Our country was founded on a core principle of rugged individualism. Let’s coach our young people to take control of their own careers, professional lives and interests and pursue their passions. We will benefit as an economy and a society in ways that we can't begin to imagine.

Steve Blank on Demystifying the Start-Up Culture

2012-07-16T08:15:00.000-04:00

In his remarks yesterday at the closing session of the National Governors Association Annual Meeting, author, successful entrepreneur and educator Steve Blank did a great job of articulating in layman’s terms how the start-up ecosystem in Silicon Valley works, why start-ups are different and require specific expertise, and why embracing start-up culture and expertise is so critical. I believe Steve's work is going to be a primary driver of innovation at scale for our country. It’s BRILLIANT.

If you care about start-up/founder culture or start-ups, you should watch his talk in its entirety.

Steve mentions the importance of researchers who focus not only on world-class research going on inside of their respective academic institutions, but also on the application of their research at scale in the public and private sectors. Some of my favorite role-model researchers in Cambridge include:

George Church
Eric Lander
Bob Langer
Marvin Minsky
Mike Stonebraker

These innovators have done not just one or two start-ups each, but many dozens of start-ups. It's this combination of world-class academic research and commercial innovation that creates exponential value in our economy and in society.

Here is a quick summary of Steve's talk and some highlights that really hit home with me.

A Quick Summary

Four lessons:

Different types of start-ups
What a start-up ecosystem looks like
How to make start-ups fail less
Can we actually teach what we now know?

Types of start-ups:

Lifestyle start-up - small businesses that serve known customers with known products and feed the family
Scalable start-ups – which are designed to grow big (“We’re going to build a company that will take over the universe!”)
Buyable start-ups – which have low capital requirements and can be very valuable very quickly
Large companies that focus on “sustaining innovation.” They innovate on core products but cycle time is compressing, creating pressure for large companies to innovate faster

Important Highlights

Referring to the Nokia Board of Directors’ reaction to the iPhone on day of launch – “It's a toy. Why should we worry about this?” – Steve said: “To a big company, a disruptive innovation always looks like a toy on day 1.”

“The secret history of Silicon Valley” [another great video]

“The first venture capital was an unintended consequence of Sputnik”

“We want the engineering department at Stanford to face outward as well as inward”

“Recognize that 90% of start-ups fail”

“Failure = Experience. We understand that these are the risks”

“Start-up culture is not just about the great entrepreneurs. It's about the ecosystem. Building this ecosystem is critical in getting any cluster off the ground.”

"Start-ups are not just small versions of big companies" - Steve Blank

“Embrace failure as part of the process”

“95% of start-ups fail because they didn't find customers or markets”

“We now know that start-ups search for something and large companies execute...The difference between search and execution are not just words...it's actually the difference in how we build these things”

“On day one, start-ups just have guesses...A start-up is a faith-based enterprise.. You want to turn the faith into facts as quickly as possible”

“What we now know is that no business plan survives first contact with customers”

“A start-up is a temporary organization”

“A start-up is designed to search for something that is repeatable and scalable”

“In the Customer Development Process, everything you know or think you know is a guess"

“In a start-up, it used to be when you failed you fired the VP of Sales, then fired the VP of Marketing, then the CEO. We know now that start-ups go from failure to failure…Pivot says that this is going to happen all the time.”

And, I love the fact that he was the only dude not in a suit. Now there’s a lesson in itself :)

Vertica - Remembering the Early Days

2012-07-13T08:15:00.000-04:00

I had an awesome visit @ Vertica earlier this week for lunch. Cool new space in Cambridge and so many fantastic new people. Thanks to Colin Mahony for the invite and to all the talented engineers and business people at Vertica for building such an amazing product and a great organization!

Couple of memories that came to life for me during my visit:

Reading the draft of Mike Stonebraker's "One Size Does Not Fit All" paper and thinking: "This is the mission of an important new company: to prove that One Size Does Not Fit All in Database Systems."

During my first meetings with the "Vertica professors" - Mike, Dave Dewitt, Mitch Cherniack, Stan Zdonick, Sam Madden and Dan Abadi - thinking "We have an unfair intellectual advantage." The technical hurdle was set early by this fantastic team.

Looking up at the new duct work from our original server room (at our original office), which Carty Castaldi vented into the conference room because the conf room was so cold and the servers were running so hot ;)

Inspired Duct Work by Carty Castaldi

The thrill I felt the first time that I watched SELECT work on the Vertica DB :)<

Our first Purchase Order. Thanks to Emmanuelle Skala and Tom O'Connell for that one and the many more that followed and made it possible to build such a great product :)

At our first company summer picnic at Mike's place on Lake Winnipesaukee: taking Shilpa's husband Nitin Sonawane for a ride on the JetSki and him being thrown 10 feet in the air going over a wave. I thought he'd never talk to me again. So glad that he didn't get hurt and that he talks to me regularly ;)

Our first big customer deal with Verizon and then the first repeat purchases by V. Thanks to Derek Schoettle and Rob O'Brien for building such a great telco vertical and for doing deals with integrity from Day One.

Sitting in the basement at Zip's house in Chicago with Stan, Zip and Mike as they jammed Bluegrass music and we all ate Chicago-style pizza until the wee hours. Thanks to Zip and to everyone at Getco for being such a great early customer and partner.

Relief I felt when Sybase admitted that Vertica did not infringe on their IP :) Thanks to Mike Hughes and everyone else involved for the truly awesome result.

Getting early offers from a bunch of big companies to buy Vertica and thinking "These guys don't realize how important Big Data is going to be and how great our product is and how incredibly talented our engineering team is." Thank you to our BOD for resisting the early temptation in spite of tough economic conditions at the time and thanks to Chris Lynch for negotiating such a great deal with HP.

During lunch on Wednesday, realizing that Vertica's product is truly world-class and has proven that one size does not fit all. Special thanks to every engineer at Vertica, especially Chuck B. : you all ROCK!

I have a much more detailed post in the works, about the early days of Vertica and what I as a founder learned from the experience. Stay tuned for this post in the next few months.

Founders: Are You Stuck Before You Start?

2012-07-10T08:15:00.000-04:00

Where Start-ups Get Stuck – and How to Avoid Going There

Between us, my long-time friend (and fellow blogger) Jim McHugh and I have started a lot of companies. We also advise many other companies and look at even more pitches from start-ups. A shared observation is that while a few start-ups shine (or at least glimmer) and go on to some success, other start-ups seem stuck before they start. Why?

Here are our observations on where start-up founders get stuck and our advice on how to prevent Stuck situations, presented Q&A style.

Q. Jim, where are the most common places you see founders getting stuck – and why?

In my experience, the two most common causes of becoming stuck are 1) an incomplete or muddled business model (see Stuck in the Fog) and 2) directly related to that, not clearly understanding the specific needs of their customers (see Stuck in a Rut).

Jim McHugh
McHugh & Company

What do I mean by an incomplete business model? A well-defined business model (i.e., the “guts of the business”) states how the company is organized; what products and services it sells to whom; and how the company “goes to market.” In addition, the whole company clearly understands the associated operational policies, processes and needs (both for the supplier and the customer).

Start-up and early-stage teams become obsessed (as they should) with the product/prototype, the team, and the market potential. However, they sometimes fall short in three important areas. First, they are naive about all aspects of the business model. Second, they don’t understand – or they have chosen to ignore – the specific linkages of their product to their customer’s product. Third, they have not solved or put in place key components of the business model and assume it will be easy to “finish those later.”

The business model can be pretty straightforward if it is a simple B2C or B2B connection – that is, “we make it, you consume it.” The linkages become more difficult to sort out if the start-up’s product becomes embedded in their customer’s product offerings – for example, as a component.

One striking example I have seen of an incomplete business model was a food ingredient technology company that had considerable success raising seed money from a group of angels. This company had traction:

● The technically elegant prototype demonstrated product effectiveness and potential significant benefits to consumers

● The company had received approval from a key regulatory agency

● The product was going to revolutionize one segment of the food industry

● There were positive (but limited) real-world test applications

● The expected market was huge

Then the company’s traction stalled; they became stuck. How could that happen?

This revolutionary product was not sold directly to end-user consumers. It was an ingredient that became part of other companies’ product formulations. The industrial and consumer target customers who evaluated the start-up’s product realized they would have to change their end-use product specs, add equipment and processes to their production line, and change their quality control testing procedures. They would also have to change their existing descriptive product information and packaging.

For some customers that would mean altering (for the better?) very successful, stable products that were established with consumers.

Were the customers prepared to take the market and product risk (with a start-up) and incur the costs and aggravation associated with adopting this new technology? Having a great product was a prerequisite for the big food ingredient companies, but it quickly became apparent to the start-up that many other factors influenced the prospective customers’ decision-making process.

Q. So, what’s your advice for avoiding getting Stuck before you Start?

To be sure, the business model for early-stage companies evolves over time. It gets fine-tuned, even changed. But fine-tuning is a lot different from having a naive view of the customers’ needs out of the gate.

It’s a cliché, but how many times does “knowing the customer’s needs” have to be said to founders?

Q. Any general tips about how to avoid getting stuck?

Yes, it really helps to have the right people on the team who understand and have experience in the industry they are selling into.

Q, Andy, where are the most common places you see founders getting stuck, and why?

Andy Palmer
Start-Up Specialist

I see a lot of founders get stuck at the very earliest stages – by being distracted by fundraising. I’ve said before – over and over again – founders should focus on developing their business first and not worry about fundraising nearly as much as they would probably like. It’s natural to be nervous when you don’t have any money in the bank. But it’s a healthy discipline to figure out how you are going to create value for customers who will pay you instead of spending time thinking about how to extract money from venture capitalists or seed investors. As an angel investor, I’m always looking for people who are mission-driven and focused on their customers, as Jim says, instead of worrying about what potential investors might think.

Q. So how can entrepreneurs avoid getting distracted by fundraising?

Just focus on your business and your customers. Wake up every morning thinking about how you are going to create value for your customers. Go to sleep at night considering which of your customers you helped that day and how. Be maniacally focused on your customers’ needs. It sounds simple – and it is – but executing this when you are starting from scratch – with no product, no credibility, and no people – is really hard. It requires all your energy and your concentration.

Q. Any final tips on how to avoid getting stuck?

Be maniacally focused on your mission, particularly understanding and meeting your customers’ needs. In my experience, the money will follow.

Medio and The Future of Big Data Analytics

2012-07-09T09:02:00.000-04:00

Why I Joined the Advisory BoD of Medio Systems

Today, I'm thrilled to announce that I've joined the advisory Board of Directors of Medio Systems.

Over the past nine months, I've had a chance to work with Brian Lent, Rob Lilleness and many of the other folks on the team at Medio. I've been approached by dozens of "Big Data" companies over the past two to three years as more people have begun to focus on large-scale database challenges. Medio is one of the few companies that I've chosen to work with directly because they're creatively tackling some of the most pressing challenges, including:

Big Data Analytics in Real Time: Over the past 10 years I've watched as database systems have become more and more "real time": as data is generated, users expect to see that data and be able to analyze it immediately. Medio has created a platform that enables its customers to analyze their data in real time without the daily-latency constraints of traditional data warehouse solutions. Incredibly powerful.

Mobile/Tablet Devices: As I sit here writing this blog post, my wife and father-in-law are playing Words with Friends on their respective iPads. Two years ago, my wife told me she would never use an iPad. Now, she's addicted. Mobile/tablet devices matter, and they generate a wealth of Big Data that can help businesses understand and get closer to their customers than ever before. The Medio team has uniquely deep experience with mobile app analytics.

Democratizing Analytics: As we were building Vertica back in 2005-2010, it became clear to me that once people had figured out how to represent their data more efficiently, the bottleneck would quickly become how to enable regular people (not just database experts or business analysts) to take advantage of the data and use the data plus statistics to ask complex questions and solve cool problems. Medio's focus has been on empowering business people with the tools necessary to make better business decisions, truly democratizing analytics.

Analytics Made Easy: I watched dozens of our customers at Vertica deploy solutions that required them to integrate database technologies with reporting and visualization tools and then tune/tweak those tools and the integrations. This turned out to be a never-ending problem that distracted them from the actual problems and questions they were trying to analyze. Customers didn't want to be experts at database systems or visualization: what they wanted were tools to help them answer business questions faster and more effectively. Medio has created the infrastructure that enables customers to focus on asking and getting answers to their questions without worrying about how the underlying technology works.

Predictive Capabilities: I strongly believe that analytic systems must have forward-looking/predictive capabilities. Just analyzing history is no longer enough. The next wave of analytic systems will empower users to anticipate what might happen in the future and model the potential impact of decisions they make today, leveraging history as the basis for those models. Medio CTO Brian Lent has been an early proponent and tireless champion of using information to help support decision-making with predictive capabilities. His vision and the technology the Medio team has created are unique.

To see for yourself, check out Medio. This is a company worth watching.

Enterprise Software Sales and Procurement Need a Makeover

2012-06-28T08:11:00.000-04:00

Time to Tie Payment to the True Measure of ROI: User Adoption

Today, I believe that the only way for IT organizations to truly measure their effectiveness is by analyzing the usage patterns, satisfaction and productivity of their customers: end-users. Period. Any IT organization that doesn’t use this kind of measurement won’t be successful over the next 10 years. As David Sacks, the founder/CEO of Yammer, has said: “Voluntary adoption is the new ROI.”

One of the things that bugs me the most about the software industry is the sub-optimal behavior that exists between vendors and buyers. Software buyers and vendors are engaged in a dysfunctional dance that wastes money and stifles innovation – on both sides. This dysfunction is driven by outdated software business models, and further complicated by non-technical sales and procurement people whose identity is tied to controlling the buying process instead of optimizing value created for end users.

Simply put, times have changed dramatically; enterprise software sales and procurement haven’t. Here are the new realities, as I see them. (Most of these realities also apply to how software is sold to and purchased by federal and state governments.)

12 Realities That Vendors and Buyers Can Ignore – But at Their Peril

1. The pressure of the “consumerization of IT” is game-changing. This pressure is coming down on enterprise IT organizations, although most still have their heads in the sand. This pressure is going to get acute over the next few years as the gap widens between what is available to the average consumer on the Internet and what an employee’s IT ecosystem provides at work. This gap will reveal just how wasteful and disconnected most IT organizations are from end-users’ real needs.

2. Buyers purchase a ton of software that they don’t ever use, much less realize value from. The enterprise software emperor has no clothes.

Buyers need to step away from the standard perpetual license agreement. Just put it down... and pick up a subscription/term agreement: it will be okay, really. Businesses haven’t imploded because they don’t use perpetual agreements. Short-term, subscription-based agreements are working great for all those Google Enterprise and Salesforce.com customers who have month-to-month agreements. Subscription/term agreements help ensure that buyers don’t waste bazillions of dollars buying software they don’t use.

Now, the finance dudes will come in with their spreadsheets and models that show how you can finance perpetual agreements at a lower cost of capital. Don’t believe them. What these models don’t take into account is one important fact: that if you pay a vendor a bunch of money for a product that is supposed to do something and the vendor isn’t on the hook financially for this, then the vendor probably won’t do it. There is little incentive for the vendor to ensure that the software is deployed, adopted and improved over time. The vendor (particularly if it’s one that’s oriented toward short-term goals) will likely take the money and run. Not necessarily because they are “bad people” but because the business (like everyone else) is under pressure to deliver more, faster, better.

3. Vendors sell a ton of software that is never deployed (see#2). And many of them don’t care. As their businesses have matured, many of these vendors have sacrificed their souls to short-term thinking and financials. They are no longer driven by missions to deliver value or great experiences for end-users. By comparison, the consumer Internet companies that have moved into the enterprise software market have used alternative models and behaviors and begun to disrupt the ecosystem. Those customers that have embraced new usage-oriented models have benefited significantly. Those customers that haven’t are just wasting money and causing their end-users to continue to suffer with outdated and expensive technology that preserves the short term job security of narrow minded IT staff members.

4. Software vendor sales people and customers’ procurement staffs are disconnected from how software is used and developed in the enterprise. Generally, neither procurement folks nor salespeople understand the technology or how it’s used. They are managed to objectives that have nothing to do with successful deployment, much less adoption of the software or technology. Procurement people generally care about discounts and sales people care about commissions. It’s time to get these folks out of the way or give them incentives that align with effective adoption and value created for end-users. One of the powerful benefits of SaaS is that end users can buy their own capabilities and just expense the cost. This is how many customers start with Salesforce.com, and I’ve seen this same adoption pattern occuring in infrastructure at companies such as Cloudant.

5. Software that sucks. There’s a disconnect between the amount of money that big companies spend on software and the value they get from it. Many big companies only resolve this disconnect over long intervals, after tons of money has been wasted on useless technology projects that aren’t aligned with users’ requirements. Moving toward shorter-term subscription models helps reinforce the need for software companies to create value within reasonable periods of time. In other works, deliver software that doesn’t suck.

Part of the reason corporate IT projects take so long is that businesses don’t push their vendors to deploy quickly or drive adoption. Here’s an example, from the Front Office/Customer Relationship Management sector of the software industry. Siebel Systems launched its system in the early to mid-1990s using the traditional third-party installed and heavily configured model. (I suspect that the rationale was: “It worked for ERP, so let’s do the front office the same way.”) Unfortunately for Siebel, things didn’t play out this way. As Salesforce.com launched, customers realized that they could get immediate adoption, usage and value by just signing up for the Salesforce.com service. These customers perhaps didn’t get all the customization that usually came with traditional enterprise software, but most of that customization was being sold to big companies by consultants who wanted to make money as part of the enterprise IT ecosystem. A lot of smaller customers didn’t need the customization, and ending up paying for overhead that they didn’t need.

A general rule of thumb for IT organizations was that you had to spend an additional 2X-5X in services to get a third-party enterprise software application deployed and working. This never made sense to me, but I participated in the dysfunction along with everyone else for many years, on both the buyer side and the sales side. As this thinking became more broadly accepted, it became a self-fulfilling prophecy: vendors could make money customizing the solutions for customers (regardless of their actual need for the customization), so it was in their best interests to create software that required a bunch of consulting to get it working for customers. (Software that sucks, in the classic sense of “suck”: time, money, corporate IT resources.)

With the evolution of SaaS, all vendors now face more accountability, like it or not. Salesforce.com knocked it out of the park as an independent business while Siebel sold out to Oracle and has bounced along the proverbial third-party software bottom, collecting maintenance on software that they sold 10 years ago to big companies who are not capable of switching to Salesforce.com.

6. Traditional business models encourage vendors to extract as much money from their customers as quickly as possible – regardless of whether the software works or the customer actually needs the software. During the 1980’s and 1990’s, this “sell first, ask questions later” model became standard practice for technology companies, based on the success of proponents like Oracle. But now, we’ve evolved. Customers shouldn’t stand for it. There are better alternatives. And vendors in just about every enterprise-software category should realize that it’s only a matter of time before someone comes along and provides better solutions that work for users quickly. Let the hangover of enterprise software purchases begin.

7. The perpetual-license model creates perverse incentives for both buyers and sellers. The subscription/term license model creates a much more rational incentive for the seller of technology to deliver both short-term value (through adoption) and long-term value (through improvement to the software) for customers’ end-users. With a perpetual license model, the seller gets too much value up front, misaligning his interests with those of the buyer.

8. Traditional “maintenance” is just as dysfunctional as the perpetual license that it stems from. Fifteen percent (15%) maintenance is not enough money to innovate and improve a new system. Therefore, vendors’ business models put them in a position where they have to “upsell” their customers’ perpetual licenses for some additional usage or a new product.

9. Many customers should be happy to pay larger subscription fees over time in exchange for significant probability of greater success, user satisfaction and innovation. They just don’t realize this, because business owners, end-users and engineers aren’t involved in procurement processes. This perpetuates a lack of accountability for vendors and feelings of helplessness among users and consumers of these software systems.

10. Multi-tenant Web services present a compelling alternative. The broad availability of commercial multi-tenant hosted web services (epitomized by Amazon Web Services and GoogleApps) is creating a widening gap. On one side of the gap, there are buyers and sellers of software who are merely perpetuating outmoded models for consuming and selling software. On the other side of the gap, are software buyers that demand that their vendors deliver value through reliability and innovation every single day – and have the means to measure this.

11. FUD continues to rule – for now. Many of the procurement and sales establishment are using the FUD (Fear Uncertainty and Doubt) arguments to slow the adoption of new software-as-a-service models. I can understand why: the new business models including SaaS challenge their very existence. However, as a result, their customers are saddled with a sub-optimal state of productivity for their IT systems and infrastructure. This is not sustainable as IT organizations are under dramatic pressure to reduce costs significantly.

12. IT organizations that embrace new software models are more productive and efficient. They can focus more on high-leverage skills like networking and integration – and worry less about lower-value activities such as racking and stacking servers or building and releasing software. These benefits have been documented among the likes of Google Apps enterprise customers (Genentech for example) as well as large companies that have embraced Amazon Web Services (Netflix for example).

I believe that all software contracts should tie payments to end-user adoption. Monthly software subscription deals can be used to accomplish this relatively quickly: if users adopt. you pay; if they don’t, you don’t pay.

For software industry old-timers this is heresy. But it’s time to leave this one in the rear-view mirror – or eventually suffer the consequences. The packaged third-party software industry is due for a reckoning - it's time for vendors to modernize their business models which depend on bilking customers for perpetual licenses and maintenance streams on software that is never used. And customers should start buying software as a service and not overpaying for big perpetual licenses that they may or may not ever use.

Spend More Time on Your Mission - The Money Will Follow

2012-06-05T08:30:00.000-04:00

Time – and how you use it – is the most important consideration for a Founder. This is one of the most important lessons I’ve learned in my 20 years as an early employee, founder, and CEO of start-ups.

When you’re starting a new company from scratch, time is your most precious resource because it’s so scarce. Initially all you have is yourself and one or two partners who are preferably (at least in my case) a lot smarter than you.

If you believe, as I do, that your success is determined by how you spend your time, then start by spending it on your mission and the experience of your customers – not on money. Randy Komisar at Kleiner Perkins likes to talk about “mission-driven” founders and start-ups. I like that phrase a lot. And I believe that you need to embrace the concept from Day One – by being mission-driven in how you allocate your time.

Spend your time talking to customers, recruits, and partners; not with consultants or financiers – it’s almost a complete waste of your time.

As money for early-stage companies increasingly becomes a commodity, entrepreneurs’ success will be determined more by their ability to create a great business – and less by their networks and credibility with a small number of professional early-stage investors (who control the purse strings granted to them by a small number of relatively disconnected Limited Partners).

I’m not saying that early-stage/venture investors don’t add value. Specific people absolutely do add value and are important to the success of many companies. However, early-stage/venture investors shouldn’t be the primary focus of your time – and the good ones don’t want you to focus on them anyway. They want to help you build a great business – to put the priority of your time ahead of their own. The ability of a venture investor to prioritize the time of the entrepreneur ahead of their own time is a primary test of a great investor.

The best venture investor partners are those who embrace modesty as a primary quality: in other words, they exist to help make the company (and by definition the founders) successful. In interviews, Peter Barris of New Enterprise Associates has specifically and frequently cited modesty as a primary cultural dynamic at NEA. I've experienced this first hand working with many partners at NEA – especially Harry Weller and Tom Grossi –and I believe it is a key component of NEA's ability to scale successfully. John Lilly at Greylock is another great example of someone who demonstrates this type of modesty – putting the entrepreneur first.

Often, the problem for Founders and venture partners is conflicting objectives relative to their time.

As a company Founder, your goal is to get the best possible return on the time you spend achieving the mission of your company. For many venture investors, the goal is to meet with as many prospects as possible because their limited partners are essentially paying them to talk to people and gather information that the partners can use to make optimal investment decisions. This behavior is particularly true of younger, less-experienced early-stage investors, who have lots of time to spend. Some young venture investors may meet with you even when they have no money to invest. Talk about a waste of time – it happens more than anyone would ever admit.

You may ask: “But don’t I get value from every meeting with investors? Even if the meeting doesn’t result in an investment, won’t I get useful free advice?”

This is a serious dysfunction, especially for first-time Founders. Sure, some of these folks can provide valuable advice, but to be mission-driven, it's much more important to focus on your customers, the people working for you and your business.

The nature of the venture capital business is that it’s populated by a lot of people with relatively large egos. In my experience, such people are prone to radically over-estimate the value of the time they “invest” in your business. They do get a lot of data from a broad variety of sources – they get paid to meet with lots of companies ;) So if you're looking for a lot of data – great, go out and ask them a ton of questions. But be specific about what you are trying to get out of the interaction instead of just “catching up.”

The best investors will want to get to decisions quickly and not waste time – yours or theirs. They recognize that their success as investors will depend on how wisely you spend your time to build a great business in a short period of time. In my experience, the best investors almost always start their side of the meeting with "How can I help?" not "You should think about...." They arrive prepared, they listen, they help and they give you decisions very quickly.

So, given all the "red flags" I talked about above, how do you find the best prospective investors and avoid wasting your time?

Time-Saving Tips for Dealing with Investors

Before you meet with any investor, try to qualify the person and the firm. Sometimes this is difficult because of lack of transparency in the venture business.

Fortunately, transparency is steadily improving. Be sure to read the Kauffman Report, which does a great job moving us toward transparency for early-stage investing. Kudos to the Kauffman team.

It's somewhat ironic that so many venture investors (who profess so much faith in capitalism and free markets) have worked so incredibly hard to limit transparency, both at the micro and macro level.

Who are the actual top venture firms by return?
Who are the partners who have created value for common shareholders vs. those that jump at the chance to dilute common shareholders at every opportunity?

This kind of information has traditionally been hard to find, even by doing primary research within entrepreneurship inner circles.

Here are some other to-do's:

Look up the investors on The Funded. Get every source of information you possibly can about not only the firm – and the culture of the firm – but also the PERSON who you are taking money from. Relentlessly pursue personal and professional references.

Before you agree to meet, ask them the following questions:

How much of your existing fund(s) is still available to invest?
When are you planning to raise another fund and what is the target size?
How many investments have you (firm and partner) made in the past month, quarter, year?

Also, ask yourself: “Who do I trust that has had experiences with the firm/partner?” Try to have candid conversations with those people. True character in early-stage companies is measured by what people do during the worst of times and the best of times. This is where you see their true values reflected in their actions – you want to know that they will do when it looks most grim or when there is a ton of money on the table. Those who will support the mission of the company in either of those cases are the people you want to work with. Empirical evidence is always telling.

If you do meet with investors and they say anything other than “absolutely yes – we want to do this deal ASAP – here is a term sheet or I will have a term sheet to you within X days,” you should interpret their response as essentially a “NO.”

Great companies are built by people (including investors) who are fiercely mission-driven. If external financing is required (which is far less often than most entrepreneurs realize) – make sure that your investors believe that great companies are defined by their ability to create value for ALL shareholders through the achievement of the companies' missions – not a quick flip to pump up the value of a particular venture fund. This is a long-term view that is all too rare among venture capitalists, but a key attribute of the most consistently successful early-stage investors.

Transparency: It’s About Time

Fortunately, there is a massive culture change brewing in early-stage investing (thanks again to the Kauffman team and many others). The potential of democratized early-stage investing is becoming obvious: combine AngelList with the potential impact of the Jobs Act. Entrepreneurs are realizing that it’s their time that’s precious (to themselves and their future investors), NOT the time of the investors. This has always been true among the best companies and the best people at the high end of the start-up market, but it’s now coming downstream. Hallelujah.

So, spend your time pursuing your mission, developing your idea and creating your technology. Spend it with potential customers, turning them into real paying satisfied customers. If you do this well enough, and are smart, disciplined and mission-driven, there will always be capital available for your company.

Finally, if you’re wasting time thinking “But I have a pit in my stomach because I can’t pay my mortgage,” stop. This is how it feels to take risks. It’s painful but it WILL make you stronger as a person (insert Nietzsche cliche). It will make you stronger as a role model for potential employees and customers, who will respect your commitment and sacrifice in the interest of achieving your mission. And it will make you more attractive to the right kinds of investors, if and when you need them.

Enterprise Software CEOs Need a Dose of “Undercover Boss”

2012-05-25T07:30:00.000-04:00

Connecting with the Disrupted Customer

What if enterprise software company CEOs were asked to do what other CEOs are doing on the TV show "Undercover Boss": to do the heavy lifting that their employees are doing every day? (It always makes me smile when I imagine Larry Ellison trying to write SQL.)

I think we'd find that there are very few software CEOs who appreciate what’s involved in developing or deploying their technology into the hands of customers. (Jim Goodnight at SAS may be a notable exception).

Most software CEOs can probably do what the average enterprise software salespeople do: sit in meetings, make phone calls, and invent new PowerPoint charts. But how many could actually put their hands on a keyboard and write code? Or even just configure their company’s products in order to create value for their end-users? Although watching people – even great engineers – write code and run build-scripts isn’t quite the same as watching some executive load a garbage truck with dirty diapers during primetime.

Obviously, I am having some fun here: programming is probably not required for running a multi-million-dollar or billion-dollar software company. But I'd go as far as to say that it's highly desirable for the CEO of a software company to understand the engineering of her/his company's technology. Furthermore, knowing your customers (mostly engineers) and their needs is absolutely a requirement. And, in general, it’s often incredible to me how disconnected many technology company executives are from what their products actually do for their customers’ end-users.

With the rapid maturation of the enterprise software industry over the past 10 to 15 years, the “nobody ever got fired for buying IBM” excuse is going away. When buying enterprise software, customers today have many options and often radically reduced switching costs. They also have the ongoing pressure to improve efficiency of both capital and operating costs.

Meanwhile, corporate end-users are asking themselves and their managements: “Why can’t the systems that I use at work be as easy to use, simple and productive as the systems I use at home?”

Good question. If your company has decided to use Google Enterprise Apps, you have an advantage. The capabilities you have from Google will improve at the pace that technology moves on the consumer Internet – not at the pace that your internal IT organization moves. Many users who are frustrated with their captive IT organizations just pull out their AMEX cards and pay for cloud-based services that do the job without involving requiring a call to anyone in IT. This is how Salesforce.com got its start and how systems such as Expensify today are replacing traditional enterprise expense management tools. (Expensify has the best tag line in the world, in my opinion: “Expense reports that don’t suck!”)

If you work for a traditional enterprise software company, the consumerization of IT should be a huge wake-up call. If you are the CEO of one of those companies, I'd suggest embracing a model other than "milk my customers for maintenance revenue."

Even if you don’t expect a software company CEO to write code on national TV, it’s a good idea for her/him to get together with end-users who are actually using the software (or, more worrisome, not using it). Then, he/she will have direct information on how good (or how bad) the software actually is and can make the decisions necessary to fix it quickly if necessary. Start-up CEOs have the advantage of being closer to their customers, for both constructive feedback and complaints. A little start-up thinking could go a long way for most large enterprise software companies.

But I’d still like to to see the CEO of a large enterprise software company write some code ;)

Many Small Stock Grants Over Time Should Be the Rule in Start-Ups

2012-05-22T08:30:00.000-04:00

An Alternate Approach

In my last post, I wrote about how important it is for founders to take a proactive and decisive approach to crafting their ownership models – to the extent of being a control freak about it. Getting the correct ownership model in place up front is one of the more important decisions you will make as a founder, and it will guide you through key hiring decisions.

And it doesn't matter how many or how few employees are involved* – it's still important. Do you grant stock up front to new employees? Or do you delay the grants (either through vesting or follow-on grants) over some period of time? There are costs and benefits to various approaches, and many different mechanisms for executing any given approach. Having a great start-up lawyer is key: the best folks I've ever worked with are Mitch Zuklie at Orrick and Marc Dupre at Gunderson.

"Front-loading" stock options – granting large options up front to key employees – has been a popular approach in the past for many start-ups. But I've come to believe that a better approach is to give smaller stock grants up front (with relatively standard vesting of four years total with one-year cliff, then monthly for the last three years); followed by many small grants over time, at least annually but preferably every six months or so.

When you grant options in this way, you essentially create a "ladder" of stock vesting that helps ensure that the rate of vesting for any given highly valued employee is going up consistently during that employee's first five to ten years at your company.

Of course, you can always make exceptions, giving them a large up-front grant. However, I believe that such grants should be the significant exception rather than the rule.

Here's my rationale.

How It Works and Why

With a laddered approach, you're focusing on the RATE of vesting, not the size of the initial grants, to increase the likelihood of retaining superstars over time. Key employees who you want to reward and retain for the long haul enjoy an ever-increasing RATE of vesting: the number of options that vest each month or quarter after they pass their cliffs. This also has the benefit of minimizing the impact of bad hires, who – assuming you are doing your job – will leave the organization before they vest too much and/or will not receive follow-on grants.

The laddered approach has a powerful psychological impact on the employees. They're getting regular feedback on their performance. They FEEL appreciated on a regular basis and their efforts are recognized by the company through ownership, the most valuable form of compensation in mission-driven startups. The sacrifices they are making are often “unnatural acts” of saying no to family, friends and personal comfort in the interest of doing “whatever it takes” to make a company successful. Being recognized with more ownership over time, in my experience, means more to the average start-up employee than most investors can ever appreciate.

Another benefit of this approach is that it can protect you from a common pitfall: inadvertently selecting people who are great negotiators but perhaps not as good at actually doing the work and earning the stock. For example, sales-oriented people tend to view the negotiation as an end in itself, while engineers tend to view the work as what determines value and earns rewards. By appealing to mission-driven people who know that there is more ownership available to those who perform, you'll attract people who are confident and willing to prove themselves and for whom the mission of the company is enough to get them on-board initially.

Some Downsides

This approach does have some downsides. It can require a lot of administrative support and careful expectation-management so that employees don't expect both frequent grants AND large grants regardless of company and individual performance.

Another potential downside is that the strike price of options or the purchase price of restricted stock increases over time. So, the weighted average price for the individual is higher – sometimes dramatically higher during the first five years of a new company’s life cycle. This can be especially hard if you are allowing your employees to exercise their stock early. However, I suspect that most companies would be more than willing to compensate for this by granting a larger quantity of stock for the ultra-high performers. It’s a net-positive-value decision to give top performers a disproportionate amount of stock. The basis for their retention compensation is that they create more value than other people. Therefore, locking them in with more stock is equivalent to fundamentally increasing the value of the stock.

Ultimately, I believe that this approach actually costs the company less in the end.

I believe that the practice of large initial stock grants is nothing more than an artifact – and one that doesn’t optimize growth for the company or the investors. Let’s bury this practice once and for all and use it only as an exception not a rule.

* Except of course for the extreme case where no one gets any ownership other than the founder. If you are looking for examples of those, there are plenty - for example, Kenan Systems and Ab Initio in Boston or Trilogy in Austin.

Beware the "Accidental" Ownership Model

2012-05-14T18:56:00.003-04:00

As a Founder, You are the Master of Your Domain

Over the past 20 years, I’ve participated in many start-ups, both bootstrapped and venture-backed. During that time, I’ve seen many approaches to start-up employee ownership across a broad spectrum of philosophies – from radically low/no employee ownership (beyond the founder/founders) to ultra-high employee ownership (where many or all employees are treated as owners). At Infinity, we referred to this broad form of ownership as "Citizen Ownership."

I’ve also experienced the best and worst of how ownership can change over time in early- stage companies: from too few people owning too much of a company, all the way to people who contributed little or nothing to the company's success owning a large stake. In between, of course, is a broad spectrum of ownership, which is where most companies end up.

I believe that it's very important for founders to (1) consciously and proactively decide up front their aspirational ownership model and (2) plan how to achieve that model using specific corporate and legal mechanisms to execute it. Don't let your ownership model happen accidentally. This happens - and more often than you would think.

Part of your responsibility as a founder is to protect the interest of early-stage common shareholders from the nature of capitalistic individuals who are not capable of starting companies themselves and/or are just trying to make a $ and don't care about your mission. If you as a founder aren't looking out for the interest of the common shareholders, it's possible that the people who put in the hard work, sacrifice and commitment to start the company from scratch may be pushed aside by investors or late-comers who desire to capitalize on others' hard work.

Just to be clear: I’m not advocating that founders be greedy and not share ownership with employees (or investors) who come in at later stages when new skill sets are required. Quite the opposite: I believe sharing ownership with those who come in later in your company's life-cycle is essential for most entrepreneurs to be successful - I've done this consistently in companies that I've helped start and it has often worked well. It's critical to embrace the reality that few people are wired like Bill Gates or Steve Jobs and have the skills to lead a company from founding all the way through world domination.

However, I am advocating that founders be VERY disciplined in ensuring that they protect their own interests and the interests of other early-stage employees, particularly engineers. If you are successful in your start-up's mission, many people will come along who want to capitalize on your hard work. Sharing your ownership with the right people can create a lot more value as you grow.

So, what's the best ownership model?

Over the past 20 years, I’ve concluded that there is no single ownership model that works broadly across all different types of companies. Founders must match their companies’ ownership models to their management philosophies, as well as their business goals and their expectations for their companies.

There are as many ways to successfully configure a cap table as there are entrepreneurs. But the end-game configuration that hurts the most is the one where you feel like you've given too much ownership to people who don't deserve or appreciate their ownership in an entity that you started from scratch and that you believed in when no one else did.

Unfortunately, all too often I see first-time founders accept outmoded or antiquated ownership practices and beliefs that may or may not have worked in another situation/project/company: "In our companies, Founders get X%, Investors get Y%, post-founding employees get Z%." Often these ownership practices are driven by investors' “models” for how they would like to run their funds: essentially as a series of relatively homogeneous ownership structures that minimize cost and complexity for the fund and help them reduce risk.

I’m not blaming the investors for trying to reduce cost, simplify their businesses and minimize risk. However, I've observed that often, the most successful companies – those that venture investors strive to fund (call them “The Fundable” ;) ) – have strong founders who are deliberate in terms of the ownership models that work for them and their new companies.

The impending Facebook IPO is a strong reminder that founders can and should pursue ownership models that work for them and their companies, regardless of how different it might be from current venture capital ownership dogma. No matter what investors say, they make all kinds of exceptions to most of their “rules,” all the time. As a founder, you may or may not have the leverage required to trigger their exceptions. If potential investors say “We never do X,” what they usually mean is “You don’t have enough leverage to make us do X.”

One interesting example of ownership innovation is the “Founder Preferred” model, which has become accepted in Silicon Valley. Unfortunately, this model has yet to become broadly used in smaller, more provincial markets such as Boston/Cambridge, Seattle and Austin. It’s hard to believe that such basic practices have not yet been widely adopted, but I hope we will begin to see this happen.

The recent improvement in the economy has reinvigorated founder confidence, and increasingly founders have the benefit of improved transparency from sources such as “The Funded” and publicly available templates that enable founders to compare deal structures and terms proposed by their potential investors to those of other founders. Let's hope that a successful Facebook IPO will help validate the benefits and integrity of some of these innovative mechanisms that benefit entrepreneurs and thus further encourage even more entrepreneurship/start-ups.

In my next post, I'll talk about one very important facet of ownership when starting your company: how to optimally structure options grants for early employees.

Building an Analytics-Driven Culture

2012-04-19T10:46:00.000-04:00

Turning Big Data and Big Analytics into Business Opportunity

If you haven’t read my friend Tom Davenport’s book Competing on Analytics (Harvard University Press, 2007), you should. If you have read it, it’s a really good time to read it again.

Why? The Big Data revolution. Or I should say, the Big Analytics revolution. (BTW, I think that "Big Analytics" isn't a great term either. But what the heck, let's just make it easy for the marketing folks to transition from Data to Analytics by using the same adjective ;)

In his book, Tom talked about organizations that were using analytics – analyzing massive amounts of data – to gain a real competitive edge in their business performance. Practitioners ranged from health care organizations and pharmaceutical companies to retailers (such as Best Buy) and the entertainment industry (Harrah’s Entertainment, whose CEO Gary Loveland, an MIT graduate, wrote the foreword to the book). And we can’t forget the sports teams – not just the Oakland A’s, famously portrayed in Michael Lewis’ book and then the movie Moneyball, but our own Boston Red Sox and New England Patriots. Professional sports is being transformed radically by analytic tools, techniques and culture.

Today, I think we’re on the edge of a secular shift in business: the ability of virtually any business – not just large and technically sophisticated businesses with big budgets – to get a competitive edge by using analytics every single day.

Big Analytics for the Rest of Us

Big Data is only part of the story. What matters more is what you do with Big Data: Big Analytics.

Big Data is a fact of life for almost every company today. It’s not something you run out and buy. You already have it, whether you want it or not – or whether you know it or not. It’s the large quantities of data that companies accumulate and save daily about their customers, their employees and their partners; plus the vast corpus of public data available to companies elsewhere on the Web.

In years past, the constraints of traditional database technology – such as first-generation relational database architectures and the outdated business models of the companies who commercialized these systems – made it difficult and expensive for people and organizations to store and access such volumes of data for analysis. Today, the popular adoption of innovative technologies such as the Hadoop distributed data file system (HDFS)/MapReduce, as well as many other "built for purpose" database systems enable data to be aggregated and available for analysis cost efficiently with extreme performance. (BTW, I believe that Hadoop/MR is way over-hyped right now: it's great and very useful, but only one piece of the Big Data/Big Analytics puzzle.)

Some of the innovative products/companies that I've had the privilege to be a part of include:

There are MANY others – which is good database innovation mojo – especially compared to seven years ago when Mike Stonebraker and I started Vertica. At that point, the standard response from people when I said I was working with Mike on a new database company was "Why does the world need another database engine? Who could possibly compete with the likes of Microsoft, IBM and Oracle?" But the reality was that Oracle and the other large RDBMS vendors had significantly stifled innovation in database systems for 20+ years.

Jit Saxena and the team at Netezza deserve huge kudos for proving that, starting in the early 2000s, the time was right for innovation in large-scale commercial database system architectures. Companies were starved for database systems that were built for analytical purposes. I'm not a fan of using proprietary hardware to solve database problems (amazing how quickly people forgot about the Britton Lee experiment with "database machines"). But putting the proprietary hardware debate aside, thanks to innovators like Mike Stonebraker, Dave Dewitt, Stan Zdonik, Mitch Cherniack, Sam Madden, Dan Abadi, Jit Saxena and many others, now we're well on our way to making up for lost time.

Some other database start-ups of note include:

NuoDB, Jim Starkey's company
Akiban Technologies
ParElastic
Hadapt, Dan Abadi's company
Basho, the makers of Riak
10Gen, the MongoDB company
Cassandra, not really a company (yet, I think) but a viable key value store

There are many new tools out there for managing Big Data, and new innovations are being delivered to the market every month, from big and small companies alike. I've actually been impressed with the progress that Microsoft has made with SQL Server of late, mostly driven by Dave DeWitt, PhD, and his new MSFT Jim Gray Systems Lab at University of Wisconsin. Most business folks don't realize that many of the technical principles behind systems at Teradata, Greenplum, Netezza and others were based on innovations such as the Gamma parallel database system as well as the dozen+ systems that Mike Stonebraker and his vast network of database systems researchers have been churning out over the past 15+ years.

The challenge now for most commercial IT and database professionals is the process of trying to match the right new tools with the appropriate workloads. If, as Mike and his team say in their seminal paper "one size does not fit all for database systems," then one of the hardest next steps is figuring out which database system is right for which workload (a topic for another blog post). This problem is exacerbated by the tendency to over-promote the potential applications for any one of these new systems, but hey, that's what marketing people get paid to do ;)

Until just recently, however, another key element that has been missing is the focus on how data is going to be used when people implement their Big Data systems. Big Data is useless unless you architect your systems to support the questions that end users are going to ask. (Yet more fodder for another blog post.)

For many decades, there was no open, scalable, affordable way to do Big Analytics. So the kind of capabilities that Tom Davenport talks about in Competing on Analytics were available only to companies with huge financial resources – either to pay companies like Teradata (which is where the Wal-Marts and eBays of the world ended up) or hire tons of Computer Science PhDs and Stats professionals to build custom stuff at large scale (Google, Yahoo, etc.) The analytics themselves were even further restricted within those companies, to professional analysts or senior executives who had staff to make the results of these analytics digestible and available to them. These capabilities were kind of a shadow of the Executive Information Systems trend in the 1980s.

Today this is changing. Established companies and start-ups are creating technologies that “democratize” Big Analytics, making large-scale analysis affordable for even medium-sized businesses and usable by average people (instead of just business analysts or professional statisticians). A great example is Google Analytics. Ten-plus years ago, the kind of analytics you get today with Google Analytics were only available to Webmasters who had implemented specialized logging systems and customized visualization. Now, my son Jonah gets analysis of his Web site that would have cost big bucks a decade ago.

However, there are still missing pieces. I believe we need:

Large-scale, multi-tenant analytic database as a service, similar to Cloudant and Dynamo but tuned/configured specifically for analytical workloads with the appropriate network infrastructure to support large loads
Large-scale, multi-tenant statistics as a service – equivalent functionality to SPSS, R, SAS, but hosted and available as an affordable Web service. The best example of this right now is probably Revolution. I guess the acronym would be Statistics as a Service – or Statistics as a Utility
Radically better visualization tools and services: I think that HTML5 has clearly enabled this and is making tools like Ben Fry's Processing more accessible so that the masses can do "artful analytics"

Once analytic databases and statistical functionality are available as Web services, I believe we'll see the proliferation of many new affordable and sophisticated analytic services that leverage these capabilities. One of the best examples of this that I’ve been working on is a product created by Recorded Future.* I believe it is one of the most advanced analytics companies in the world. Christopher Ahlberg, Staffan Truve and the entire amazing team at Recorded Future are making some of the most sophisticated analytics in the world available to the masses. Another example in Boston is Humedica, where my friend Paul Bleicher (founder of Phase Forward) is doing fantastic work – perhaps some of the most advanced health care analytics in the world.

At this point there are still significant technical hurdles. But in the very near future, the challenge for most companies won’t be technology: it will be people, especially those who will no longer be limited to static reporting.

To be successful, businesses will need to build analytics-driven cultures: cultures where everyone believes it's his job to be information-seeking and to think analytically about the integrated data that can help him make better decisions and move faster every day.

The #1 Step Toward Building the Right Culture

So, how do you go about building an analytics-driven culture?

This is obviously a long discussion; Tom’s book is a great primer on this, particularly Chapter 7 where he points out that it’s analytical people that make analytics work. But I think that the most important step for businesses is to rethink the way we build systems and to respond to the call to action created by the “consumerization” of information technology in the enterprise.

Every day new analytic tools are being made available to consumers over the Internet. Those consumers then walk into their workplaces and are faced with a pitiful cast of static, mundane and difficult-to-use tools provided by their IT organizations – most of which are 10+ years behind on analytics. (Sorry if it hurts to hear this, but it's true – and I include myself as one of the people who is under-delivering on meeting enterprise end users' analytical expectations).

To build analytics-driven cultures, businesses need to shift IT’s emphasis from process automation/"reengineering" (popularized by the late Michael Hammer and others) to decision automation. With process automation, the average worker is treated as a programmable cog in a machine; with decision automation, the average worker is treated like an individual and an intelligent decision point.

This isn’t New-Age management theory or voodoo; there’s already a good track record for it. Southwest Airlines empowers its gate agents do things that gate agents at American Airlines can’t even think of doing. Ditto for Nordstrom and Zappos in retail, where sales representatives have broad discretion on how they satisfy each customer. These companies believe in the individual identity of every person in that organization and use data and systems to empower them. (The antithesis to these companies are companies that are stuck in post-industrial employment models – most amusingly like Charlie Chaplin’s employer in "Modern Times" [watch]. And, yes, such companies do exist even today – otherwise we wouldn't have shows like "The Office" or movies like "Office Space.")

Unfortunately, the technology people in most companies don’t think this way, and most of their vendors are still stuck in the process automation mindset of the 80s and 90s. If companies thought of their competitive edge as being decisions, they would expect their systems and their user experiences to be radically different. In some ways, this is the process of thinking about an organization's systems in context of how data is going to be used/consumed instead of how the data is being created. Because we tend to build systems with a serial mindset, many systems in today's organizations were built to "catch" the data that is being generated. But the most forward-thinking organizations are designing their systems from the desired analytics back into the data that needs to be captured/managed to support the decisions of the people in their organizations.

Ironically, there are a bunch of us artificial intelligence (AI) people from the 1970s and 1980s who experienced a technology trend called expert systems. Expert systems really involved taking AI techniques and applying them to automating decision support for key experts. Many of the tools that were pioneered back in the expert systems days are still valid and have evolved significantly.

But we need to go one step further. Today, consumer-based tools are providing data that empower people to make better decisions in their daily lives. We now need business tools that do the same: Big Analytics that enable every employee – not just the CEO or CFO or other key expert – to make better everyday business decisions.

Here’s one great example of what can happen when you do this.

Over the past decade, synthetic chemists have begun to adopt quantitative/computational chemistry methods and decision tools to make them more efficient in their wet labs. The use of tools such as Spotfire, RDKit and many others have begun to change the collaborative dynamic for chemists, by enabling them to design libraries using quantitative tools and techniques and perhaps most importantly to use these quantitative tools collaboratively.

It’s very cool to see a bunch of chemists working together to design compounds or libraries of compounds that they wouldn’t otherwise have created. Modern chemists use their remarkable intuition along with incredibly powerful computational models running on high- performance cloud infrastructure. They analyze how active or greasy a potential compound could be or how soluble, big, dense, heavy or how synthetically tractable it might be. Teams of chemists spread across the globe use this data to make better decisions about which compounds are worth synthesizing and which are not as they seek to discover therapies that make a difference in the lives of patients.

This is where the magic comes from – from being decision-oriented not process-oriented. Big Analytics can make average people junior artists – and natural artists wizards – by giving them the infrastructure to make sense of data and interact with people. It makes art and magic more repeatable.

Google Analytics is a good example of what happens when a business adopts an analytics-driven culture. By using Google Big Table with the Google filesystem, Google expressed the value of its analytics in a way that could be given to anyone who manages a Web site. Google then watched the value of the analytics get more rich, statistical, analytical.

I predict that this is what will happen in the rest of the business world as Big Analytics takes hold and analytics-driven cultures become more the norm – and expected of every enterprise system.

I have seen what can happen close up, many times. As the co-founder of Vertica, I was fortunate in having Mozilla and Zynga as two of our best early customers. Zynga thought of itself as an analytics company first and foremost. Yes, their business was providing compelling games, but their competitive edge came from making analytics-based recommendations to their customers in real time within games and about where to place ads in their games. Another company that I work with closely that is providing this type of capability is Medio Systems.* Companies like Medio are democratizing Big Analytics for many companies and users.

By rethinking how you build systems – within the context of how the data in the system will be analyzed/impactful, and thinking of every person in the company as an intelligent decision point – you’ll smooth the path to Big Analytics and 21st-century competitive analytics.

Analytics as Oxygen

In the future, analytics won’t be something that only analysts do. Rather, analytics will be like oxygen for everyone in your organization, helping them make better decisions faster to out-maneuver competition. Competing on analytics is not something to be done only in the boardroom: it’s most powerful when implemented from the bottom up.

By empowering the people who are closest to the action in their organizations, Big Analytics will have an impact that dwarfs the potential suggested by Executive Information Systems and Expert Systems. Companies that figure out how to leverage this trend will reap significant rewards – not unlike how companies like I2 and Trilogy realized the value of artificial intelligence decades after AI was perceived to have failed.

*Disclosure: I am a board member, investor or advisor to this company.

The Power of Mentoring

2012-04-13T16:03:00.000-04:00

Paying Back by Paying It Forward

There are all kinds of reasons to mentor people in business.

It feels good to invest in the next generation.
You get back more than you give.
You'd want someone to help your own kids.

But it’s also good business and it pays forward more than you can ever possibly imagine – especially if you are (or aspire to be) an entrepreneur.

That’s why I’m psyched to be participating in a Fireside Chat on Mentorship at the Greener Ventures Conference at the Tuck School of Business this Saturday, April 14. Joining me will be my long-time friend and colleague Dave Girouard (Dartmouth ’88, Thayer School of Engineering ’89), founder and CEO of Upstart. Along with a number of other great folks, we will be judging the Greener Ventures Entrepreneurship Contest. We are psyched to be sponsoring the first-ever business plan competition at Dartmouth/Thayer/Tuck and are looking forward to a weekend in Hanover.

I’ve been blessed over the past 20 years to have a fantastic series of mentors who have all given more to me than I can ever possibly describe or repay.

In particular, Peter Barris (who I met in 1993 while a second-year student at Tuck) has been both consistent and thoughtful in his advice and support over the past 18+ years. One of the unique aspects of Peter’s advice over the years was that – consistently – he gave me advice that was as objective as possible. Peter was always able to abstract his own interests out from the situation. He gave me feedback and advice on my own professional development, and ideas that were the best for me in the long term – regardless of the short-term benefit or cost to himself. This ability to focus on my long-term development regardless of short-term interests was more valuable for me than I can describe.

Also, my friend Frank Moss has been an incredibly powerful influence on my professional development. Through a very critical time in my career, Frank taught me that – no matter what the reward – compromising your personal values in business is never worthwhile and that true leadership in business is not about making money, but rather it is about being mission-driven, building valuable things and helping people.

I've been working with Dave Girouard on his new company, Upstart, which is focused on empowering young, smart and innovative people who are just beginning their careers and are interested in taking paths other than the traditional or conservative.

Building New Systems for Scientists

2012-04-10T08:15:00.000-04:00

Three Good Places to Start

In my last post, I wrote about the need to build new data and software systems for scientists – particularly those working in the life sciences. Chemists, biologists, pharmacologists, doctors and many other flavors of scientists working in life sciences/medical research are potentially the most important (and under-served) technology end-user group on the planet. (If you are looking for a great read on this, try Reinventing Discovery : The Age of Networked Science.)

There are many ways to break down what needs to be done. But here are three ways to think about how we can help these folks:

Support their basic productivity – by giving them modern tools to move through their information-oriented workdays
Help them improve their collaboration with others – by bringing social tools to science/research
Help them solve the hardest, most-challenging problems they are facing – by using better information/technology/computer-science tools

While all three of these are worth doing, #3 is the most tempting to spend time on, as the problems are hard and interesting intellectually.

A great example in the life sciences currently is Next Generation Sequencing/Whole Genome Sequencing, where (with all kinds of caveats) one instrument generates about 75TB of data a year. The cost of these instruments has dropped by an order of magnitude over the past five years, resulting in many decentralized labs just going out and buying them and getting busy with their own sequencing.

One of the many challenges that this poses is that often the labs don't realize the cost of the storage and data management required to process and analyze the output of these experiments (my back of the envelope is at least 2X-3X the cost of the instrument itself). The worst case scenario is when the scientists point these instruments at public file shares and find those file shares filling up in a small number of days – to the dismay of other users who want to use the file shares. Don't laugh: it's happening every day in labs all around the world .

The drop in cost-per-genome makes Moore's Law look incremental.

Anyway, there are hundreds of interesting scientific problems like NGS that require equally advanced computer science and engineering. One effort towards providing a place where these things can develop and grow in an open environment is OpenScience.

But I'd like to focus more on #1 and #2 – because I believe that these are actually the problems that, when solved, can deliver the most benefit to scientists very quickly. They don't depend on innovation in either the fundamental science or information technology. Rather, they depend primarily on end users and their organizations implementing technologies that already exist and – if deployed with reasonable thought and discipline – can have a huge positive impact on scientific productivity.

Here are a few examples.

Collaboration Tools

Social networking, Wikis, Google Hangouts [watch] and other networking technologies make it easy and inexpensive to create more-flexible collaboration experiences for scientists. These aren’t your father’s collaboration tools – the kind of process-automation gizmos that most technology companies produced for businesses over the past 30 years. Rather, these new tools empower the individual to self-publish experimental design and results and share with groups of relevant and interested people – early and often. Think radically more- dynamic feedback loops on experimentation, and much more granular publishing and review of results and conclusions.

Much of what scientists need in their systems is collaborative in nature. If you are a researcher working in either a commercial organization or academic/philanthropic organization, how do you know that those experiments haven’t already been done by someone else – or aren’t being run now? If scientists had the ability to "follow" and "share" their research and results as easily as we share social information on the Web, many of these questions would be as easy to answer as "Who has viewed my profile on LinkedIn?"

Part of this depends on the clear definition of scientific "entities": Just as you have social entities on Facebook – Groups, Individuals, etc. – scientists have natural scientific entities. In the life sciences, it's the likes of Compounds, Proteins, Pathways, Targets, People, etc. If you do a decent job of defining these entities and representing them in digital form with appropriate links to scientific databases (both public and private), you can easily "follow compound X." This would enable a scientist to not only identify who is working on scientific entities that he's interested in, but also fundamentally to stand on the shoulders of others, avoid reinventing the wheel, and raise the overall level of global scientific knowledge and efficiency by sharing experiments with the community.

Two start-ups that I mentioned in my earlier blog post – Wingu and Syapse – are creating Web services that enable this kind of increased collaboration and distribution of research in the pharma industry. Many large commercial organizations are attempting to provide this type of functionality using systems such as SharePoint and MediaWiki. Unfortunately, they are finding that traditional IT organizations and their technology suppliers lack the expertise to engage users in experiences that can compete for attention relative to consumer Internet and social tools.

I've watched this dynamic as various research groups have begun to adopt Google Apps. It's fascinating because companies and academic institutions that adopt Google Apps get the benefit of all the thousands of engineers who are working to improve Google's consumer experience and just apply these same improvements to their commercial versions – truly an example of the "commercialization of enterprise IT" (and credit to my friend Dave Girouard for the great job he did building the Enterprise group at Google over the past eight years).

One of the ways that Google might end up playing a large and important role in scientific information is due to the fact that many academic institutions are aggressively adopting Gmail and Google Apps in general as an alternative to their outdated email and productivity systems. They have skipped the Microsoft Exchange stage and gone right to multi-tenant hosted email and apps. The additional benefit of this is that many scientists will get used to using multi-user spreadsheets and editing docs collaboratively in real time instead of editing Microsoft Office documents, sending them via email, saving them, editing them, sending them back via email, blah...blah...blah.

If companies aren't doing the Google Apps thing, they are probably stuck with Microsoft - and locked into the three-five year release cycles of Microsoft technology in order to get any significant improvements to systems like SharePoint. After a while, it becomes obvious that the change to Google Apps is worthwhile relative to the bottleneck of traditional third-party software release cycles. Particularly for researchers, for whom these social features can have a transformational effect on their experimental velocity and personal productivity.

Another example of how this dynamic is playing out is seen in the dynamic between innovators Yammer and Jive vs. Microsoft SharePoint. This is a great example of how innovators are driving existing enterprise folks to change, but ultimately we'll see how the big enterprise tech companies – Microsoft, IBM, etc. – can respond to the social networking and Internet companies stepping into their turf. And we'll see if Microsoft can make Office365 function like Google Apps. But if Azure is any indication, I'd be skeptical.

Open-Source Publishing

First - thank you Tim Gowers for the post on his blog - all I can say is YES!

In my opinion, the current scientific publishing model and ecosystem are broken (yet another topic for another post). But today new bottom-up publishing tools like ResearchGate let scientists self-publish their experiments without depending on the outdated scientific publishing establishment and broken peer-review model. Scientists should be able to publish their experiments – sharing them granularly instead of being forced to bundle them into long-latency, peer-reviewed papers in journals. Peer review is critical, but should be a process of gradually opening up experimental results and findings through granular socialization. Peer review should not necessarily be tied to the profit-motivated and technically antiquated publishing establishment. I love the work that the folks at Creative Commons have done in beginning to drive change in scientific publishing.

One of the most interesting experiments with alternative models has been done by the Alzheimer Research Forum, or Alzforum. The set of tools known as SWAN is a great example of the kind of infrastructure that could easily be reused across eScience.   It makes sense that in the face of the huge challenge represented by treating Alzheimer's, people –  patients, doctors, scientists, caregivers, engineers – would work together to develop the tools required to share the information and collaborate as required, regardless of the limitations of the established infrastructure and organizational boundaries. I know there are lots of other examples and am psyched to see these highlighted and promoted :)

Data As A Service

Switching to infrastructure for a second: One of the things that scientists do every day is create data from their experiments. Traditionally this data lives in lots of random locations - on the hard drives of scientific instruments, on shared hard drives within a lab, on stacks of external hard drives sitting on their lab benches, perhaps a database that someone has set up within a lab.

I believe that one of the ways that we can begin to accelerate the pace of experimentation and collaboration of scientists is to enable them to put their data into a rational location for sharing their data, conducting collaborative analytics, sharing the derived results, and establishing the infrastructure and provenance required to begin producing reproducible results.

And, as we've seen with Next-Generation Sequencing: One of the challenges of science that depends on Big Data is that scientists are traditionally conditioned to manage their data and their analytics locally. This conditioning creates problems when you have large-scale data (can't afford to keep it all locally) and when you want to collaborate on the analysis of the data (data is too big to copy around to many locations). This also creates a challenge in terms of the ability to create the provenance required to reproduce the results.

One of the emerging trends in database systems is the "data-as-a-service" model. Data as a service essentially begins to eliminate the complexity and cost of setting up proprietary Big Data systems, replacing this with running databases as services on scale-out infrastructure in multi-tenant and single-tenant modes. The most high-profile example of these lately has been Dynamo, Amazon's data-as-a-service key value store.

One of the other well-developed data-as-a-service providers is Cloudant, which provides developers an easy-to-use, highly scalable data platform based on Apache CouchDB. Cloudant was founded in 2008 by three physicists from MIT, where they were responsible for running multi-petabyte data sets for physics experiments running on infrastructure such as the Large Hadron Collider. Cloudant’s product was born out of its founders’ collective frustration with the tools available specifically in context of the interest of serving science. (Yet again an example of the needs of science driving the development of technology that benefits the rest of us.)

One of the things that attracted me to working with the team at Cloudant was their continued interest in developing large-scale data systems for scientists and their commitment to prioritize the features and functions required by science end-users at the top of the company's priority list.

What other pockets of inspiration and innovation are you seeing in building new systems for scientists? Please comment.

It’s Time to Build New Systems for Scientists (Particularly Life Scientists)

2012-04-03T08:15:00.000-04:00

Society and the Cambridge Innovation Cluster Will Benefit

Scientists are potentially the most important technology end-users on the planet. They are the people who are conducting research that has the potential to improve and even save lives. Yet, for the most part, scientists have been almost criminally under-served by information technologists and the broad technology community.

Interestingly, life sciences have had a tremendous impact on computer science. Take, for example, Object Oriented Programming (OOP), developed by Dr. Alan Kay. A biologist by training, Dr. Kay based the fundamental concepts of OOP on microbiology: “I thought of objects being like biological cells and/or individual computers on a network, only able to communicate with messages (so messaging came at the very beginning - it took a while to see how to do messaging in a programming language efficiently enough to be useful).”

It’s time to return the favor.

Pockets of Revolution and Excellence

Of course, there are others who are trying to advance information technology for scientists, like visionaries Jim Gray, Mike Stonebraker, and Ben Fry.

Throughout his career, Jim Gray, a brilliant computer scientist and database systems researcher, articulated the critical importance of building great systems for scientists. At the time of his disappearance, Jim was working with the astronomy community to build the world-wide telescope. Jim was a vocal proponent of getting all the world’s scientific information online in a format that could easily be shared to advance scientific collaboration, discourse and debate. The Fourth Paradigm is a solid summary of the principles that Jim espoused.

My partner and Jim’s close friend Mike Stonebraker started a company – Paradigm4, based on an open source project called SciDB – to commercialize an “array-native” database system that is specifically designed for scientific applications and was inspired by Jim’s work. One of my favorite features in P4/SciDB is data provenance, which is essential for many scientific applications. If the business information community would wake up from its 30-year “one-size-fits-all” love affair with traditional DBMS, it would realize that new database engines that have provenance as an inherent feature can create better audit-ability than any of the many unnatural acts they currently do with their old school RDBMS.

Another fantastic researcher who is working at the intersection of the life sciences and computer science is Ben Fry. Ben is truly a renaissance man who works at the intersection of art, science and technology. He’s a role model for others coming out of the MIT Media Lab and a poster child for why the Lab is so essential. Cross-disciplinary application of information technology to applications in science is perhaps the most value-creating activity that smart young people can undertake over the next 20 years. (At least it’s infinitely better than going to Wall Street and making a ton of dough for a bunch of people that already have too much money).

Time to Step Up IT for Scientists

But ambitious and visionary information technology projects that are focused on the needs of scientists are too rare. I think that we need to do more to help scientists – and I believe that consumer systems as well as traditional business applications also would benefit radically.

As a technologist and entrepreneur working in the life sciences for the past 10+ years, I’ve watched the information technology industry spend hundreds of billions of dollars on building systems for financial people, sales people, marketers, manufacturing staff, and administrators. And, more recently, on systems that enable consumers to consume more advertising and retail products faster. Now the “consumerization of IT” that drove the last round of innovation in information technologies – Google, Twitter, Facebook, and so on – is being integrated into the mainstream of corporate systems. (In my opinion, however, this is taking 5 to 10 times longer than it should because traditional corporate IT folks can’t get their heads around the new tech.)

Meanwhile, scientists have been stuck with information technologies that are ill-suited to their needs – retrofitted from non-science applications and use cases. Scientists have been forced to write their own software for many years and develop their own hardware in order to conduct their research. My buddy Remy Evard was faced with this problem – primarily in managing large-scale physics information – while he was the CIO at Argonne National Labs. Now he and I share the problem in our work together at the Novartis Institutes for Biomedical Research (NIBR).

When I say “systems,” I am talking about systems that capture the data and information generated by scientists' instruments and let scientists electronically analyze this data as they conduct their experiments and also ensure the “repeatability” of their experiments. (Back to the value of provenance). With the right kind of systems, I believe we could:

Radically increase the velocity of experimentation. Make it much easier for scientists to do their experiments quickly and move on to the next experiment sooner

Significantly improve the re-usability of experimentation – and help eliminate redundancy in experiments

Ensure that experiments – both computational and wet lab – can be easily replicated

Radically improve the velocity of scientific publication

Radically improve the ability of peers to review and test the reproducibility of their colleagues’ experiments

Essentially, with radically better systems, we would vastly improve the productivity and creativity of science. I also believe there would be immeasurably large benefits to society as a whole – not only from the benefits of more effective and efficient science, but also as these systems improvements are applied to consumer and business information systems.

How We Got Here

So, what’s holding us back?

Scientific applications are highly analytical, technical, variable and compute-intensive – making them difficult and expensive to build.

Scientific processes don’t lend themselves to traditional process automation. Often the underlying ambiguity of the science creates ambiguity in the systems the scientists need, making development a real moving target. Developers need to practice extreme Agile/Scrum methods when building systems for scientists –traditional waterfall methods just won’t work.

Developers need to treat the core data as the primary asset and the applications as disposable or transitory. They must think of content, data and systems as integrated and put huge effort into the management of meta-data and content.

Great systems for scientists also require radical interoperability. But labs often operate as fiercely independent entities. They often don’t want to share. This is a cultural problem that can torpedo development projects.

These challenges demand high-powered engineers and computer scientists. But the best engineers and computer scientists are attracted to organizations that value their skills. These organizations have traditionally not been in life sciences, where computer scientists usually rank near the bottom of the hierarchy of scientists.

When drug companies have a few million dollars to invest in their lab programs, most will usually choose to invest it in several new new chemists instead of an application that improves the productivity of their existing chemists. Which would you choose? Some companies have just given up entirely on information systems for scientists.

So, scientists are used to going without – or they resort to hacking things together on their own. Some of them are pretty good hackers [watch]. But clearly, hacking is a diversion from their day jobs – and a tremendous waste of productivity in an industry where productivity is truly a life-and-death matter.

No More Excuses

It’s also completely unnecessary, given the changes in technology. We can dramatically lower the cost and complexity of building great systems for scientists by using Web 2.0 technologies, HTML5, cloud computing, software-as-a-service application models, data-as-a-service, big data and analytics technologies, social networking and other technologies.

For example, with the broad adoption of HTML5, apps that used to require thick clients can now be built with thin clients. So we can build them more quickly and maintain them more easily over time. We can dramatically lower the cost of developing and operating flexible tools that can handle the demanding needs of scientists.

Using Web technologies, we can make scientific systems and data more interoperable. With more interoperable systems based on the Web, we can capitalize on thousands of scientists sharing their research and riffing off of it – whether inside one company or across many. There’s tremendous benefit to large scale in the scientific community – if you can make it easier for people to work together.

Many of my friends have been asking me why I’ve been spending so much time at the Novartis Institute for Biomedical Research (NIBR) over the past three years. The simple answer is that I believe that building systems for scientists is important – and I find it incredibly rewarding.

One of the lessons I learned while we were starting Infinity Pharmaceuticals was that while scientists needed much better systems, small biotech companies didn’t have the resources to build these systems. So, every time we looked at spending $1 Million on a software project at Infinity, we always decided it was better to hire another five medicinal chemists or biologists. The critical mass required to build great systems for scientists is significant -- and arguably even exceeds the resources of a 6,000+person research group like NIBR. The quality of third-party solutions available for scientists and science organizations such as NIBR are pitiful: there have been only a handful of successful and widely adopted systems, Spotfire being the most notable example. Scientists need better 3rd party systems and delivering these systems as web services might finally provide a viable business model for "informatics" companies. Two great new examples of these types of companies are Wingu and Syapse - check them out.

Calling All Cambridge Technologists

So here’s my challenge to the technology industry: How about investing $3 to $4 billion on systems for scientists? And have the Cambridge Innovation Cluster take the lead?

If you work in Kendall or Harvard Squares, you can’t throw a Flour Bakery & Café sticky-bun without hitting a scientist. It’s one huge research campus with millions of square feet of laboratory space and scientists everywhere.

Get engaged with scientist-as-the-end-user – they’ll welcome you, trust me. Build something in one of these labs. If you build something compelling in a thoughtful way, it’s going to be noticed, sought and adopted by others.

Since Cambridge has one of highest concentrations of scientists in the world, technologists here should focus on building systems for scientists. I’m betting that they can do it better than anyone else in the world.

What do you think?

Building a Founders Culture in Cambridge, Mass.

2012-03-27T08:15:00.000-04:00

Why It Will Take More than Money

My adopted hometown of Cambridge, Massachusetts, has perhaps the highest density of IQ anywhere in the world and a strong entrepreneurial spirit that helped create many great companies. It’s one of the best places in the US to nourish new Internet, info-tech and biotech companies.

What’s missing is a radically stronger founders culture that will attract and motivates the kind of entrepreneur who will found companies and work to keep them here. Multiple times. Silicon Valley has done a great job at this. Cambridge/Boston has not. I am not the first person to have said this - far from it.

In a recent Innovation Economy column, Scott Kirsner compared the paths of two companies – Brightcove and Facebook – that both got their starts in 2004 in Cambridge but followed clearly different paths to IPO. In analyzing why Boston let Facebook’s founders simply walk away, Spark Capital’s Todd Dagres cited a discomfort with unproven entrepreneurs, among other things.

But if we’re only willing to trust proven entrepreneurs and help them succeed, how will we attract the next wave of company founders?

My friend and trusted colleague Dave Girouard puts it the following way: “In Boston, they want somebody who has done it before. In the Valley, they want someone who could do it next.” Eight years ago, Dave made the decision to go to Google instead of moving back to his home town of Boston. He recently announced that he’s leaving Google to do a new start-up backed by Kleiner Perkins, NEA and Google Ventures – and his new company, Upstart – will be based in San Francisco. :(

It's Time to Lean Into Risk

We need to ensure that we’re starting the kind of companies and attracting the best talent to Boston on a regular basis. Let’s make it easy for potential recruits by leaning into risk and doing whatever it takes to attract the best and brightest entrepreneurial business talent to Cambridge.

There are a few shining examples of how it should work. Probably one of the most notable is the fantastic job that Tim Healy has done in founding and building EnerNOC here in New England.

Tim and his co-founders started EnerNOC from scratch in New England and located in Boston. They did all the unnatural acts required to start a company, took it public, and resisted temptations to sell out. EnerNOC is now an anchor of the energy cluster in Boston.

Money isn’t the problem. Venture capitalists invested $30 Billion in the US in 2011, so there’s plenty of money out there. About $3 Billion of this went to companies in Massachusetts.

If you're an entrepreneur, the question is whether you need to raise money, and on what terms is it worthwhile. Savvy entrepreneurs recognize that venture funding is just another form of capital – and it’s by far the most expensive form of capital. The partners and firms that you choose to work with have to make it worthwhile for you – the entrepreneur – the person with a portfolio of ONE.

Perhaps most importantly, you have to trust that those investors that you select as your partners will support you during the worst and the best of times – because doing start-ups is a guaranteed roller coaster. But as my friend Adelene Perkins at Infinity Pharmaceuticals says: “Things are never as good or as bad as you think they are in early-stage companies.”

Great start-ups and entrepreneurs can always raise money: by definition, they figure out how to get the capital they need to achieve their missions. What’s missing are the right kind of talent and support required to take ideas from their raw state to something that is Fundable regardless of whether they’re raising outside institutional funding or self-funding/bootstrapping.

The right kinds of talent and support will allow entrepreneurs to build companies that will generate huge value for shareholders by achieving an aspirational mission that has a significant positive impact on the world.

Let’s work together to make this happen. What are your ideas?

Introducing The Fundable Blog

2012-03-27T08:00:00.001-04:00

Ideas for Start-ups and Entrepreneurship in Cambridge and Beyond

I love start-ups. My grandfather – Ray Schuster, started his own company - Illinois FWD - was a role model and inspired me to start companies. (That's him in the photo, fifth from the right – wearing a big smile as he and his team break ground on a new facility.)

I believe that the rugged individualism on which our country was founded and has prospered is a competitive cultural advantage for the US. I hope that during the 21^st century we realize our country's destiny to become the most entrepreneurial and innovative country in the world.

We can do this. We just have to use our inherent cultural advantage proactively by making the US the best place for the smartest and most ambitious people in the world to start new, innovative companies. And we need to start right here in my adopted home town of Cambridge, Massachusetts.

Cambridge has perhaps the highest density of IQ anywhere in the world and a strong entrepreneurial spirit that helped create great companies such as Akamai, Biogen, Brightcove, EMC, EnerNOC, Genzyme, Harvard Bioscience, iRobot, LogMeIn, Lotus, Staples, Vertex, and Vistaprint. It is one of the best places in the US to nourish new Internet, info-tech and biotech companies.

But I believe (along with many others) that the start-up community here in Cambridge is vastly under-performing relative to its potential. I’m not the first person to have said this – far from it – but what I believe we are missing is a radically stronger “founders culture.” Among others, Scott Kirsner of The Boston Globe and Eric Paley of Founder Collective have written about this extensively. (I will talk more about this idea in my next post.)

To net it out: I think that we have exceptional technical and scientific talent, ideas and content. But we’ve done a miserable job of attracting, retaining and motivating the right type of business-oriented entrepreneurs.

On this blog, I’ll offer my opinions about some of these strengths and weaknesses, and I won’t pull any punches. I will also offer my ideas for addressing the challenges and taking advantage of the huge untapped opportunity here in Cambridge.

My intention is not to be critical but constructive: I want to help re-invigorate the entrepreneurial culture in Cambridge. I would like to ensure that the next Facebook, Google, Cisco, Amazon or Yahoo happens here in Cambridge. I am committed not only to blogging about this but also to helping found companies that will be potential candidates. I believe that we all need to work together to build world-class independent companies that not only start in Cambridge, but also grow up and flourish here as independent entities. That is, they’re not sold to companies elsewhere (even if that would mean multi-billion-dollar windfalls for local venture firms).

If we can do this a few times, we will have established the critical mass required to sustain a world-class culture of founders who bridge the academic and commercial worlds to create economic and social value like nowhere else on earth.

On this blog I’ll share ideas, insights, and practical advice to help first-time founders and CEOs build better start-ups:

How to work the entrepreneurial ecosystem to gain unfair competitive advantage.

How to inspire, empower and develop young people who aren’t constrained by habit or artificial ideas of what’s possible.

How to create a culture of founders in Cambridge that reinforces founding as a unique skill set and role within great new companies, independent of other traditional functional roles.

How to have fun doing all this while working insanely hard, treating other people with respect, and behaving with honor.

Which brings me back to my grandfather. He loved trucks - big trucks. When I was in elementary school, one of his customers was the City of Chicago Fire Department. The Fire Department had to figure out how to support the Hancock Tower, which at the time was one of the tallest buildings in the world. My grandfather sold what was at the time the tallest fire truck and ladder in the world to the City of Chicago. Before he delivered it, he brought it to my school – Tarkington Elementary – to demo it for the kids. I had a chance to ride in it to the top. Freaking AWESOME for a fifth-grader.

Besides my grandfather making me the most popular kid at school for a day, I always looked back on that memory and thought how cool it was that my grandfather was able to do a job that let him play with trucks every day – truly doing what he loved.

This same spirit drives me today. I’m mission-driven. I work on things that I’m passionate about. The money is a means to an end, not the end itself. I'm passionate about computers, chemistry, biology, drug discovery, education and the intersection of these.

I hope you’ll let me know what you think of my blog, through your comments, questions and challenges. Please also let me know what else you’d like to read about.

See you around Kendall and Harvard Squares.

I would like to thank Mike Stonebraker, Rich Miner, Tim Rowe, Ellen Rubin, Frank Moss, Steve Holtzman, Marilyn Matz, Remy Evard and Christopher Ahlberg for their unrelenting support and commitment to moving me and my family into Cambridge. I’m truly blessed to have you as my friends, colleagues and now neighbors.