Enterprise-Focused Public SaaS Companies More Likely to Be Acquired

David Cummings on Startups

Reflecting on the list of publicly traded B2B SaaS companies from yesterday, it’s clear that over the past few years, companies that reached scale and went public were more likely to be acquired if they focused on selling to large companies (enterprises) as opposed to ones selling into the small-to-medium sized business segment. Companies like ExactTarget, Eloqua, and Responsys are all in the email marketing space, which is white hot (and just had another nice exit with IBM buying Silverpop and closing the transaction today). If you go back another year you have SuccessFactors and Taleo both getting acquired, and both targeting the enterprise with HR software.

Here are a few thoughts on why enterprise-focused startups are more likely to be acquired:

  • Companies targeting the enterprise with scale have an easier time maintaining a fast growth rate due to the nature of high dollar sales (if each new deal is $200k/year…

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Data Science for Social Good

Last year something incredible happened at the University of Chicago. Thirty-six aspiring data scientists spent their summer working in small teams on challenging real-world problems in education, health, energy, transportation, and more to directly benefit their local communities in need using data science techniques including data mining and machine learning. They applied their coding and analytics skills under guidance of mentors from industry, academia, and the chief data scientist from the 2012 Obama campaign, Rayid Ghani (@rayidghani).

These graduate and undergraduate Fellows came from quantitative and computational fields spanning computer science, statistics, and public policy. The results were simply amazing and changed the lives of the fellows forever as leaders in data science with the skills and passion to change their local communities for the better.

Watch this short video to meet some of the participants from last year and to see their unbridled enthusiasm for the Data Science for Social Good Summer Fellowship program.

This summer the Data Science for Social Good (DSSG) Summer Fellowship program is coming to Atlanta through the support of Georgia Tech and the City of Atlanta.

DSSG will be a landmark event for data science in Atlanta! Our hope is that Fellows, mentors, and project partners will be able to benefit from national exposure of their works through a demo day at the end of the summer and that the efforts of Fellows will be implemented by some of the project partners to benefit the Atlanta Metro Area. Also, our hope is that DSSG becomes an annual event going forward and builds into a premier showcase for Georgia Tech and Atlanta.

The long term benefit of a program like this to Atlanta is to foster a concentration of data scientists and data-savvy business and non-profit leaders to come to or remain in Atlanta. These leaders are likely to become professors, non-profit leaders, or start new data-centric businesses to grow and foster economic development in Atlanta.

If you are interested in applying to be a DSSG Fellow, mentor, project partner, or just to be updated about future events please go to DSSG-ATL.io to join our mailing list. We are working to update the page so you can apply directly through our website so look for an update in February.


Travis Turney (@travturn)

Cofounder, Data Science ATL

Power of Community

I am continuously amazed by the Atlanta tech community giving back to provide opportunity to others. Recently, I attended the first “Kevy Connects” event at Atlanta Tech Village in Buckhead. Kevy is an Atlanta-based software startup that makes it easy to integrate and sync your cloud apps. Kevy is led by entrepreneur David Cummings (@davidcummings) , co-founder of Pardot which is now a Salesforce company.

David is a very busy guy as you might imagine but he took the time to spend the evening away from his family to invest in growing Atlanta’s enterprise software community. These investments in community can pay huge dividends down the road both professionally and personally.

Over 200 people gathered to hear a panel of leaders most of whom were founders, current or former CEOs of Atlanta based software companies who spoke about their choice to found or move their companies to the capital of the South and why. Culture and talent were the big themes and they really resonated with me.

The biggest takeaway for me was that Atlanta needs a big independent platform company to IPO, remain in Atlanta, and seed new companies with talent or investment to supercharge the positive feedback loop in the startup community here. Seems to me that Kevy has a great shot at being such a company.

So what is the meaning of all this? Building a company starts with community and culture and should that not stop being core to the mission of the company after they are successful or are acquired. Sometimes an acquisition by a much larger parent can blunt a company’s culture changing it for the worse (see ISS). Community oriented companies attract passionate customers who magnify your message in a way that your communications team could never do on their own.

Community matters but it doesn’t happen on accident. You need to invest in community and provide real tangible benefits continuously to keep them coming back.

When I co-founded Data Science ATL last year with Dr. Raj B. (@plusbzz) community was the first order of business. We saw first hand the passion from other meetups and we wanted to build off that with a broad and diverse community of people in Atlanta dedicated to life-long learning about data science.

After 17 events we are 800+ members strong with 100+ attendees at each event. While it’s great to have a group that likes what we have to offer the diversity of the group really surprised me.

Through a non-scientific self-identifying survey (n=82) the group’s membership looks something like this: DS ATL - Survey results - Member Categories There are a lot of categories of member’s jobs here and still the “other” categories is one of the biggest! What this means to me is that the diversity of our membership mirrors the diversity of the practice of data science. It’s an indicator that we’re doing something right.

One of the things we do to get new members involved is to host the event at different locations around town and strive to present fresh new topics that really engage the membership. Sometimes we host at co-working facilities like Hypepotamus or Atlanta Tech Village both of which have been a great way to engage the community and find new data science enthusiasts.

But, it’s hard to beat hosting hosting events at company offices. It’s always fun since it’s great for the members to get a sneak peek inside these innovative companies like our event next month at MailChimp. RSVP here.

The best case scenario is when a member gets a job with one of our speaker’s companies after we host an event at their office! It has happened but i’d like to see if happen more.

Being a meetup group based on monthly speakers is fun but it can be tough to get high quality speakers at times. The best speakers are in high demand and have really busy professional and personal lives besides spending their evenings out and about. My experience is that our best source of speakers has come through the networks of the membership.

Now that we are targeting all the chief data scientists in the Metro Atlanta area as speakers it has become critical that we branch out through our members networks to identify and secure commitments from speakers. So far the members have been more than helpful and some have even come to us on their own with great speakers for the meetup. We pride ourselves on providing opportunity for members through education, networking, or employment and hearing the latest in data science from great speakers is a great way to keep current.

We are thinking about hosting a gala event in the Spring to really give back to the data science community with a day of great speakers, networking, and fun! If you would like to speak or sponsor for an event like this please contact me ASAP.


J. Travis Turney (@travturn)

Co-founder Data Science ATL

Grand Challenges

Since my inaugural blog post last week on the Hack for CF hackathon (RSVP today!) I have been thinking about grand challenges and specifically grand challenges where data science can be a critical catalyst. My first instinct was to reach out to the 750+ members of @DataScienceATL and see what they thought were the most ambitious challenges before the human race and how data science could play a central role.

The feedback was surprising and only encouraged me to dive deeper. I’ll have more on the group’s feedback in a future blog post.

The great thing about data science is that it is a meritocracy unparalleled in human history that can flourish anywhere two or more smart people congregate physically (See @DataScienceATL) or virtually (see Github). The applicability of the insights from data science shared widely can be applied instantly and everywhere causing all boats to rise and inspiring new voyages to embark on. The journey is never over. And that’s a good thing.

The future is wide open and nowhere more so than in the universe of data science. Data science is an inherently collaborative discipline requiring such a broad yet focused set of skills that no individual can do it alone. It takes a village to make advances in data science. This is also a good thing.

This begs the question, what exactly is data science?

Honestly, I can’t tell you exactly. But, I can share something that gives an idea of what it is. A data scientist is a better hacker than any of their statistician peers and a better statistician than any of their hacker peers. A chief data scientist has a very interesting role that involves both of these skills among many others in part but first and foremost is chief recruiter to build a team that satisfies all critical areas of data science. Dominant companies that depend on data science will likely have multiple data science teams as they begin to scale and fully take advantage of their insights.

This collaboration requirement goes beyond a given team in a given company but extends within and across industries. There isn’t a data science industry per se. Rather, there exists the practice of data science. Data science is applicable to all industries and the winners will embrace it, invest in it, and dominate because of it. Data science like computer science is largely a human capital play. You can’t just buy a data science patent and wrap a killer enterprise sales team around it. Perhaps this is necessary for success but certainly not sufficient.

The only way to “solve” a grand challenge in data science is collaboration at a scale unprecedented in human history while incorporating hyper-specialization. Open source software is to computer science as open source collaboration is to data science. One common source of this collaboration is the panoply of PhD theses that are public domain. Again this is necessary but not sufficient.

At ProductCamp ATL 2013 @KylePorter of SalesLoft shared an insight that once he started sharing secrets and insights with customers they began building a following of fanatic subscription customers. This is an insight that other CEOs can learn from. I know I did. Once you start giving away your crown jewels you begin to engage customers in a way that just wasn’t possible before. The best way to differentiate your company is to get the smartest and hardest working team you can possibly assemble and get to hustling. This is even more critical for data science.

We need more companies to provide this kind of thought leadership. Google publishes original research that is core to their business (or at least was at one time) and nearly every “big data” company you have heard of exists because of it. Google published a paper on MapReduce and the open source community with significant contributions from Yahoo! created Hadoop. Now Cloudera, Hortonworks, and MapR are building businesses based on Hadoop as a result of of that community effort. Google later published a paper on BigTable and now several “NoSQL” companies are competing to dominate the unstructured database market. Google will continue to publish original research and bright software entrepreneurs will capitalize on their proven insights but more companies need to follow suit.

I’m optimistic that companies publicly sharing original research will become commonplace mainly because so many chief data scientists come from academia and that’s in their nature. If CEOs don’t like it chief data scientists will start their own companies and will publish original research anyway. Facebook is experimenting with sharing original research that is fundamental to their business by championing the OpenCompute project.

Solving the grand challenges of the next decade will require everyone to share their crown jewels. Patent protection will not help solve grand challenges. People will solve grand challenges and chances are some of the most critical constituent members of these teams are randomly dispersed around the world and not necessarily in Silicon Valley. Certainly having concentrations of talent geographically helps. It helps a lot! It’s necessary but not sufficient.

Share now, share often, and shout it out from the rooftops or the digital equivalent! It’s the only way we’ll get to where we need to go.

What are the grand challenges over the next decade or next century that we should solve? What are you doing to get there?


J. Travis Turney

Co-founder @DataScienceATL