Is Big Data Still a Thing? (The 2016 Big Data Landscape)

 

In a tech startup industry that loves its shiny new objects, the term “Big Data” is in the unenviable position of sounding increasingly “3 years ago”.   While Hadoop was created in 2006, interest in the concept of “Big Data” reached fever pitch sometime between 2011 and 2014.  This was the period when, at least in the press and on industry panels, Big Data was the new “black”, “gold” or “oil”.  However, at least in my conversations with people in the industry, there’s an increasing sense of having reached some kind of plateau.  2015 was probably the year when the cool kids in the data world (to the extent there is such a thing) moved on to obsessing over AI and its many related concepts and flavors: machine intelligence, deep learning, etc.

Beyond semantics and the inevitable hype cycle, our fourth annual “Big Data Landscape” (scroll down) is a great opportunity to take a step back, reflect on what’s happened over the last year or so and ponder the future of this industry.

In 2016, is Big Data still a “thing”? Let’s dig in.

Enterprise Technology = Hard Work

The funny thing about Big Data is, it wasn’t a very likely candidate for the type of hype it experienced in the first place.

Products and services that receive widespread interest beyond technology circles tend to be those that people can touch and feel, or relate to:  mobile apps, social networks, wearables, virtual reality, etc.

But Big Data, fundamentally, is… plumbing.  Certainly, Big Data powers many consumer or business user experiences, but at its core, it is enterprise technology: databases, analytics, etc: stuff that runs in the back that no one but a few get to see.

And, as anyone who works in that world knows, adoption of new technologies in the enterprise doesn’t exactly happen overnight.

The early years of the Big Data phenomenon were propelled by a very symbiotic relationship among a core set of large Internet companies (in particular Google, Yahoo, Facebook, Twitter, LinkedIn, etc), which were both heavy users and creators of a core set of Big Data technologies.  Those companies were suddenly faced with unprecedented volume of data, had no legacy infrastructure and were able to recruit some of the best engineers around, so they essentially started building the technologies they needed.  The ethos of open source was rapidly accelerating and a lot of those new technologies were shared with the broader world.  Over time, some of those engineers left the large Internet companies and started their own Big Data startups.  Other “digital native” companies, including many of the budding unicorns, started facing similar needs as the large Internet companies, and had no legacy infrastructure either, so they became early adopters of those Big Data technologies.  Early successes led to more entrepreneurial activity and more VC funding, and the whole thing was launched.

Fast forward a few years, and we’re now in the thick of the much bigger, but also trickier, opportunity: adoption of Big Data technologies by a broader set of companies, ranging from medium-sized to the very largest multinationals.  Unlike the “digital native” companies, those companies do not have the luxury of starting from scratch.  They also have a lot more to lose: in the vast majority of those companies, the existing technology infrastructure “does the trick”.  It may not have all the bells and whistles, and many within the organization understand that it will need to be modernized sooner rather than later, but they’re not going to rip and replace their mission critical systems overnight.  Any evolution will require processes, budgets, project management, pilots, departmental deployments, full security audits, etc.  Large corporations are understandingly cautious about having young startups handle critical parts of their infrastructure. And, to the despair of some entrepreneurs, many (most?) still stubbornly refuse to move their data to the cloud, at least the public one.

Continue reading “Is Big Data Still a Thing? (The 2016 Big Data Landscape)”

The Power of Data Network Effects

In the furiously competitive world of tech startups, where good entrepreneurs tend to think of comparable ideas around the same time and “hot spaces” get crowded quickly with well-funded hopefuls, competitive moats matter more than ever.  Ideally, as your startup scales, you want to not only be able to defend yourself against competitors, but actually find it increasingly easier to break away from them, making your business more and more unassailable and leading to a “winner take all” dynamic.  This sounds simple enough, but in reality many growing startups, including some well-known ones, experience exactly the reverse (higher customer acquisition costs resulting from increased competition, core technology that gets replicated and improved upon by competitors that started later and learned from your early mistakes, etc.).

While there are various types of competitive moats, such as a powerful brand (Apple) or economies of scale (Oracle), network effects are particularly effective at creating this winner takes all dynamic, and have been associated with some of the biggest success stories in the history of the Internet industry.

Network effects come in different flavors, and today I want to talk about a specific type that has been very much at the core of my personal investment thesis as a VC, resulting from my profound interest in the world of data and machine learning: data network effects.

Network Effects vs. Data Network Effects

The concept of network effect (in general) is by now well understood: a flywheel type situation where a good or service becomes more valuable when more people use it.   Many examples out there from the telephone system (the value of a phone increases if everyone has a phone) to Facebook to many marketplaces (with some nuances for the latter).

While they produce many of the same benefits, data network effects are more subtle and generally less well understood.  Data network effects occur when your product, generally powered by machine learning, becomes smarter as it gets more data from your users.  In other words:  the more users use your product, the more data they contribute; the more data they contribute, the smarter your product becomes (which can mean anything from core performance improvements to predictions, recommendations, personalization, etc. ); the smarter your product is, the better it serves your users and the more likely they are to come back often and contribute more data – and so on and so forth.  Over time, your business becomes deeply and increasingly entrenched, as nobody can serve users as well.

Data network effects require at least some level of automated productization of the learning.  Of course, most well-run businesses “learn” in some way from data, but that’s typically done through analytics, with human analysts doing a lot of the work, and a separate process to build insights into the product or service.  The more automation you build into the loop, the more likely you are to get a flywheel effect going.

Continue reading “The Power of Data Network Effects”

Playing “fake VC” (or the portfolio approach to getting a job in venture capital)

How does one get a VC job?

Method 1:  Start a tech company, drive it a multi-billion dollar success. Drop a few bon mots on Twitter to your robust group of followers, make visionary statements during your TechCrunch Disrupt fireside chat, and build a reputation as a helpful mentor to entrepreneurs.  Then wait by your phone as major firms call you with General Partner offers.  Or start your own firm.

Method 2: Welcome to the long hard slog.  And read on.

The good news is that it’s a great time to get into venture capital.  While venture capital remains a small industry with comparatively few openings, many VC firms have raised a lot of money lately, so they need more people to help them deploy it.  A number of investment analyst/associate positions are advertised (or were recently) – for example, we just posted a new opening at FirstMark, see here.  I’m also aware of several General Partner-level searches being conducted right now.

Ok, but how do you get those jobs?  Ask any VC, and they will tell you that they get that question all the time, and I’m no exception — in fact, I’m not-so-secretly hoping that this (long) blog post will help cover 80% or so of the content discussed in the frequent conversations I have on the topic, so I can focus on the 20% that’s specific to each person’s circumstances.

It’s not just the odds that make the topic complicated – it’s also that that there’s no single or sure-fire path to the job. Continue reading “Playing “fake VC” (or the portfolio approach to getting a job in venture capital)”

Sketchfab and the democratization of 3D content

We’re about to see a lot more 3D content in our digital lives.  Various trends, some years in the making, are now intersecting to make this a near-term reality.

On the production side, 3D has of course existed for many years – this has been, in particular, the world of Computer Aided Design (CAD), which originated in part from MIT’s Sketchpad project in the early sixties.  In one form or another, 3D has been used as a professional format across many industries, such as architecture, engineering, construction, and entertainment. Creation of 3D content (even for consumer-facing products like gaming) has remained largely the province of a comparatively small group of specialized professionals. Continue reading “Sketchfab and the democratization of 3D content”

Hardware Startups: The VC Perspective

Among all the excitement for the Internet of Things and the resurgence of hardware as an investable category, venture capitalists, many of whom new to the space, have been re-discovering the opportunities and challenges of working alongside entrepreneurs to build hardware companies.  Below are the slides that David Rogg and I prepared for the recent Connected Conference, a great global event held in Paris.  They’re a good snapshot of how someone like me thinks about the hardware space, mid-2015.

 

 

The “Straight to A” Round

The venture financing path has evolved incredibly fast over the last 18 months. In this very busy financing market, what used to be a reasonably well understood progression from a seed round to a Series A to a Series B, etc. has now morphed into a more complex nomenclature of pre-seeds ($500k or less), crowdfunding rounds (especially for hardware), seeds ($1M-$2M, 6-9 months after the pre-seed), seed primes (an extra $1M or so, 12-18 months after the seed), Series A (now routinely $10-$12M in size, occasionally up to $15M), Series A-1, Series B, C, D, E, F etc. (as companies remain private longer).

The latest entrant in this rapidly evolving nomenclature seems to be what I’d call the “Straight to A” round, where the founders skip the seed stage altogether and raise directly a $5M-$10M Series A, often before building anything, sometimes even before incorporating a company. I had seen it here and there in the past, but it now seems to have become an accelerating trend. Continue reading “The “Straight to A” Round”

The Astounding Resurrection of AI [Slides]

A few days ago, I was invited to speak at a Yale Entrepreneurship Breakfast about about one of my favorite areas of interest, Artificial Intelligence.  Here are the slides from the talk — a primer on how AI rose from of the ashes to become a fascinating category for startup founders and venture capitalists.  Very much a companion to my earliest post about our investment in x.ai.   Many thanks to my colleague Jim Hao, who worked with me on this presentation.

x.ai and the emergence of the AI-powered application

AI is experiencing an astounding resurrection.  After so many broken promises, the term “artificial intelligence” had become almost a dirty word in technology circles.  The field is now rising from the ashes.  Researchers who had been toiling away in semi-obscurity over the last few decades have suddenly become superstars and have been aggressively recruited by the largest Internet companies:  Yann LeCun (see his recent talk at our Data Driven NYC event here) by Facebook; Geoff Hinton by Google; Andrew Ng by Baidu.  Google spent over $400 million to acquire DeepMind, a 2 year old secretive UK AI startup. The press and social media are awash with thoughts on AI.  Elon Musk cautions us against its perils.
 
What’s different this time? As Irving Wladawsky-Berger pointed out in a Wall Street Journal article, “a different AI paradigm emerged. Instead of trying to program computers to act intelligently–an approach that hadn’t worked because we don’t really know what intelligence is– AI now embraced a statistical, brute force approach based on analyzing vast amounts of information with powerful computers and sophisticated algorithms.”  In other words, the resurgence of AI is partly a child of Big Data, as better algorithms (in particular, what’s known as “deep learning”, pioneered by LeCun and others) have been enabled by larger than ever datasets and the ability to process those datasets at scale at reasonable cost.

Continue reading “x.ai and the emergence of the AI-powered application”

Lending Club IPO: Nice Guys Don’t Finish Last, and Other Lessons

The superb Lending Club success story is what the startup world is all about: a software-based reinvention of massive and inefficient industry; a product that puts consumers first and delivers undeniable benefits ; and an entrepreneurial mega-hit that brings incredible riches and returns to its founder and investors.

In some ways, Lending Club is a classic Silicon Valley story; in some other ways, it is pretty atypical. As a friend of Renaud Laplanche’s for over 20 years, I have had a chance to witness from up close some parts of his journey with Lending Club. It is full of interesting lessons for entrepreneurs and the tech industry in general.:

1.  Nice guys don’t finish last. According to some, the tech ecosystem has been grappling with a proliferation of jerks with oversized egos at the helm of very successful startups (see Pando’s “asshole rollcall” here). Whether one shares that point of view or not, Renaud is exactly the opposite of that. Kind, loyal, generous and understated, he’s the living proof that world-class entrepreneurial talent, drive and persistence don’t necessarily come associated with arrogance and low EQ.

2.  CEO focus does matter. Renaud has been focusing maniacally on his venture for the last eight years. Up until recently, he had spoken at comparatively few conferences. He doesn’t have a portfolio of cool angel investments on the side. Heck, he doesn’t even have a Twitter account.

3. Great founders can come from all sorts of backgrounds. Renaud defies a lot of current startup CEO stereotypes. He is not a technical founder. He started his career as a (gasp) corporate lawyer.   He’s a sole founder. He is French, with an unmistakable accent. Continue reading “Lending Club IPO: Nice Guys Don’t Finish Last, and Other Lessons”

The Internet of Things: Reaching Escape Velocity

An edited version of this post appeared on TechCrunch here.  A downloadable version of the chart is available on SlideShare here.

It’s been about 18 months since my original attempt at charting the Internet of Things (IoT) space. To say the least, it’s been a period of extraordinary activity in the ecosystem.

While the Internet of Things will inevitably ride the ups and downs of inflated hype and unmet expectations, at this stage there’s no putting the genie back in the bottle. The Internet of Things is propelled by an exceptional convergence of trends (mobile phone ubiquity, open hardware, Big Data, the resurrection of AI, cloud computing, 3D printing, crowdfunding). In addition, there’s an element of self-fulfilling prophecy at play with enterprises, consumers, retailers and the press all equally excited about the possibilities. As a result, the IoT space is now reaching escape velocity. Whether we’re ready for it or not, we’re rapidly evolving towards a world where just about everything will be connected. This has profound implications for society and how we collectively interact with the world around us. Key concerns around privacy and security will need to be addressed.

For entrepreneurs, the opportunity is massive. Where Web 1.0 connected computers to the Internet and Web 2.0 connected people, Web 3.0 is shaping up to be connecting just about everything else – things, plants, livestock, babies… Each new wave has spun out giant companies (Google and Amazon for Web 1.0, Facebook and Twitter for Web 2.0). Will Web 3.0 create a comparable group of behemoths?

The space has been evolving so rapidly over the last year and a half that our IoT landscape became quickly outdated. Here is a revised and updated version. A few notes: as always, and despite our best efforts, a number of great companies will be missing; omissions are completely unintentional. Also, as much as possible, we have tried to put one brand per category, although many companies probably belong to several categories. Finally, categorization of a rapidly evolving space is an imperfect exercise – we have done our best to be directionally correct, but we’re certainly open to feedback on how to make this chart better and more accurate.

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Some comments on the chart:

Explosion of startup activity: With hardware incubators graduating legions of new entrepreneurs, crowdfunding in full swing and an increasing number of VCs excited about the space, new companies and products are popping up left and right. The previous version of this chart featured 199 companies – it now has 612 logos!

Big companies are very active: I’ve written this before about the connected home segment, but this is a distinctive characteristic across the board: large companies have been active in this space or closely related spaces for a long time and are showing no interest in letting themselves get disrupted by small startups. This is true for industrial companies (GE, Siemens, Bosch, Philips, etc.) as well as big tech (Cisco, Intel, Apple, Samsung, etc.). The increasing impact on the space by Google, a fairly recent entrant in the hardware category, will be a development to keep an eye on in the coming months.

M&A activity is accelerating. Since the first version of the chart 18 months ago, a number of promising startups have been acquired: Nest and Oculus, of course, but also others such as Basis, Dropcam, SmartThings, Revolv, etc. Partly as a result of this, it’s pretty striking that there are comparatively few mid- to late-stage startups in the IoT space.

A global phenomenon: Innovation in the IoT space has been happening all around the world. Europe has been very active (Withings, Sigfox, Netatmo, Berg, and many others), as have other parts of the world (Australia with LIFX, etc.).

Some areas are getting overheated: The wearables market is probably getting close to its saturation point, at least for casual/consumer products (trackers, watches, etc.). The connected home segment has seen large companies make aggressive moves through acquisitions, investments or product launches (Google with Nest, Dropcam and Revolv, Apple with Homekit, Samsung with SmartThings, GE with Quirky/Wink), and the opportunity to become the home’s central “hub” is quickly becoming a deep-pocketed player’s game.

Plenty of other areas are just getting started: Innovation is accelerating at an incredible pace in a variety of segments such as digital health (patient monitoring), “invisibles” (connected pills, connected contact lenses), augmented reality, enterprise and industrial internet (asset tracking, energy management, machinery monitoring), smart cities, robotics, connected cars, aerials (drones, nanosatellites), user interfaces, software platforms and analytics, developer tools and ecosystems, security applications, and connectivity infrastructure. Just as certain verticals have neared critical mass, new and innovative applications have been spun up and built out.

In many ways, this is just the beginning. A lot can go wrong, but we are all in for an exciting ride.

This updated chart has been a team effort at FirstMark. Many thanks to David Rogg who handled the research and heavy lifting on the chart, as well as Caitlin Graham (logos), Dan Kozikowski (interactive version) and Sutian Dong (original version).

A Few Non-Obvious Things I Learned as a New VC

I joined FirstMark as a partner a little over 18 months ago now, and it’s been a thrilling ride.  It’s also felt like a steep learning curve: lots of nuances, and lots of institutional memory to absorb.  Below is a glimpse into what I’ve seen happening “behind the scenes” on the VC’s side to the table – stuff that was not obvious to me in my former roles as entrepreneur, angel investor or corporate incubator/strategic.

1.  A real commitment.  Like for many new VCs operating at the Series A level,  the biggest shock to the system was the realization that one gets to make very, very few investments – basically two or three a year.  You quickly find yourself having to choose between a number of opportunities you really like. Making a new investment is a big deal, and a decision that one has to live with for years to come. You also get to work with an entrepreneur very closely, and live up to their level of trust and expectations.  In a way, it feels like a marriage, except one where divorce is not really an option.  There’s an occasionally brutal asymmetry between the fundraising process (which can be quick and intense, especially if it is competitive) and what happens afterwards, which is a lot of hard work over a long period of time.  Both the entrepreneur and the VC would be well advised to get to know who they’re about to work with for the next few years of their lives.  You don’t need to be friends with your VC (although friendships develop over years of working together), but you do need a core of mutual respect and commitment to hard work and excellence, as well as a shared vision of the future.

 

2.  Conviction, not data. Early stage VCs (seed and Series A) operate in a daunting scarcity of data points. You get a few numbers, a few meetings with the founders, and also you see a bunch of companies, so you get a sense of how an opportunity compares to others. Other than that, and for all the thinking about data driven VC investing, the reality is that investment decisions are mostly about storytelling and forming personal conviction – painting a vision of the world where a company becomes hugely important. One consequence for entrepreneurs to bear in mind: VCs are really hungry for any data point that can help them.  It’s certainly true about the “big things” (revenue, traction, etc., especially as they compare to other opportunities the VC is seeing), but it’s also true for the “small things”, which can become become disproportionately important  (particularly if they add up), as the VC is trying to piece together a story: whether that’s signs of possible greatness (e.g., your former boss really insisted on putting $50k in your new venture) or trouble (being rude to the receptionist, consistently taking forever to reply to emails, etc).

 

3.  Not a single way to reach conviction:  VCs come in all sorts of flavors – some successful investors are deeply analytical (build roadmaps and investment thesis, get into details) while others are more “social” (relying on networks of trusted experts they’ve built over years to help them identify signal from noise).  What’s been interesting to me is that you find very successful investors on both sides of the spectrum, and also find those different types happily co-existing within the same firm.   Naturally, everyone is also heavily influenced by their professional history (what worked for them in the past as an operator or investor), as well as all sorts of personal criteria that often have nothing to do with the intrinsic merits of an opportunity – for example, the bar for a new investment will be naturally higher if an investor is already on 12 boards and always on the brink of being overwhelmed by the amount of work they face.   For the entrepreneur, it’s always a good idea to understand who they’re pitching to, as in any sales process, as an investor’s personal circumstances and background matter immensely.

NYC: A Natural Home for European Entrepreneurs

Last night I was invited to speak at the inaugural NYC European Tech Meetup.  There are tons of obvious reasons why the NYC and European tech ecosystems should work closely with one another, so a meetup on the topic was long overdue.  Congrats to Alban Denoyel and Anthony Marnell for starting it, and thanks for inviting me to speak, was a lot of fun.  Below are the slides I used – the presentation was meant to be a “State of the Union” of European tech in NYC, a high level overview fit for an inaugural meetup and get the conversation started.

 

Many thanks to David Rogg, our newest associate at FirstMark, for helping me with this.  I’m sure we missed some companies and people – if so, let us know in the comments, and we’ll update the presentation.