The New Gold Rush? Wall Street Wants your Data

 

trading-data

 
A few months ago, Foursquare achieved an impressive feat by predicting, ahead of official company results, that Chipotle’s Q1 2016 sales would be down nearly 30%. Because it captures geo-location data from both check-ins and visits through its apps, Foursquare was able to extrapolate foot-traffic stats that turned out to be very accurate predictors of financial performance.
 
That a social media company could be building a data asset of immense value to Wall Street is part of an accelerating trend known as “alternative data”. As just about everything in our lives is getting sensed and captured by technology, financial services firms have been turning their attention to startups, with the hope of mining their data to extract the type of gold nuggets that will enable them to beat the market.
 
Could working with Wall Street be a business model for you?
 
The opportunity is open to a wide range of startups.  Many tech companies these days generate an interesting “data exhaust” as a by-product of their core activity.  If your company offers a payment solution, you may have interesting data on what people buy. A mobile app may accumulate geo-location data on where people shop or how often they go to the movies.  A connected health device may know who gets sick when and where.  A commerce company may have data on trends and consumer preferences. A SaaS provider may know what corporations purchase, or how many employees they hire, in which region. And so on and so forth.
 
At the same time, this is a tricky topic, with a lot of misunderstandings. The hedge fund world is very different from the startup world, and a lot gets lost in translation.  Rumors about hedge funds paying “millions” for data sets abound, which has created a distorted perception of the size of the financial opportunity.  A fair number of startups I speak with do incorporate idea of selling data to Wall Street into their business plan and VC pitches, but how that would work exactly remains generally very fuzzy.
 
If you’re one of the many startups sitting on a growing data asset and trying to figure out whether you can make money selling it to Wall Street, this post is for you: a deep dive to provide context, clarify concepts and offer some practical tips.
 

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HyperScience and the Enterprise AI Opportunity

 

Today our portfolio company HyperScience is coming out of stealth and talking a bit more about what they’ve been working on for the last couple of years. We have been involved for a little while already as lead Series A investors, and we are excited to now be joined today by our friends at Felicis, a great addition to a strong syndicate from both coasts that also includes Shana Fisher (Third Kind) who led the seed, AME Cloud Ventures, Slow Ventures, Acequia, Box Group and Scott Belsky.  The company is announcing today a total of $18M in Series A investment.

HyperScience offers AI solutions targeting Global 2000 corporations and government institutions. Their products enable those customers to automate or accelerate a lot of dusty back office processes, particularly those that involve the manipulation and triage of large amounts of documents and images.

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Dataiku or the Early Maturation of Big Data

dataiku_logo_color
 
In the early days of Big Data (call it 2009 to 2014), a lot had to do with experimentation and discovery.  Early enterprise adopters would play around with Hadoop, the then-new open source framework with a funny name, trying to figure out where the technology fit in the broader landscape of databases and data warehouses.  People would also try to figure out what a “data scientist” was – a statistician who can code? An engineer who knows some math?  It was a time of hype, immature products and trial and error.

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Investing in Frontier Tech

drone

Over the last few months, the usual debate around unicorns and bubbles seems to have been put on hold a bit, as fears of a major crash have thankfully not materialized, at least for now.

Instead another discussion has emerged, one that’s actually probably more fundamental. What’s next in tech? Which areas will produce the Googles and Facebooks of the next decade?

What’s prompting the discussion is a general feeling that we’re on the tail end of the most recent big wave of innovation, one that was propelled by social, mobile and cloud.  A lot of great companies emerged from that wave, and the concern is whether there’s room for a lot more “category-defining” startups to appear.  Does the world need another Snapchat? (see Josh Elman’s great thoughts here).  Or another marketplace, on-demand company, food startup, peer to peer lending platform? Isn’t there a SaaS company in just about every segment now? And so on and so forth.

One alternative seems to be “frontier tech”: a seemingly heterogeneous group that includes artificial intelligence, the Internet of Things, augmented reality, virtual reality, drones, robotics, autonomous vehicles, space, genomics, neuroscience, and perhaps the blockchain, depending on who you ask.

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Phosphorus and the Rise of the New Genomics Startup

 

As we are perhaps reaching the end of a cycle of innovation in tech – the one that resulted from the simultaneous emergence of social, mobile and cloud – and collectively pondering what’s next, one of the areas I’ve found particularly exciting recently is the intersection of Big Data and life sciences.

A little over two years ago, in connection with my investment in Recombine, a genomics startup, I wrote (here) about another powerful combination of trends: the sharp drop in the cost of sequencing the human genome, the maturation of Big Data technologies, and the increasing commoditization of wet lab work.

The fundamental premise was, and still very much is, as follows:

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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.

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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.

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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.

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The State Of Big Data in 2014: a Chart

Note: This post appeared on VentureBeat, here.

It’s been almost two years since I took a first stab at charting the booming Big Data ecosystem, and it’s been a period of incredible activity in the space. An updated chart was long overdue, and here it is:

(click on the arrows at the bottom right of the screen to expand)

A few thoughts on this revised chart, and the Big Data market in general, largely from a VC perspective:

Getting crowded: Entrepreneurs have flocked to the space, VCs have poured money into promising startups, and as a result, the market is starting to get crowded. Certain categories like databases (whether NoSQL or NewSQL) or social media analytics feel ripe for consolidation or some sort of shakeout (which may have already started in social analytics with Twitter’s acquisitions of BlueFin and GNIP). While there will be always room for great new startups, it seems that a lot of the early bets in the broader infrastructure and analytics segments have been made at this stage, and the bar for success is getting higher – which doesn’t mean that VC money will stop pouring in. In terms of this specific industry chart, we’ve clearly reached the limit of how many companies we can fit one page. I’m sure there are a number of great companies we either missed or didn’t have enough space to include – apologies in advance to those, and I’d love to hear people’s thoughts and suggestions in the comments section about who else should be included.

Still early: Overall, we’re still in the early innings of this market. Over the last couple of years, some promising companies failed (for example: Drawn to Scale), a number saw early exits (for example: Precog, Prior Knowledge, Lucky Sort, Rapleaf, Nodeable, Karmasphere, etc.), and a handful saw more meaningful outcomes (for example: Infochimps, Causata, Streambase, ParAccel, Aspera, GNIP, BlueFin labs, BlueKai). Meanwhile, some companies seem to be reaching significant scale, and have raised spectacular amounts of money (for example, MongoDB has now raised over $230M, Palantir almost $900M and Cloudera $1B). But overall, we’re still early in the curve in terms of successful IPOs (Splunk or Tableau notwithstanding) and large exits, although the big companies are getting more acquisitive in the space (Oracle with BlueKai, IBM with Cloudant). In many segments, startups and large companies are jockeying for position and no obvious leader has emerged.

Hype, meet reality: A few years into a period of incredible hype, is Big Data still a thing? While less press worthy, the next couple of years are going to be hugely important for this market, as corporations start moving Big Data projects from experimentation to full production. While they will lead to rapidly increasing revenues for some Big Data vendors, those deployments will also test whether Big Data can truly deliver on its promise. Meanwhile, the fundamental need for Big Data technology keeps increasing, as the deluge of data keeps accelerating, powered in part by the rapidly emerging Internet of Things industry.

Infrastructure: Hadoop seems to have solidified its position as the cornerstone of the entire ecosystem, but there are still a number of competing distributions – this will probably need to evolve. Spark, another open source framework that builds on top of the Hadoop Distributed File System, is getting a lot of buzz right now because it promises to fill in the places where Hadoop has been weak, namely interactive speeds and good programming interfaces (and early signs seem to point to fulfilling that promise). Some themes (for example, in memory or real time) continue to be top of mind; others are appearing (for example, there’s a whole new generation of data transformation/munging/wrangling tools, including Trifacta, Paxata and DataTamer). Another key discussion is whether enterprise data will truly move to the cloud (public or private), and if so, how quickly. Many will argue that Fortune 500 companies will keep their data (and the software to process it) on premise for years to come; a generation of Hadoop-in-the-cloud startups (Qubole, Mortar, etc.) will argue that all data is moving to the cloud long term.

Analytics: This has been a particularly active segment of the Big Data ecosystem in terms of startup and VC activity. From spreadsheet type interfaces to timeline animations and 3D visualizations, startups offer all sorts of different analytical tools and interfaces, and the reality is that different customers will have different type of preferences, so there’s probably room for a number of vendors. Go to market strategies differ as well – some startups focus on selling tools to data scientists, a group that is still small but growing in numbers and budget. Others adopt the opposite approach and sell automated solutions targeting business users, bypassing data scientists altogether.

Applications: As predicted, the action has been slowly but surely moving to the application layer of Big Data. The chart highlights a number of exciting startups that are fundamentally powered by Big Data tools and techniques (certainly not an exhaustive list). Some offer horizontal applications – for example, Big Data powered marketing, CRM tools or fraud detection solutions. Others use Big Data in vertical specific applications. Finance and ad tech were always early leaders in adopting Big Data, years before it was even called Big Data. Gradually, the use of Big Data is spreading to more industries, such as healthcare and biotech (particularly in genomics) or education. This is only the beginning.

Many thanks for my FirstMark colleague Sutian Dong for doing a lot of the heavy lifting on this chart. My former colleague Shivon Zilis of Bloomberg Beta contributed immensely to prior versions of this chart.

 

Can the Bloomberg Terminal be “Toppled”?

In the eye of some entrepreneurs and venture capitalists, the Bloomberg terminal is a bit of an anomaly, perhaps even an anachronism.  In the era of free information on the Internet and open source Big Data tools, here’s a business that makes billions every year charging its users to access data that it generally obtains from third parties, as well as the tools to analyze it.  You’ll hear the occasional jab at its interface as reminiscent of the 1980s.  And at a time of accelerating “unbundling” across many industries, including financial services, the Bloomberg terminal is the ultimate “bundling” play: one product, one price, which means that that the average user uses only a small percentage of the terminal’s 30,000+ functions.  Yet, 320,000 people around the world pay about $20,000 a year to use it.

If you think that this sounds like a perfect opportunity for disruption or “unbundling” at the hand of nimble, aggressive startups, you’re not alone.  I spent four years at Bloomberg Ventures, and this was a topic that I heard debated countless times before, during and after my tenure there. Most recent example: a well written article in Institutional Investor a few weeks ago declared the start of “The Race to Topple Bloomberg“, with a separate article highlighting my friends at Estimize and Kensho as startups that “Take Aim at Bloomberg“.

Yet, over the years, the terminal has seen its fair share of would be disruptors come and go. Every now and then, a new wave of financial data startups seems to be appearing, attempting to build businesses that, overtly or not, compete with some parts of the Bloomberg terminal.  Soon enough, however, those companies seem to disappear, through failure, pivot or acquisition.

What gives? And where are the opportunities for financial data startups?

Frontal assault: good luck

To start, Bloomberg is not exactly your run-of-the-mill, lazy incumbent. Perhaps I drank too much of the Kool-Aid while I was there, but I left the company very impressed.  Bloomberg, which was itself a startup not that long ago, comes armed with a powerful brand, deep pockets, a fiercely competitive culture, a product that results from billions of dollars of R&D investment over the years, and a technology platform that basically never goes down or even slows down, supported by generally excellent customer service.

But great incumbents have been disrupted before.  So there is perhaps another set of less immediately apparent reasons why the terminal has so far been very resilient to disruption by startups:

1.  It is protected by strong network effects.  One surprisingly misunderstood reason to the long term success of the Bloomberg terminal is that, beyond the data and analytics, it is fundamentally a network.  In fact, it was probably the first ever social network, long before the term was coined. Although some believe that its cachet as a status symbol is starting to erode, “the Bloomberg” (as it is often called) has been for decades the way you communicate with other finance professionals (for legitimate or not so legitimate reasons).  In its relevant target market, everyone is on it and uses it all day to communicate with colleagues, clients and partners. Web based services (Facebook, Dropbox, Gmail), often banned in financial services companies, haven’t made much of a dent in that, at least for desktop communication.

2.  It is an aggregation of niche products.  In the world of financial data, there is enough specificity to each asset class (and subsegment thereof) that you need to build a substantially different product for each, which requires deep expertise, as well as a huge amount of effort and money, to address a comparatively small user base (sometimes just a few tens of thousands of people around the world).  Bloomberg started with fixed income data and over many years, used its considerable cash flow to gradually conquer other classes (still a work in progress, to this day).  So disrupting the Bloomberg is not as “easy” as coming up with a great one-size-fits-all product.  It would take immense amounts of venture capital money to build a direct competitor across all those niches.

3.  It’s not “just” a technology play.  Providing financial data at scale is not a pure technology play, so it is not a matter of coming up with radically better technology to aggregate and display data, either.  At this stage at least, there is a whole web of human processes, relationships and contracts with underlying data providers that has been put on place over many years.

4.  It’s a mission critical product. This is a key point.  In the financial world, data is used to make gigantic bets, so total accuracy and reliability is an absolute must – which makes people cautious when experimenting with new products, particularly built by a startup.

The Bloomberg terminal business may face macro headwinds, as described in the Institutional Investor piece (dwindling of the number of relevant jobs on Wall Street and a global shift from desktop data to data feeds).  However, as a result of the above, I don’t see the Bloomberg terminal being entirely “toppled” by any one given startup anytime soon, and I think even competing directly with any of its key functionalities (unbundling) is a tall order for startups, even with access to large amount of VC money.  Not that it can’t be done – I just think there are lower hanging fruits out there and some real benefit to position away from the Bloomberg.

Where are the opportunities in financial data?

While I don’t see much opportunity for startups to build a Bloomberg terminal replacement (or a a replacement to Thomson Reuters or Factset, to be clear), I think there are fertile grounds “around” and “below” the terminal – meaning in areas where the company is unlikely to want to go.

Specifically, I believe there are going to be ongoing opportunities to apply some of the quintessential internet concepts and processes (networks, crowdsourcing, etc) as well as new-ish technology (Big Data)  to the world of financial data, including:

1.  Finance networks/communities.  Like the Bloomberg terminal did, some of the more interesting “adjacent” plays opportunities will marry data, tools and community.  Historically, capital markets haven’t seen much of a sharing culture (lots of nuances here, I know), which is in part due to the nature of finance investing itself – however, it’s going to be interesting to see how, at least in certain areas, that culture will evolve as digital natives rise in the ranks of their organizations.  Beyond early entrants Stocktwits and Covestor (which generally target a more casual audience), examples of such professional communities include SumZero, initially for Buy Side analysts but now wider, and more recently Quantopian, an algorithmic trading community where scientifically educated people and other quant types share strategies and algorithms.  Early stage startup ThinkNum thinks financial models should be shared and wants to the “Github” for financial models.  What else can be shared?

2.  App stores. The app store model is an interesting way of leveraging the expertise of a “crowd” of specialized third party developers (Bloomberg launched its own a couple of years ago). OpenFin, for example, provides infrastructure to enable the deployment of in-house app stores, addressing the necessary compliance, security and inter-operability requirements (having data flow from one tool to the other). A combination of an in-house app store infrastructure with some best of breed applications (say, a ChartIQ, which provides HTML5 financial charts, including technical analysis tools) is an interesting approach to target the portion of the market “below” the terminal, as  companies that cannot afford a full on terminal infrastructure could pick and choose the apps they need and have them work in their environment.

3.  Crowdsourced data.  From Estimize (which crowdsources analyst estimates) to Premise (which crowdsources macroeconomic data through an army of people around the world equipped with mobile phones), a whole new way of capturing financial data has emerged. Quandl, a financial data search engine, has aggregated over 8 million financial and economic datasets through both web crawling and crowdsourced, community contributions.  Once such a data platform has been built, could third party developers add analytic and visualization tools on top, essentially resulting in a crowdsourced “terminal” of sorts that would be reliable enough, at least for non mission critical, non real time use cases?

4.  Big Data “insights”: Extracting signal from data is obviously the end game here, and interesting startups are heavily focused on those opportunities, from Dataminr (social data analytics for Wall Street) to Kensho (which is working on “bringing the intelligent assistant revolution to finance”). In terms of market positioning, it is unclear to which extent those technologies compete with the Bloomberg terminal (which, for example, has been very active on the social data front), or potentially complete it.

The big question facing entrepreneurs and VCs alike is how to scale those businesses and turn them into billion dollar companies in a context where solidly entrenched platforms have a stronghold on arguably the juiciest part of the market. But overall I believe that we’re only going to see more startups going after financial data opportunities, with potential for some serious wins – I’m excited to see how it all evolves.

Recombine

The field of bioinformatics is having its “big bang” moment.   Of course, bioinformatics is not a new discipline and it has seen various waves of innovations since the 1970s and 1980s, with its fair share of both exciting moments and disappointments (particularly in terms of linking DNA analysis to clinical outcomes).  But there is something special happening to the industry right now, accelerated by several factors:

•      The cost of full genome sequencing has been dropping precipitously, in fact a lot faster than Moore’s law would have suggested.  Illumina just released brand new machines that make the $1,000 full genome sequencing a realistic possibility.  As a result, an extraordinary amount of data is going to become available at reasonable cost (5.5TB or 6.3 Billion bases… per patient).

•      Big Data technology has had its own, separate evolution, and there is now an arsenal of tools to process and analyze massive amounts of data, at a comparatively cheap cost.

•      Wet lab work has become a more standardized and increasingly automated process, considerably reducing the “friction” involved in collecting and processing physical samples. The cost of setting up biology labs, while still high, is starting to decrease, and molecular techniques are no longer the limiting step in genomic analysis.

As a result of the above, biology is rapidly evolving from being predominantly driven by traditional life sciences research to being largely driven by software and Big Data.  This evolution considerably reduces the capital required to build a successful venture in the space.  It also opens up the field to a new generation of startups run by inter-disciplinarian teams that have at least as much of a software and data science background as a biology background.  A whole new world of bio-hackers is also emerging, from synthetic biology to personalized medicine, the possibilities are immense and the impact on our lives potentially unparalleled.  It is entirely possible that the next generation of great entrepreneurs will be building “biology 2.0” companies, rather than mobile apps.

This opportunity has not been lost on entrepreneurs and the last 3 years or so have seen a rapid acceleration of startup creation, in a wide range of area from diagnostics (Counsyl) to cloud platforms (DNANexus) to lab automation (Benchling, Transcriptic).  Interestingly but not surprisingly considering the above, most of those startups are funded by technology, rather than life sciences, venture capital firms.

Today I’m excited to announce that FirstMark is partnering with Recombine, a New York based startup that very much operates at this intersection between software, Big Data and biology, as its lead Series A investor. Recombine’s CEO, Alex Bisignano, symbolizes this new generation of entrepreneurs who have deep knowledge in multiple technical fields.  He has built around him a great, multi-disciplinarian team, and benefits from the deep industry knowledge and expertise of co-founder Dr. Santiago Munne, the owner of Reprogenetics and pioneer in pre-implantation genetic diagnosis.

Recombine’s core focus is the field of fertility and reproductive genetics, and it has had a spectacular early start with CarrierMap, its first product, generating a profitable multi-million dollar business with a comparatively small seed investment. The CarrierMap test is the most comprehensive, cost-effective, carrier screen on the market, and has already helped thousands of couples to identify and mitigate the risk of passing on serious illnesses to their children.  CarrierMap is sold exclusively through doctors and clinics, it is not a Direct to Consumer product (and therefore falls in a different category than 23andMe).

Beyond this initial focus, Recombine has ambitious plans to fully leverage Big Data technology to help decode the myriad aspects of our genome that are still not well understood. They have already obtained Institutional Review Board (IRB) approval for their first large-scale study, and the company is currently assembling a crack team of data scientists in New York City.  If you have deep expertise in data science field, this is an opportunity to help bring about a revolution in personalized medicine. Come join us!

 

Thomson Reuters CTO Series (Podcast)

Thomson Reuters CTO James Powell runs a great series of podcasts where he interviews people in the technology world about topics of relevance to his organization.  I was fortunate to be invited to speak with James about the Internet of Things and Big Data, and it was a lot of fun.   Below is the podcast, uploaded on SoundCloud.  Thanks to James Powell and Dan Cost for the opportunity.

Launching New Sites for Data Driven NYC and Hardwired NYC

Some updates on the event/community front:

1) A little while ago, I changed the name of the data event I’ve been organizing from “NYC Data Business Meetup” to “Data Driven NYC”.   I originally started the event mostly as experiment, and didn’t give much thought to branding (so yeah, that was a terrible name).  The event has now grown quite a bit (over 3.700 members as I write this), so it was time for a better name; also at this stage, it feels more like a community than “just” a meetup, so I wanted a name that reflected this reality.

2) Back in June, I launched a new community called “Hardwired NYC”.  It covers startups, technologies and products at the intersection of the physical and digital worlds, including topics like 3D printing, Internet of Things, wearable computing, etc.  I developed a strong interest in those areas through my involvement in the Big Data world – the Internet of Things, in particular, is deeply intertwined with Big Data (the proliferation of sensors has been contributing to the Big Data “problem”; equally  the Internet of Things will be highly dependent on Big Data technologies if it is to deliver on its promise).

3) As Hardwired NYC is taking off fast (more than 700 members after just two events), I figured that both events/communities should have their own website with full video libraries, including for people who don’t live in New York and are interested in the content. So, with the great help of my FirstMark colleague Dan Kozikowski,  I’m launching this week www.datadrivennyc.com and www.hardwirednyc.com.  Both sites have a “Watch” section where, from now on, I will post pictures and videos of events (as opposed to this blog).

Data Driven NYC

Hardwired screenshot

Big Data 101 Presentation

A few weeks ago, I was invited to do a couple of guest lectures at NYU (as part of the excellent “Ready, Fire, Aim” entrepreneurship class that Lawrence Lenihan, now my partner at FirstMark, has been doing for a while there) and at The New School (as part of a Big Data course organized by Debra Anderson and Greta Knutzen).  Thought I’d share the slide deck I had prepared for those classes.  Very much a Big Data 101 class for a college-level audience that had had little or no exposure to the key concepts prior to the class.