At FirstMark, we believe that every company is going to become not just a software company, but a data company.
For that to happen, it is essential that technologies that leverage data be democratized. For the foreseeable future, the global demand for digital innovation will continue to vastly outweigh the number of developers, engineers and scientists. Therefore, some of the technical complexity must be abstracted away to enable a broader group of people to build data-driven products and companies.
We all have insatiable appetite for video, both in our personal and professional lives. Time and again, video is shown to capture our attention better than any other medium. This is increasingly how we learn, explore, collaborate and get entertained.
However, especially in an enterprise context, creating professional-quality video remains a complex and costly endeavor. For all the capabilities of smartphones, most companies still need studio-level equipment to produce enterprise-grade videos: cameras, sound equipment, actors, post-production editing. The process is time-consuming, and not very scalable. Shooting a video in multiple languages, for example, requires multiple actors or dubbing, Any update requires everyone to go back to the studio.
But what if video could be just… code? What if it could be infinitely flexible and customizable at scale, as simple as an API call?
Today we’re excited to announce that FirstMark led a $12.5M Series A investment in Synthesia – a fast-growing startup that offers exactly that.
Synthesia makes creating a business video as simple as writing an email or putting together a powerpoint presentation – a compelling “text to video” experience.
Billions of dollars have been invested in the rise of data science and machine learning as mainstream disciplines in the world of business, one of the most exciting tech trends of the last (and next) decade.
In the enterprise, many of the applications of data science and machine learning ultimately produce a prediction: which customers are the most likely to buy? Or churn? Which transactions are most likely to be fraudulent? What part of town is likely to place the most food deliveries tomorrow afternoon?
However, powerful though it may be, there is one thing machine learning generally doesn’t tell you: once you have a prediction, what do you do with it? For example, once you have predicted high demand for food delivery in a certain part of town, how do you decide which delivery team member to dispatch where and when, to optimize for efficiency and maximize revenue and customer satisfaction?
Enter decision science. While the term has not crossed over to mainstream consciousness like its data science cousin, decision science has been around for decades. Also often known as Operations Research, it encompasses a variety of advanced analytical methods and quantitative models to help with decision-making and efficiency, including simulation, mathematical optimization, queuing theory, etc.
Business planning is, of course, one of the vital functions in the enterprise: hard to run a successful company beyond a certain size without a clear sense for objectives and resources.
Yet, to this day, business planning is a often a cumbersome, rigid and time-intensive process. Typically led by the finance team, it is largely done through email, excel spreadsheets and meetings. In large companies involving multiple business units and geographies, the process can take several months. As a result, business planning tends to effectively happen once a year.
I’m excited to announce that FirstMark has led a $12.5M Series A investment in Crossbeam, alongside Salesforce Ventures and Hubspot Ventures, with existing investors Uncork, First Round and Slack Fund participating.
At its core Crossbeam is a data escrow service. It allows companies that are partnered with each other (or looking to be) to combine their data sets (mostly customers and prospects) in a secure, trusted, compliant way, into a third party data warehouse (Crossbeam). They can then run analytics, without exposing the raw data behind the scenes, to identify opportunities to partner together.
Great power, great responsibility: as the data revolution continues, and companies big and small are increasingly able to collect and analyze massive amounts of data, they are facing rapidly mounting pressure to become much better data stewards. Security, privacy and compliance were largely an afterthought, but there are becoming crucial topics. Regulation is rapidly spreading around the world – witness for example GDPR, CCPA and the even more ambitious New York privacy bill.
As abundantly evidenced in the news, corporations are woefully unprepared to handle the challenge of identifying, managing and securing sensitive data in its various forms — PII, PHI, attorney privileged information, etc. The problem keeps getting worse as the amount of unstructured data keeps increasing in the enterprise. Too often, corporations tend to deal with it after the fact, once a crisis has occurred, in the context of litigation. Only a very small minority is able to address those issues proactively.
2017 was an extraordinary and crazy year in the world of cryptocurrencies. Prices skyrocketed (Bitcoin: +1,400%; Litecoin: +5,400%, Ethereum: +8,700%; Ripple +35,000%). ICOs raised over $3 billion. Crypto hedge funds emerged all over the map and a handful of blockchain startups reached unicorn-level valuations.
Almost inevitably, the price of individual cryptocurrencies will experience substantial volatility in 2018, and the first few days of January already look like a rollercoaster. Prices may very well crash altogether. In more ways than one, the space feels reminiscent of the dot-com days of the late 1990s, whether it is stories of newly minted bitcoin millionaires, the undeniable speculation rampant throughout the market, or the emergence of many weird things. While growing and expanding, the actual use cases of the blockchain still trail behind.
Taking a step back from the immediate frothiness, however, it seems that the crypto world has hit the point of no return, vaulting from a fringe movement into the mainstream collective consciousness, with strong interest both from the public and Wall Street. The blockchain has cemented its position as a new paradigm, which will only grow in importance, offering new solutions to the world, and new opportunities to entrepreneurs.
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.
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.
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:
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”
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: