I’ve been interested in the intersection of AI and crypto for a while (see AI & Blockchain: An introduction), and Numerai is one of the most exciting companies I came across in that world. Numerai is a new kind of crowdsourced quant hedge fund, which provides data for free and enables any data scientist around the planet to contribute models they believe will beat the stock market. Numerai offers its own token, called Numeraire, to incentivize participants.
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.
I am a venture capitalist. Here’s my daily routine.
8am: Wake up hungover from a crypto dinner.
While in bed, tweet how refreshed I feel from a great night on my Eightsleep and my 1-hour morning meditation.
9am: look at my reflection in the mirror and say “you are *not* getting disrupted by Tiger”. Repeat 10x, increasingly loudly.
10am: Look at the list of deal announcements on VC newsletters. Feel vaguely nauseous. “Should have done that one”. Then “oh, that one, too. And probably that one…”
11am: Haven’t tweeted in a while. Time for some thought leadership. What would Naval say?
11:30am: Debate how to reach out to a founder to tell them I “heard good things”. Email? Too cheugy. Text? Creepy. Telegram? Bit desperate. Signal? This job is so hard.
As the volume of data in the enterprise continues to explode, with ever large amounts stored in data warehouses and data lakes, the problem of data discovery has become an increasingly painful one. How do data analysts, data scientists and business people find not just data, but the right data for the problem they need to solve? How do they know how it was produced, how recently it was updated and whether that’s the right dataset they need to use? In addition, from an organization’s perspective, there’s a question of data governance – how to manage access in a way that preserves data security and privacy, and ensures compliance with data protection regulations (GDPR, CCPA, etc.).
Data catalogs have been a powerful response to those problems, and that category has seen renewed activity in the last couple of years with a whole new group of startup entrants.
At our most Data Driven NYC, we got a chance to chat Mark Grover, co-founder and CEO of Stemma and the co-creator of Amundsen, the leading open source data discovery and metadata engine. Mark built Amundsen while he was a product manager at Lyft and started Stemma to offer a fully managed Amundsen.
It was a fun conversation about the space. Below is the video and below that, the transcript.
Ask anyone who spends time in the data ecosystem, and the name “ClickHouse” is one that has come up countless times in conversations over the last few years.
ClickHouse is a real-time OLAP (meaning, analytical) database that is known for its performance and scalability, and has a wide footprint of users around the world.
ClickHouse started its life at Yandex, the Russian search giant. It was originally created as an internal web analytics tool called Metrica, which evolved around 2009 into “Clickstream Data Warehouse” or ClickHouse for short.
The product was open sourced in 2016 and became a very popular project, with adoption at impressive scale by a number of companies including Yandex (10s of trillions of rows), Uber, Ebay, Cloudflare, Spotify, Deutsche Bank, and more.
ClickHouse was spun out into early 2021 into ClickHouse, Inc., a commercial company co-founded by Aaron Katz, Alexey Milovidov (ClickHouse’s creator), and Yury Izarilevsky (ex-Google VP Engineering), with a focus on bringing ClickHouse to all types of companies via a managed version.
ClickHouse Inc raised a $50M Series A announced in September, followed closely by a $250M Series B last month, in which my firm, FirstMark, participated.
It was a treat to welcome Aaron Katz, the Co-Founder and CEO of ClickHouse, Inc. to Data Driven NYC. Prior to co-founding ClickHouse, Aaron had extensive experience as a world-class sales leader, most recently as the Chief Revenue Officer at Elastic and the Senior Vice President of Enterprise Sales at Salesforce
Below is the video and below that, the transcript.
For anyone interested in a quick overview of our long-form 2021 Machine Learning, AI and Data (MAD) Landscape, here are the Cliffs Notes! My co-author John and I did a presentation at our most recent Data Driven NYC, focused on top 10 trends in this year’s landscape.
As a preview, here they are:
Every company is a data company
The big unlock: data warehouses and lakehouses
Consolidation vs data mesh: the future is hybrid
An explosive funding environment
A busy year in DataOps
It’s time for real time
The action moves to the right side of the warehouse
The rise of AI generated content
From MLOps to ModelOps
The continued emergence of a separate Chinese AI stack
Below is the video from the event, and below that, the transcript.
In the admittedly small world of people who obsess over data technologies, one of the hottest topics of the last year has been the “data mesh”.
Created by Zhamak Dehghani of ThoughtWorks, the concept struck a chord and made the rounds in countless conversations on Twitter and elswhere.
As I highlighted in the 2021 MAD Landscape, the data mesh concept is both a technological and organizational idea. A standard approach to building data infrastructure and teams so far has been centralization: one big platform, managed by one data team, that serves the needs of business users. This has advantages, but also can create a number of issues (bottlenecks, etc). The general concept of the data mesh is decentralization – create independent data teams that are responsible for their own domain and provide data “as a product” to others within the organization. Conceptually, this is not entirely different from the concept of micro-services that has become familiar in software engineering, but applied to the data domain.
It was a real treat to get to chat with Zhamak at our most recent Data Driven NYC.
Below is the video and below that, the transcript.
Full resolution version of the landscape image here
It’s been a hot,hot year in the world of data, machine learning and AI.
Just when you thought it couldn’t grow any more explosively, the data/AI landscape just did: rapid pace of company creation, exciting new product and project launches, a deluge of VC financings, unicorn creation, IPOs, etc.
It has also been a year of multiple threads and stories intertwining.
One story has been the maturation of the ecosystem, with market leaders reaching large scale and ramping up their ambitions for global market domination, in particular through increasingly broad product offerings. Some of those companies, such as Snowflake, have been thriving in public markets (see our MAD Public Company Index), and a number of others (Databricks, Dataiku, Datarobot, etc.) have raised very large (or in the case of Databricks, gigantic) rounds at multi-billion valuations and are knocking on the IPO door (see our Emerging MAD company Index – both indexes will be updated soon).
But at the other end of the spectrum, this year has also seen the rapid emergence of a whole new generation of data and ML startups. Whether they were founded a few years or a few months ago, many experienced a growth spurt in the last year or so. As we will discuss, part of it is due to a rabid VC funding environment and part of it, more fundamentally, is due to inflection points in the market.
In the last year, there’s been less headline-grabbing discussion of futuristic applications of AI (self-driving vehicle, etc.), and a bit less AI hype as a result. Regardless, data and ML/AI-driven application companies have continued to thrive, particularly those focused on enterprise use cases. Meanwhile, a lot of the action has been happening behind the scenes on the data and ML infrastructure side, with entire new categories (data observability, reverse ETL, metrics stores, etc.) appearing and/or drastically accelerating.
To keep track of this evolution, this is our eighth annual landscape and “state of the union” of the data and AI ecosystem – co-authored this year with my FirstMark colleague John Wu. (For anyone interested, here are the prior versions: 2012, 2014, 2016, 2017, 2018, 2019 (Part I and Part II) and 2020.)
For those who have remarked over the years how insanely busy the chart is, you’ll love our new acronym – Machine learning, Artificial intelligence and Data (MAD) – this is now officially the MAD landscape!
Today, Dataiku is announcing a major new financing – a total of $400m at a $4.6B valuation, led by Tiger Global (which had also invested in the company’s Series D), alongside a great group of existing and new investors.
While financings are ultimately just milestones, this is certainly a testament to the remarkable progress the company has been making towards becoming a major global software player, as it has scaled to hundreds of customers around the world and some 750 employees (and yes, hiring a lot more).
Beyond the headlines and high-fives, what is the story? Here’s a quick industry backgrounder and reminder for anyone new to the company.
A huge part of the data world has been historically focused on business intelligence, with both historical players (Tableau, Microsoft’s Power BI, Google Looker) and newer players (SiSense, Mode, etc.). Business intelligence tools enable you to analyze the past and the present of your business: “which region performed best last quarter?”, “who are our best salespeople?” etc. This is sometimes referred to as descriptive analytics.
Dataiku is a leader in another part of the data world, which different people call different names: data science, enterprise AI (for artificial intelligence), enterprise machine learning. Beyond the semantics, the core idea is to make it possible to asnwer questions about the future of your business, based on the analysis of historical data: “which customers are most likely to buy this product?”, “which customers are most likely to churn?”, “which transaction is most likely to be fraudulent?”, “which region is most likely to show strong demand this month?”. This area is sometimes referred to as predictive analytics.
This morning, Sketchfab announced that it was joining the Epic Games family.
From inception, Sketchfab has been a visionary company in the creator economy, pioneering the emergence of 3D as a key format on the web. It built the best 3D viewer on the market, and leveraged it to build a remarkable community of 3D creators and enthusiasts all around the world. It navigated the ups and downs of the “VR Winter” and, through entrepreneurial grit and great execution, emerged on the other side a stronger, profitable company – a journey that CEO Alban Denoyel documented with remarkable transparency.
Epic is the perfect partner for Sketchfab. It has epic (yes) plans for building the Metaverse (see Matthew Ball’s excellent essays on the Metaverse here and Epic here). The Metaverse will be a heavy consumer of 3D, AR and VR content, and Sketchfab fits perfectly within that vision. Sketchfab will continue operating largely as an independently branded service, and will be able to access Epic’s resources and distributions capabilities.
This last year has seen tremendous levels of activity for early stage startups in the data infrastructure ecosystem. At our most recent Data Driven NYC, we featured some of the rising stars:
Nick Schrock, Founder & CEO, Elementl (Dagster) | Elementl is building the next generation of open source data tools including Dagster, the open-source data orchestrator for machine learning, analytics, and ETL.
DeVaris Brown, Founder & CEO, Meroxa | Meroxa is a real-time data platform that gives data teams the tools they need to build real-time infrastructure in minutes.
Abe Gong, Founder & CEO, Superconductive (Great Expectations) | Superconductive is the team behind Great Expectations, the leading open source tool for defeating pipeline debt through data testing, documentation, and profiling. The company’s mission is to revolutionize the speed and integrity of data collaboration.
A member of our Emerging MAD Index of companies on their path to an IPO, Confluent is a very interesting company in a strategic part of the data space, providing infrastructure for real-time data streaming – what it nicely calls “data in motion”, in contrast to the world of batch processing or “data at rest”.
I had the pleasure of hosting the company’s co-founder and then CTO, Neha Narkhede, at Data Driven NYC back in 2016, and her great talk remains entirely relevant to understand the premise behind the company and its core technical foundation.
Confluent recently released its full S-1, and will trade under the stock ticker CFLT on the NASDAQ.
In the same vein as previous “Quick S-1 teardowns” (see Palantir, Snowflake, nCino), here are some high level thoughts and quick highlights, from my colleague John Wu and I.
