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Within the ever vibrant world of the “Trendy Knowledge Stack” (an ecosystem of largely younger tech startups that characterize the rising era of information software program distributors, and combine properly with each other), Hex has been getting rising visibility and momentum. At its core, Hex is a collaborative information platform the place groups can discover, analyze, and share. It goals to carry collectively one of the best of notebooks, BI & docs right into a seamless, collaborative UI.
The corporate was based in 2019 and also you raised a complete of $73.5 million in enterprise capital so far, together with most not too long ago a $52 million Collection B.
CEO Barry McCardel joined us at Knowledge Pushed NYC for a deep dive in to the product, the corporate, the information area and his journey from doing “unholy issues in Excel” as a younger marketing consultant to constructing a terrific startup.
Under is the video and full transcript.
(As all the time, Knowledge Pushed NYC is a staff effort – many due to my FirstMark colleagues Jack Cohen, Karissa Domondon Diego Guttierez)
VIDEO:
TRANSCRIPT [edited for clarity and brevity]:
[Matt Turck] Why does the world want a collaborative workspace for information groups? What’s the large downside that you just’re engaged on fixing?
I’ve been working in information successfully my complete profession. I began as an undergrad, actually, doing a bunch of stuff, simply writing R scripts and all this analysis stuff earlier than information science was even a factor, actually. After which was doing unholy issues in Excel as a marketing consultant. Then I used to be at Palantir for 5 years, the place I acquired publicity all throughout a bunch of various technical issues. Then I labored actually intently with our information staff at my final firm. And all alongside the best way I mainly noticed the identical set of issues. In essence, Hex is supposed to unravel these.
So the very first thing we actually set out on and the factor that was a very acute downside that we wished to unravel was across the potential to share work. It’s this quite common factor and we noticed it up shut at our final firm we had been at which is you might have information analysts and information scientists and simply individuals working with information all around the enterprise doing actually attention-grabbing, superior issues. They’re getting in. They’re asking and answering questions. They’re driving perception.
Then the precise potential to share and publish that all through the group is terrible. It’s actually a catastrophe. You may have individuals screenshotting charts out of Jupyter Pocket book and pasting them in Google Docs. You may have individuals exporting a CSV from a BI device to allow them to construct the proper match on this different factor after which put that in a deck. Then you might have individuals hacking collectively scripts to attempt to construct a pipeline to place the forecast within the warehouse so you possibly can have a look at it within the BI device.
It was this enormous mess. So we began actually specializing in that downside. The preliminary factor we had been centered on was, how will you assist information scientists who’re engaged on one thing like a Jupyter Pocket book, take that and share it with others in a method that’s interactive and helpful and usable? As we began stepping into that, we realized the ache was actually a lot deeper than that. It was truly like individuals had been simply pissed off with the entire stack. You had people leaping round between instruments relying on whether or not they’re utilizing SQL or Python or no code. You’ve acquired groups actually unable to collaborate. The entire versioning and real-time collaboration for all that is only a mess. It’s very regressive in comparison with instruments in different areas, like Figma or Google Docs.
Then there’s simply an quantity of overhead and ache to getting these instruments up and operating anyway that’s truly actually arduous. There’s a really traditional expertise the place you’ll see a brand new information scientist will be part of and the primary two weeks are actually nearly getting all the proper packages put in domestically of their Jupyter surroundings after which ensuring that’s synced up. You wind up with this overhead that’s each very irritating for the people who find themselves doing these workflows but additionally prevents lots of people from accessing.
So again to your query, Hex is basically meant to be three huge issues. It’s an incredible, collaborative surroundings for with the ability to do evaluation and information science. It’s acquired a pocket book UI that’s simply completely magical. I’ll present that to you in a bit. It’s very, very straightforward to take your work and share it and publish it as an interactive information app that anybody can use. Then that work is then saved and arranged in what we name a Information Library which makes it very straightforward for anybody else within the group to find and profit from the work that the information staff has been doing. So mission sensible, that’s actually what we’re about and we constructed a product that actually addresses that finish to finish.
Nice. And it’s, by definition, meant to be very inclusive, proper? So it’s information scientists. It’s information analysts. It’s enterprise individuals as properly. You may have an expression that I learn someplace, which I actually preferred, which was the “analytically technical.”
Analytically technical, yeah. It’s attention-grabbing as a result of you concentrate on a number of the huge modifications which have occurred in the previous couple of years. You see this explosion in people who find themselves information literate. They’re even, I’d name them, considerably technical. And there’s extra individuals who know Python, positive. There’s much more individuals who know SQL. And lots of people have both realized SQL on the job or are available out of undergrad with that skillset. There’s additionally this a lot greater inhabitants of folks that I’d argue which are technical in their very own method; which when you’re an Excel Energy consumer and also you’re writing deeply nested features or VBA and even just a few pivot tables or IFs, you’re mainly writing code. I’d argue you’re writing code. You might be technical in a roundabout way.
And I feel conventional information science and analytics instruments have truly been a excessive tower. They’re troublesome for these individuals to entry. And so one of many issues that’s actually attention-grabbing for us, what we see in our clients, is now we have lots of customers. In actual fact, most of our clients, many of the customers are largely writing SQL. And that’s very totally different than what you may consider if you consider a pocket book surroundings, which is historically very related to Python and, quote-unquote, “information science.” However Hex makes it very straightforward to trip between SQL and Python. You may collaborate between these. And so it’s very inclusive.
It’s very cool for us to see that our clients will begin with a really small variety of information scientists, a pair people who find themselves migrating their workflows over from Jupyter however then will explode to the place you see all kinds of individuals utilizing Hex to ask and reply questions. That’s one thing we’re very enthusiastic about. I really feel like we’re simply nonetheless on the tip of the iceberg. And we consider it as constructing a platform that has a low ground and a excessive ceiling. We wish to have a platform that anybody can are available and ask and reply questions. Nevertheless it doesn’t arbitrarily high out.
And I feel that’s an enormous distinction between the final era of instruments, which is like, “Okay. This can be a no-code factor. It’s acquired a low ground and a low ceiling.” However the second you wish to do one thing extra complicated, you’ve topped out. And now you might have a UX SqlRunner. Medium ground, medium ceiling. After which, “Okay. Now I’m over in my Jupyter Pocket book,” excessive ground, excessive ceiling. I problem why this must be three fragmented issues. And I feel we’ve performed a terrific job thus far with the ability to carry a few of these extra collectively.
So to take a few of us by a bit and drill into the following stage, so the core is a pocket book. We talked about exhibiting the product. So I’m excited for a product demo. However simply at a excessive stage earlier than we soar into the demo and perhaps to make it inclusive for everybody, so only a 10-second definition of what a pocket book truly is.
Yeah positive so notebooks have been round for a very long time. As legend has it, they had been first pioneered at Mathematica. And the commonest one now could be a undertaking known as Jupyter. It was known as IPython.
That was within the ’80s, proper?
Yeah. Effectively, I imply, Mathematica is an actual OG. IPython’s a bit bit newer. After which it was rebranded as Jupyter, I feel, in 2015, one thing like that. However anyway, the pocket book format is mainly you’ll have cells which have code historically. After which these cells present the output of that code. And people cells will be evaluated individually. That is totally different from a script. A script is one file. And the script is often evaluated, the entire thing, high to backside.
And this breaking it up into cells makes it actually nice for iterative and exploratory evaluation. So you possibly can say, “I simply wish to run this little chunk. And, oh. I wish to do the aggregation a bit bit totally different. I wish to do that.” And that is all an expression of a factor known as literate programming. I cannot go within the deep finish on this. However mainly, it’s this concept which you can see your logic after which the outputs in a single place. It’s a really, extremely popular format. I imply, tens of millions of individuals use notebooks. However we expect that it’s truly a format that much more individuals ought to be utilizing. We’re very glad to see that with our consumer base and clients.
Yeah and simply even at a better stage, a pocket book is a spot the place information scientists and information analysts work collectively. And it’s a mixture of code and rationalization. So it’s like a piece area.
That’s proper. And it’s actually the factor. Effectively, when you speak to lots of the information scientists, particularly, it’s the factor they use all day. It’s the factor the place they’re going and writing code. And so they’re iterating on one thing. Now, notebooks additionally historically have lots of points. There’s a well-known speak known as I Don’t Like Notebooks that this man Joel Grus gave at JupyterCon. It was very simply exhibiting up within the improper place to provide that speak. However he was proper. There’s all these points.
It was like 4 years in the past or one thing like this.
Yeah. It was 2018, I feel. Nevertheless it was all these points. A part of what we’re doing at Hex is, “Effectively, notebooks are nice. They’ve some points.” I feel there’s a camp of individuals which are like, “Due to these points, everybody ought to be doing one thing like writing scripts or no matter.” I feel we’re looking for that synthesis of, “Effectively, what if we simply repair these points with notebooks and made them superior and made them accessible to 100 instances the individuals? I feel this truly may go someplace.” And that, in a very simplistic method, is what we’ve been as much as on lots of issues.
A part of what you had been describing was one of many key points – simply to ensure I paraphrase and I make sure that I understood appropriately, one huge challenge of notebooks is which you can have totally different definitions of a variable in a pocket book.
Yeah. We name this a state challenge. So I’d escape some points with notebooks. I’d say the very first thing is accessibility. I used to be getting at this earlier, however most individuals working with information in most locations have by no means used a pocket book as a result of the 1st step is studying computer systems. You need to determine learn how to arrange a neighborhood Python surroundings and set up Jupyter. And most of the people usually are not going to try this. Factor two is state. That’s what you’re getting at. And the brief model of that is notebooks historically run in what’s known as a kernel. It’s mainly reminiscence area the place you run one thing like, “X equals 1.” Now in reminiscence, X equals 1.
However as a result of you possibly can run cells out of order, it’s truly you may get in these bizarre state points the place you possibly can’t truly know what state issues are in. You may have one cell that’s X equals 1. And one other cell is X equals 20. In the event you ran X equals 1 earlier than X equals 20, properly, now it’s one and vice versa. So it will get actually sophisticated. For many who aren’t acquainted, now we have an entire weblog submit about it. However the brief model is it is a ache within the ass for individuals who have been utilizing notebooks a very long time, like me.
Nevertheless it’s actually painful for people who find themselves new to it, who’re like, “What’s occurring?” You lose lots of people. And we consider this as one of many many issues which are in that low ground, excessive ceiling of, how will we make notebooks superior, higher for these energy customers? But additionally, how do you make it extra accessible and usable and welcoming for this greater inhabitants of folks that we expect deserve nice instruments?
There’s different points with notebooks we’re engaged on too. However that state challenge… we launched a characteristic final October. We known as it Hex 2.0. Nevertheless it was this reactive compute engine we had. And I’ll present it off in a minute. Nevertheless it’s successfully saying, “What if notebooks labored a bit bit extra like a spreadsheet the place cells have this sense of provenance between them?” While you replace one factor, it routinely updates downstream cells. And the state is in a significantly better state. State is in a greater state.
And that is nice. That is higher for these energy customers who’re like, “Man, that is the best way I all the time wished this labored.” And it’s nice for novice customers, who lots of them have by no means used a pocket book earlier than. They’re not even conscious there’s a state challenge. They simply know they don’t have that in Hex. So it’s all good. And that was the objective of that characteristic for us.
As only one final query on notebooks earlier than we soar into the demo – a part of the worth proposition as properly is that you are able to do information science within the Python world. However it’s also possible to do SQL and databases. And I feel you are able to do that in Jupyter as properly by putting in packages. Nevertheless it all comes out of the field. Or is that not appropriate?
Effectively, you possibly can. I imply, this was the factor after we began out after I was like… if you’re beginning an organization, you get an concept, you’re pitching individuals the thought. And it’s not unusual for individuals like, “Effectively, that’s already attainable.” I’d be like, “Effectively, what if it labored like this?” Individuals are like, “Effectively, Barry, that’s already attainable.” “Oh. Actually? Have I missed one thing?” “Effectively, when you set up these three packages and then you definitely’re prepared to, if in case you have the surroundings variables all arrange appropriately and then you definitely roll your personal reference to SQLAlchemy after which write your [inaudible], yeah, you would completely write SQL in notebooks.” That’s an terrible expertise. And never solely do I hate doing it as somebody who’s technically able to doing it however what about all these people who find themselves not going to struggle by all of that ache or don’t have the power to try this?
And I feel it’s the identical with the sharing factor. I used to be like, “Effectively, what if it was very easy to publish your pocket book in a method that anybody may use?” And it’s like, “Effectively, that’s attainable.” There’s these three open supply packages that when you set up them in your JupyterHub occasion and everybody’s utilizing the proper model of JupyterLab they usually’re all updated. And, oh. Effectively, these extensions are incompatible. However ignore that. And when you do that all proper after which Mercury is aligned with Jupiter the proper method, then you are able to do it. And by the best way, you’re going to want a full-time particular person to handle all of this.
That is the kind of shit that’s solely accessible to those actually technical customers and turns lots of people off from these workflows. And we don’t suppose it must be this fashion. So whether or not it’s the SQL stuff or reactivity or stunning no-code charts, we’re simply making it actually freaking straightforward to share your work with anybody. We predict that there’s a solution to make this extra accessible with out dumbing it down. Our energy customers love these things too. That’s the place, I feel, there’s this false dichotomy generally of, are you constructing for low-end customers or constructing for energy customers? We predict there’s lots of sensible individuals. We predict there’s lots of people that, given the proper instruments, will have interaction with these information workflows. And we’re all about constructing for that inhabitants.
Superior. Find it irresistible. All proper. Let’s soar into the demo.
[DEMO]
So switching tacks a bit bit, you guys appear to have performed a very nice job partnering with lots of firms within the ecosystem, together with lots of firms we’ve had at this occasion over time, together with, in your spherical not too long ago, I noticed that each Databricks and Snowflake invested within the firm. However earlier than that, you had bulletins with metric retailer firms and different firms, like dbt.
dbt is an enormous accomplice. Yeah.
Yeah. So is {that a} go-to-market technique? Is {that a} product? How do you concentrate on it?
Effectively, it’s each. I feel the partnerships with Snowflake and Databricks are very attention-grabbing in that… I didn’t speak about this earlier however we’re actually constructing a product to embrace what we consider because the cloud information period, which is you might have information that’s an enormous scale, saved in cloud information warehouses. And people cloud information warehouses usually are not simply there for storage. Databricks and Snowflake and different firms are additionally constructing very highly effective compute primitives whether or not it’s simply with the ability to push a question down totally different warehouse sizes and even with the ability to push Python code down. We predict they’re doing a terrific job with that. We predict that they’re going to proceed to do a terrific job with that. And we wish to accomplice actually shut with them on that.
So the partnership makes a ton of sense as a result of when persons are utilizing Hex, they’re going to be asking and answering questions on extra information. They’re going to be pushing extra workloads right down to these information warehouses, which is nice for them. And people information warehouses additionally present a very nice scale and information story for us. We truly must do much less on our finish to construct out an entire compute infrastructure and ecosystem ourselves in the event that they’re doing a terrific job of that. So we expect that partnership makes a ton of sense. We see our clients actually pulling us on, how are we integrating very intently with these applied sciences that they’re already investing in?
After which dbt? dbt is the concept you’re constructing some transformation within the pocket book?
You definitely may. There’s a pair attention-grabbing angles. We truly simply printed a weblog submit, considered one of our first analytics engineers, she makes use of dbt and Hex all day. She’s acquired a really cool workflow the place she’ll develop lots of stuff in Hex, carry it over to dbt. She’s printed a weblog submit on our website about how she makes use of them collectively, which could be very cool. However going a bit bit deeper than that, I feel, if you have a look at what dbt is doing or what firms like Rework are doing on the metrics layer, they’re actually virtually unbundling BI on this actually attention-grabbing method the place they’re saying, “Hey, it’s not nearly remodeling information as normalized tables in your warehouse. It’s about the way you’re then truly turning them into metrics and measures and semantics which are accessible to BI and analytics layer.” And so we’re very enthusiastic about what’s taking place there.
We predict it’s very a lot in its infancy. However because it matures, we expect there’s a very cool alternative to carry that extra in Hex the place you would have individuals as an alternative of getting to put in writing a ton of SQL in Excel, perhaps they’re capable of write one thing way more concise or perhaps one thing extra UI pushed, the place they will simply choose a metric they need, get an information body again after which begin working in opposition to that. So now we have a ton of shared enterprise with dbt in the present day. However I feel with the place they’re going and the place we’re going, there’s much more that we’re going to be doing collectively and others in that area.
Nice. So perhaps to shut, I’d like to spend two or three minutes on go-to-market gross sales. Who do you promote to? Who’s a terrific buyer? Perhaps, who’re some current clients? That facet of the enterprise.
Yeah. So we’re utilized by over… I feel the final depend was over 150 groups globally now, information groups paying us for Hex, which we’re extraordinarily happy with. We assist actually huge public firms, like Persion Prescription drugs, for example. They use Hex enterprise broad to assist their analysis efforts.
We’re additionally utilized by small startups. I feel the one constant factor throughout our buyer base and the place we’re actually resonating, they’re making investments in information infrastructure and information. And we simply talked about Snowflake and Databricks and dbt.
If firms are adopting these applied sciences, they’re usually then coming to Hex for, “Nice. I’ve acquired all this information now in my warehouse. I’m remodeling it with dbt. Now I truly need to have the ability to ask and reply questions of it. I would like to have the ability to do extra with it than I’m capable of in legacy instruments or Jupyter Notebooks or SQL Scratchpads.”
Hex is a brilliant good complement to firms which have invested in that stack. And what we’re seeing is any firm that’s hiring of us in roles like information science, analytics, analytics engineering actually wants and needs and will get a ton of worth out of Hex. So, yeah. From a buyer and goal perspective, that’s the place we’re at proper now.
Very cool. Congratulations on all of this and this journey. The corporate continues to be fairly younger. I imply, it looks as if you guys are executing extremely properly and really quick.
Yeah. We began in late, late 2019. So actually, not been at it too lengthy. I used to be very, very lucky to start out at an organization with two of us that I had labored with at Palantir, Caitlin and Glen. And we’ve had lots of enjoyable and a bit little bit of luck the final couple years constructing this out. So we’re seeking to proceed that streak for a bit bit longer and maintain occurring. We’re having time.
Very cool. Effectively, thanks a lot, Barry, for coming to this occasion, telling us your story. Better of luck for the longer term. I hope you come again in a few years.
Yeah. Paradoxically, I’m truly in New York this week, not that removed from you. However we’re nonetheless doing this digital. So perhaps subsequent time I come again, we’ll be capable of do it in particular person.
Do it in particular person. Sure. I can’t look ahead to that. Okay. Cool. Thanks a lot, Barry.
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