This episode features an interview with Matthew Scullion, Founder, and CEO of Matillion. Matthew has spent more than 20 years revolutionizing IT and software development, and just raised $100M in Series D funding. In this episode, Matthew dives deep into data transformation. He shares how Matillion is pushing the world of software forward, how their partnership with Snowflake is advancing the industry, his predictions for 2021 data trends, and much more.
This episode features an interview with Matthew Scullion, Founder, and CEO of Matillion. Matthew has spent more than 20 years revolutionizing IT and software development, and just raised $100M in Series D funding.
In this episode, Matthew dives deep into data transformation. He shares how Matillion is pushing the world of software forward, how their partnership with Snowflake is advancing the industry, his predictions for 2021 data trends, and much more.
--------
How you approach data will define what’s possible for your organization. Data engineers, data scientists, application developers, and a host of other data professionals who depend on the Snowflake Data Cloud continue to thrive thanks to a decade of technology breakthroughs. But that journey is only the beginning.
Attend Snowflake Summit 2023 in Las Vegas June 26-29 to learn how to access, build, and monetize data, tools, models, and applications in ways that were previously unimaginable. Enable seamless alignment and collaboration across these crucial functions in the Data Cloud to transform nearly every aspect of your organization.
Learn more and register at www.snowflake.com/summit
[00:00:00] Steve Hamm: Hey Matthew, it's good to talk to you again. The last time we spoke with God, it seems like ages ago is when we were doing the research for the rise of the data cloud, the book that Frank Slootman and I did together. So it's nice to hear your voice.
Matthew Scullion: I'll Steve likewise. And , uh, we were thrilled to be involved in the book and delighted to be speaking to you again today.
Steve Hamm: Yeah. Hey , uh, would you start by briefly describing your company and the services it provides?
Matthew Scullion: Of course. So miss Lillian's mission is to make the world's data useful. We're a software company. We produce a range of products called Matilda and ETL, and also Mytilene data. Loader. And specifically relevant to this conversation, of course, material ETL for snowflake, which is built specifically for the snowflake cloud data platform and customers use our products, our data middleware platform.
If you like , uh, to make that data useful, innovating with data at an accelerated rate.
[00:01:00] Steve Hamm: great. Now I know that your company has a very interesting history. You actually started in a different business and then pivoted into this space. Tell us how that happened and why.
Matthew Scullion: Yeah, thanks, Steve. And , uh, I suppose. Uh , uh, were , uh, still confident about this , uh, this story, because we've seen it with other great companies like Slack and, and others that started off doing one thing and ended up doing something else. Um, and psyllium was founded , uh, in Manchester, UK. All the way back in 2011.
So actually next week it will be our 10th birthday. Uh, we knew we wanted to be in the cloud. Um, we knew we were working with data , uh, but the original business did something different. Uh, what we do today, we were actually in the business of delivering turnkey, fully managed software as a service business intelligence solutions to typically midsize, mostly British.
Companies people that needed that's [00:02:00] management information and data, but they didn't have the time or it fire power to deliver it themselves. Now, the way we used to deliver that solution was build them a modern data stack. We were perhaps slightly ahead of our time. Uh, we were doing. Uh , uh, data analytics, um , uh, in the cloud before products like snowflake or its contemporaries were actually out there in the markets.
And we built the solution with a number of , uh, high quality commercial off the shelf technologies, as well as some IP that we developed ourselves. That was the only one that's built out an analytics , uh, solution. And so on we'll know, a big part of the work that you do is first of all, getting the data into one place and then making that data useful, joining it together, embellishing it with metrics.
And typically the category of technology that you use to do that is called ETL. We were [00:03:00] using our commercial off the shelf ETL tool from a major vendor , uh, which we liked, but the fact that it wasn't built for the cloud. Was slowing us down and this original business. Uh, and so as it turned out, this was a young market and we were using the technology very aggressively.
So there weren't too many teams in the world doing that. At that time we had this problem. We tried to find an ETL product that was as rich and deep as we need it. And that we were already using from this commercial off the shelf products, but that was built for the cloud. When you know it, we couldn't find a product like that.
So bearing in mind, we knew quite a lot about this. Problem that we were trying to solve. We thought, Hey, let's have a go , uh, building a solution ourselves. And the company was founded originally 2011. This is about 2014 that we're doing this. And we spent a year or so building a, an early products, ostensibly for our own use.
We did code name, that [00:04:00] technology, we call it Emerald and our company color is green. And , uh, and it turned out. It really was , um, it works really well. And, uh , uh, so well, in fact that we thought perhaps there's more value in this product than there is in the original business that we set up.
So in late 2015, we launched what we, by then we're calling Matilda and ETL for sale in its own. Right. We've subsequently sunset the previous business. And grown, um , uh, a real nice business that we find today having been informed by this original pain of needing a proper grown-up ETL product, but one that was built natively for the cloud.
So, yeah. I love telling that story, Steve, and thanks for asking the question.
Steve Hamm: No, that's a very cool story that the company that's able to be nimble and flexible enough to pivot like that. Uh, that's a really good sign of a good culture. So , uh, congratulate you on it. Yeah. So when and why did and snowflake become partners?
[00:05:00] Matthew Scullion: Yeah. So we came partners , um, in August of 2017, but we'd been talking together for a while before that. So , um, we mats the snowflake team , uh, at the industry conferences as you'd imagine. And. We already had a belt for cloud ETL products out there. I'm on a different platform that was getting a lot of traction.
And I think was starting to become an seen as a preeminent products in the post cloud data middleware world. And of course , Uh, for snowflake, um , uh, that was , um, nothing like the business. It is today, but we're still a business with a lots of excitements around it and certainly a technology with a lots of excitements and respects.
And so this was a conversation we were excited to have with the snowflake team. Um, and we were really grateful that they kind of wandered over to our booth and events and, and started a [00:06:00] conversation. Uh, of course, a lot of times that would very quickly lead to a partnership. Um, but there was really two other things.
That's happened over the following months , uh, that led to us launching , um, it's silly an ETL for snowflake in August, 2017. The first one is our technology is built specifically for the snowflake piled data platform. That's important because it gives the customer, the user, the resonance, as they use our technology with snowflake, they don't want to buy snowflake.
And then B. Uh, hampered or obstacles from all the underlying , uh, capability, features and functions of the snowflake cloud data platform that they want our stuff surfacing and to be able to use as resonantly as possible and to achieve that we build a specific product. Um, and that's what we did to support this snowflake partnership.
That takes a few months and on a chunk of people. And we were starting to work closely with the [00:07:00] snowflake team as we've done subsequently actually to , uh, to support shipping something , uh, that was highly functional straight out of the box. Uh, and I think the other thing that we discovered, which I guess is the long-term secret behind the partnership is that as we got to know the snowflake team and hopefully as they got to know us, We discovered we not only had a shared focus on the same technical space, but also very importantly to, as a material.
And I know it is a snowflake as well, a shared culture and a shared set of values. And so we very quickly came to realize that this was a partnership that certainly we wanted to lean into energetically. And we've been privileged to be snowflake partners ever since.
Steve Hamm: No, that's great. Now, could you explain how the technologies work together?
Matthew Scullion: Yeah, absolutely. So, I mean, starting off, I'd say , uh, at a technology points of view , uh, material ETL for snowflake , uh, you already know, [00:08:00] built for the cloud. Um, built specifically for snowflake , um, is aimed at the enterprise and it's , uh, Uh , uh, 100% pure ELT architecture that works hand in hand with the underlying snowflake cloud data platform to allow customers to make that data useful.
Outscale and that's an accelerated rates. So Mytilene provides a portfolio of technologies to allow customers to get that data into snowflake. And then importantly, once it's in snowflake to innovate with that data on the snowflake platform and in turn snowflake provides, uh , the, the host power performance and underlying capability.
To support that a hundred percent ELT architecture. And so it's the products working hand in hand that really delivers the finished solution to our customers. What that maps out to in terms of how our companies work together? Well, I mentioned [00:09:00] already that there is what we feel a very healthy relationship between Medallian and snowflake.
We worked together on our products and our products teams are. Um , uh, deeply integrated and, and , uh, um , um, worked together. Resonantly that allows for our part, for our products to keep up with snowflake and always be supporting and surfacing the latest snowflake technology and the cloud data platform. Um, and of course we work hand in hand from a go-to market points of view as well.
Uh, for . That's mostly working with the snowflake and surprise and majors. Segments has, that's where our technology is mostly focused. Uh, overall we have , um, just by way of example, Steve, about 550 mutual customers, I think, and industries like, uh , uh, media and manufacturing and retail and technology also healthcare.
Financial services. This is customers like Koch [00:10:00] industries, Slack , uh, Siemens, Sony, Western union, DocuSign, or Novartis. So as you're going to hear, you know , um, larger companies using snowflake and using materials to make that data useful on snowflake , um, at an accelerated rate,
Steve Hamm: yeah, that sounds like a great roster of, um , of, of mutual customers. But could you back up just a second, I really want to understand the transformation of data, kind of where that happens and what the, what actually is, is being transformed to why is it making it more useful?
Matthew Scullion: Yeah. Okay. So let's talk about the problem domain and then how, how we solve it, if that works. Um, so. Uh, in order to make data useful in an organization and by making it useful, I mean, make it ready to be consumed in a useful way in a data analytics, business intelligence , um, AI or machine learning use case.
Um, you've got to do a couple of things. Uh, first of all, you've got to get the data in to snowflake. Um , uh, if it starts outside in source [00:11:00] systems, files, APIs, databases, W we can't do Coolum fun and value added stuff within snowflake. So you've got to get the data into snowflake and that's important. Um , uh, but it's not the hardest or, or, uh , uh, you know, perhaps most leveraged part of the job.
And there's also quite a range of ways in which you can do that. But getting the data into snowflake is only parts of the job. And, you know, it was probably the easier part of the job.
If you just think about it saying, Hey, I want to connect database. Hey, it's my snowflake cloud data platform important. It's got to work. It's got to perform. It's got to be secure, not super duper complicated. Where you really make data useful is what you do with it once it's on snowflake. And, uh , and, and if you imagine, for instance, you've got data coming in from two or three different systems and you want to join that data together.
So it tells a cohesive, useful business story. You don't want to consume and whatever the downstream analytics application is, let's say it's just one system. So , uh, one system, but with lots of data [00:12:00] entities, maybe there's customers and products and transactions. And then analytics, you tend to need to join those things together.
You also want to embellish metrics onto , uh, the data. Usually you need to sort out the quality of the data, getting rid of the stuff that you don't want, making sure that the staff that you do one is correct. And then, you know, there's versions of this as well. Like. Um, aggregation and granularity. So for instance, you have a lot more order lines than you do orders.
You have a lot more orders than you do customers. You often need to flatten that stuff out. And this is the work of the data professional, right? This is what , uh, ETL guys and, uh , uh, data engineers and all that aperture of user personas. This is what they do all day. They moved the data. Yeah. But then they transform it into something that's useful and ready for analytics. So you can do that in a number of different ways. [00:13:00] You can do it in code. Um, and like most computing you can do it in code. That's got some advantages, you've got very fine grain control. You also don't need to buy a product like mine, of course, which you may see as an advantage on the downside. Um, more work, so a little bit slower, tougher to maintain and requires expertise.
So that tends to make sense if you're the sort of company that's got a lot of engineers hanging around. Um , uh, but it's also makes sense if it's comparatively simple transformation , uh, that you're not going to have to make lots of changes to in an enterprise. Transformations tend to be pretty complicated and involved.
You've got lots of systems each, which you've got lots of entities in. It has to be right, because you've probably counting millions or billions of dollars of revenue or customer behavior. And so it needs to be Ryan audit-able maintainable , um, secure of course, but also wouldn't it be great if you could get.
More people working on innovating with data and making it useful than just high end engineers, that code. [00:14:00] And so really that's where , um, product Simon Caelian and in the post cloud world really only Mytilene focuses by making the ability to load data. Yes, definitely. To, um , uh, to orchestrate that data pipeline really sophisticated functionality there that's buckle proven in lots of large enterprises, but also to innovate with that data by joining it together, embellishing it, aggregating it, sorting out the quality and do that in a way that's super high performance because it's pure ELT and it's just leveraging the underlying snowflake cloud data platform.
Um, but also in a way that's visual. Low code, no code code optional. So I brought up aperture of user personas can be involved with making data useful, which in turn that's down. So the company innovating with data more quickly and being able to support it more easily.
Steve Hamm: Now Matthew Medallian and stuff like have hundreds of mutual customers. You've described some of the relationships and some of the, some of the customers have a few moments ago and a bunch of them are [00:15:00] really big organizations. Can you describe some of the situations where the organizations are getting a lot of value out of using the technologies from these two companies together?
Matthew Scullion: Yeah, absolutely. Um, so in terms of the value that customers get, and I guess also why they work with snowflake and Medallian , um, in the first place, you know, the value they're hoping to get, first of all, if I maybe start with use cases, Um , uh, so oftentimes it's , uh, companies doing that new , uh, innovation with data in order to allow them to compete, um , uh, understanding the customer's products, processes, uh , uh, using data in a way that they couldn't do.
Uh, 10 years ago, there's a worldwide competitive race , uh, to do this, I would argue , um, facilitated by the cloud. Um, and by snowflake, cause it gives customers this , uh, limitlessly scalable [00:16:00] cost effective, flexible and secure platform , uh, to, with data in incredible volume. Yeah. Gain insight from it. So that's one of the vectors that we see , uh, our customers , uh, jointly come into is on that the kind of asking and answering.
New business questions with data. And certainly that's been the case now for a number of years. I think the area that's changed in this respect. Uh, over the past , uh, 12 months. So some extent accelerated in our experience, at least through the pandemic , uh, has also been the addition of that to migration and modernization projects , um, as , uh, larger businesses and surprises and large cap organizations.
Uh, now we're really accelerating as they modernize , uh, their legacy, it infrastructure to the cloud. Uh, perhaps moving from on premises , uh, appliances , um, moving the data piece , uh, into snowflake , uh, where they have [00:17:00] more power, more functionality, more flexibility, and it's more cost effective. Um, all of which companies want to be particularly in the post pandemic world and then for our parts on that.
Well, if you're moving from , uh, an on-prem. Uh, data analytics start data warehouse appliance to a modern cloud data platform like snowflake. Then you also want to modernize your legacy ETL and beta middleware layer to our enterprise class, but built for the cloud ETL or data middleware layer. And that's where we come in and the sorts of customer opportunities that snowflake and ourselves work on.
Um, now once that's up and running and, and , uh, that tripartite relationship of snowflake battalion and the customer. Or oftentimes the consulting partner or GSI as well are working on delivering on that goal. Uh, then what we find is that the tight integration between , um, our two [00:18:00] technologies and companies , uh, really helps in two ways , uh, first is speed.
Um, time to market, how quickly customers can get POC is. Projects and then incremental innovations on those projects done. And we see that in the way that the accounts grow, but also just in the raw results. So, I mean, to give you an example at Cisco, um , uh, that the team there experienced a 84% reduction in the spend associated with ETL, um , uh, when they moved to , uh, Mytilene ETL for snowflake and snowflake based solution compared to the , uh, legacy solution , um, that was looking at cost.
If you look at performance at DocuSign, another joint customer of ours, those guys reduced the time needed to process. Uh, huge volumes of data and some long running jobs. They're hard from 22 hours , uh, to just a handful of hours. And so there's a long list of areas where that tight integration, [00:19:00] the underlying power of the snowflake cloud platform and the focus on making data useful inside of the mud psyllium products , uh, delivers outcomes for the customer quicker.
Uh, does that make sense?
Steve Hamm: although those are great examples and great scenarios. I think people really get a lot of value out of listening to those things. Um, you know, we we've talked quite a bit about battalions technology, but I wanted to, just to zero in what's the most powerful aspect of the technology in your view.
Matthew Scullion: Yeah. So , uh, it's a great question, Steve, what's the most powerful aspects of METAlliance technology. I think it starts at the top and kind of alluded to this earlier in the call. We're focused on helping and surprises, make that data useful. Um, and as we discussed , uh, earlier on in the podcast , Um, so do that.
They have to get data on snowflake. Um, but then they've got to do stuff with it. They've got to join it together and embellish it. [00:20:00] And Chuck is Ryan sorts out the quality and put metrics on it and aggregates it. I might say innovation and the steps that's the data needs to go through for me, it's kind of.
Raw material state. Like I ignore our through to becoming shiny stainless steel, ready to use in the project. Those stabs are business knowledge that needs encoding into , uh, the process of transforming the data. Just like all business knowledge. I was tied up in people's heads, uh , uh, team members, employees of our customers.
And so I would say the most powerful aspects of materials technology is , uh, the speed and fidelity with which Matilda and ETL allows people to express that business knowledge. To transform data , um, connecting what's in their brains with the steps that they need to go through. [00:21:00] Uh , to, to turn iron ore in sustainable steel.
Um, now all the products centered around that concept, you know, the visual transformation layer, the , uh, the deep orchestration , uh, it even has , uh, things like collaboration built into a little bit like Google docs, because anything that's a people game is also a team game. But yeah, for me, That's the , um, that's the most powerful aspects of materials technology, the way that we can get , uh, more people in pass rush and get each of them more productive , um, as they go on that journey of making that data useful.
Steve Hamm: Yeah, battalion was one of the pioneers of doing ETL in the cloud. But of course now it's kind of a land rush. I I mean, the cloud is, is the biggest opportunity or one of the biggest opportunities in the technology sphere. And a lot of companies are either retooling technology for the cloud or they're.
They're reinventing it for the cloud. So how do you stay in front of your competitors? You know, in this kind of intense, competitive [00:22:00] environment?
Matthew Scullion: Yeah, it's hard to get to glum about all that competition, because it's just a symptom of what an amazing markets, uh , uh, I, and , uh, the rest of the team green are lucky enough to live through. Of course. Um, in part created and fueled by , uh, our friends at snowflake. Um, I think the intersection of cloud and data , uh, it's a once in a generation , uh, set of market dynamics and that, you know, a byproducts of that is this lots of competition.
Uh, and so I think there's two parts to that. Hey Steve, if I may, one is , uh, you know, how do we differentiate? And secondly, how do we maintain it? The , the, the second answer is easier. Uh, than the first, but I'll try and punch out both quickly. Hey, and in terms of competitive differentiation, I think is pretty simple material really is the only cloud native built for the cloud solution.
That's sophisticated and deep enough and optimized for the unsurprised for larger businesses. Um, [00:23:00] In the pre-cloud world, we've got forefathers data, middleware and ETL with deep, sophisticated platforms that enterprises are used to using, but they're not built for the cloud. Um, and that slows customers down , um, technically , uh, and also commercially and the way that they buy and consume software in the post-grad world.
You're right. There's a land grab of solutions out there and also emerge companies. Um , uh, really good companies, but typically focusing on one part of the data landscape, for instance, data movement, or , uh, some other , uh, part of the set of requirements, but there really isn't another. A product that's enterprise class and deep enough to service the needs of an enterprise user.
So you've kind of got a Hobson's choice there. Right? You can have cloud native, but simple, or you can have enterprise class and deep. But pre-cloud and therefore legacy, which slows you down hard to buy, et cetera. [00:24:00] So Italians, the only company that , uh, has built technology specifically optimized for the unsurprised there's deep and sophisticated, but cloud native and easy to consume.
So the other part of your question, how do we maintain it? Um , uh, we , uh, keep going fasty , uh, we have a large development team , um, that we will be nearly doubling in size again , uh, this year, which I'm excited about. Uh, and we are lucky to be informed , um, by , uh, thousands of users across some of the most prestigious companies in the world and work closely.
Uh, of course with our friends at snowflake to ensure that our product stays in sync with them. So, yeah, it's a race and we keep investing heavily in the technology to ensure that we stay competitive within it.
Steve Hamm: Yeah, it's interesting. You know, this , this, this , uh, data cloud thing is really kind of a decade old, but it seems like it's still at the very beginning of its potential impact. Right?
Matthew Scullion: Yeah, I couldn't agree more. Um, you have to be aware of it. I think in , um, in our jobs , uh, perhaps Steve, because it would be so easy to [00:25:00] think that , uh, the whole world is. On the cloud. Uh, but they're definitely not. And the majority of workloads, um , uh, are still running on prime or at least away from public cloud and some of the industries where.
Uh, I'm Italian and snowflake have been focusing recently places like , uh, healthcare and financial services. Um, you know, we're really only just starting to see the acceleration there all is. This is experience. Um , uh, I think , uh, as I alluded to earlier, the. The the global pandemic, um , uh, has perhaps helped accelerate progress here, but there's still so much to do.
And we certainly feel , uh, positioned really well alongside snowflakes to help with arts, I guess the nature of where markets go in the early days. Is the non-mission critical solutions, the net new innovations with data that are by [00:26:00] definition, therefore, a little more simple. And then as large organizations start to move serious workloads in a purposeful way.
Want to do it as fast as possible, but need to do it with that sophistication and gravitas that an enterprise requires, then it requires grown-up technologies to do that. Like the snowflake cloud data platform and like Mytilene ETL.
Steve Hamm: Yeah. Yeah. You know, we're talking in early 20, 21 and, you know, looking back at 2020, what an incredible year, I mean, just, you know, the pandemic, the economic turmoil, the political turmoil. It seems like there was one thing after another environmental turmoil, you know, forest burning. But I think there's a sense now looking ahead into 2020, have some stability, you know, politically, maybe economically this kind of thing.
Hope I'm being hopeful here. So if you could look ahead, what are some of the big trends and new capabilities that you see coming this year?
[00:27:00] Matthew Scullion: Yeah. So 2020, it was like dog years. Right? I think I. I live seven years through the , uh, the four quarters of that year. Uh, and you're right. I, I entirely share your optimism , uh, for 2021 and beyond , uh, as a member of the human race, as well as a business person and an a, an, uh , uh, an actor in this market of, uh , Uh, data in the cloud.
Um, so I agree with you. So , and, and we're super excited. Um, and , uh, you know, in the short term and perhaps job number one is , uh, we've been extremely lucky insofar as the, our business has still grown , uh, quickly and strongly despite , um, the global challenges , uh, and off the back of that, we are purposely continuing to grow our business.
And in fact, Accelerating the rate at which we do that , uh, both on the , um, customer facing side, I'll go to market teams like , uh, sales and marketing and customer success and support. Uh, but also , uh, as I mentioned earlier in the podcast , uh, continuing to invest ahead of the curve on [00:28:00] engineering. So I guess that's the short term thing that's to some extent never stops, but actually where.
Um , uh, putting the hammer down on that , um, at the moment, which we're very lucky to be able to do , uh, more strategically there's some trends that we're really excited about. Um, and some technology , um, that we're working on that we're really excited about as well. I think one of the trends, the were pretty pumped up about is seeing what's happening in the data middleware space around reusability and repeatable processes.
And , uh, I think , uh, the snowflake team , um, have a similar thesis here. Uh, in many ways, um , uh, what's great about technology, uh , uh, like cars and like snowflake is that of course yeah. Allows , um, for , uh, democratization to happen. I'm aware that that phrase is getting a little hackneyed now, but , uh, for democratization up and within an organization to gain access to data.
But I think was starting to happen now, first of all, [00:29:00] With a deep and sophisticated yet easy and productive. Because the low code, no code or code optional tools like ours. That mean tech savvy business analysts, ETL developers. DBA is, um , uh, a BI developers and analysts and architects. People , uh, of those user personas and similar ones can do sophisticated scaled innovation with data, even though they don't either once or have the sorts of high-end code based data engineering skills.
That means organizations can go faster. Of course, I mentioned earlier on that really what people are doing as they do that stuff is encoding business knowledge in their head, into the way that they transform data. And those people have a higher probability of being closer to the business problem domain as well.
And so the thing we're excited about as a [00:30:00] next generation on that is saying , well, okay, Someone solved a business or technical problem once maybe that could be parameterized and wrapped up and sort of shrink wraps and then shared inside of that organization or beyond the organizational boundary with the ecosystem.
And by way of example, a material has just taken a step here to juice our own customer ecosystem with something called the material exchange program where customers can create. Uh, business or technical solutions inside Mazzilli and ETL , uh, inside the transformation and orchestration canvas of our products, but then wrap it up and give it an icon and give that a nice description and then put it onto a marketplace where other people can then download and use that over time.
We expect. Um, I and specialist consulting partners , uh, to develop more and more of those solutions as well. So we're really excited about that concept of pre-canned recipes being shared across the [00:31:00] ecosystem. Cause that just further accelerates , uh, an enterprise's ability to make data useful. Outpace , uh, sorry.
Steve Hamm: Let me just interject here for a second. I'm curious, are you involved with snowflakes exchange and marketplace technologies and platforms?
Matthew Scullion: Yeah. So these are very complimentary technologies, um , um, through Snowflake's data marketplace, of course, customers can now , uh, for the first time in history, really with a matter of a few clicks , uh, connect themselves with data that they can use to inform and make richer. Their analytics, AI and ML, um , uh, projects and use cases.
And so we're really transfer , um, transformative technology that snowflake have brought to the market. And , uh, of course, a lots of momentum behind that. And so Mitsui medallion on the material exchange and the shared jobs framework. Yeah. We see as a natural partner, just like snowflake and Matilda and as businesses, I suppose, a natural partner [00:32:00] technology , uh, to that and Sofar as well as the snowflake data marketplaces , uh, making data available across the organizational boundary , um, cotillion on the materials exchange is making the logic to make that data useful available across the organizational boundary.
Does that answer the question, Steve?
Steve Hamm: Good really nicely done. And, but I interrupted you. Do you remember where you were going?
Matthew Scullion: I do.
Steve Hamm: Okay, very good. Very good. Go
Matthew Scullion: talking about my favorite subject. So second favorite after my case, I suppose, but, um , Um, yeah. So the other thing that we're really excited about , um, we're excited about it because we think and hope that our customers will share this excitement under that deliver a level of finality that might be satisfactory to the market.
But, uh , uh, Hey, we're at data middleware company, I explained , uh, in answer to your well-educated question, Steve, earlier on that that's about moving the data. Yeah. And it's about transforming the data and encoding what's people's [00:33:00] heads to make it useful. And in that data movement bets, there is the perennial question done.
My data middleware provider does my ETL or data pipeline provider. Half of the connector that I need on every organization has dozens or maybe hundreds of systems, some proprietary and bespoke. Some class-leading an industry standard. It's really rare that the products or a SAS based service that you buy has exactly the right connectors that you need for all of your use cases.
So this is a huge frustration for customers because they just want their connectors and actually. Quite a big challenge and frustration for the ISV as well. Uh, the material in this case, because , uh, we would need to develop an infinite number of connectors in order to match up to every customer's requirements.
Of course, there's a huge long tail of , uh, how many customers [00:34:00] use each of those connectors, which makes it harder for us to maintain them and make it economic. It's just a lose, lose the customer. Doesn't go away once the ISV isn't able to service the customers need, um , lose, lose. And so the thing I'm super excited about is that we're perhaps about to see the end of this connect or Wars in the ETL ELT space.
Um, and answer that question. Do you have a connector for this? Always and with certainty with the word. Yes. Um, and that's because of some functionality we were really proud of and , uh, super excited to launch. Um, and the back half of last year , uh, which is called create your own connector , um, or universal connector.
And it's the ability to create a load connector and ability to load data from a source from any API end points. Without writing any code. So you don't need to be technical. You don't need to be an engineer. You don't need to run a [00:35:00] project. You point Matilda and ETL at your end point API, you run through the graphical wizard.
It figures out the API and the paging and the security and all that other boring stuff. And it just turns it into a beautiful form of hastily organized data. And then puts it into snowflake for you. And so the answer from now on , uh, to the question, do you have a connector for this will always be yes.
And that's another thing that , uh, as you can maybe tell from the delights in my voice, Steve, that we're pretty excited about.
Steve Hamm: Well , I, I can absolutely tell. And when will this new capability come out?
Matthew Scullion: Yeah. So actually it is that already it's Matilda and ETL for snowflake now. Uh, so you can , uh, launch a free trial if you're not a customer today on either the , um, AWS or Azure marketplaces and , uh, depending on which flavor of snowflake years and try it out, you know, it's going to take you five minutes to get the product set up.
There's a nice little YouTube video. Um, shows you how to do it super easy, even though it's so powerful. If you're an existing [00:36:00] customer, just hit us up on support and we'll show you how to use it.
Steve Hamm: I dunno, that sounds great. Hey, you've done a very good job of explaining kind of what's here and now and coming up in in the coming months. But I want to ask you to put on your visionary cap for a minute, and I know you have one. And look out for five years or so. What are the big changes that are coming in cloud computing and cloud data that will have outsized impacts on organizations, but also on society.
Matthew Scullion: Ah, it's a great question. Isn't it? And it's also a great risk for any CEO. Uh, going on record on a podcast, making predictions in five years time, because a smart young man like yourself, Steve , uh, well check why set in five years and see if that, if it was right, it is , um, the wondrous opportunity, I think , um, the first and foremost, and to me, this actually doesn't change materially over the next five years is the migration because that's not.
[00:37:00] A one year job or a three-year job, or even perhaps a five-year job for the world to my greats. Um , uh, what, 30, 40 years of , uh, legacy data analytics, which has served businesses great, but which is shackled and expensive and ties up organizational resources. My greater ends to their cloud. We hope my greater on snowflake and material and unlock than.
Uh, the extra capability that it gives an organization in terms of its ability to innovate with data. So I am excited to see that, you know, the pioneers , uh, of London in the new world. A few more people have turned up, but the rest of humanity is still waiting to cross the ocean. Um, so silently my view is what you tend to see with markets like this is, um , uh, I guess an increasing seriousness and professionalism as , uh, the main stream.
Uh, business becomes about doing [00:38:00] business with data. I think it moves more to the heart of organizations, I think processes and software matures around that. And so again, we've been in the sort of early wild West days, um , uh, with lots of innovation, lots of code, lots of collections of products, but our view on the market.
Is , uh, as , um, large enterprise begins to accelerate in the way that it migrates to the cloud. Uh, you'll see a maturation , uh, um , Uh, in, in , uh, unreal with the gravity , um, uh , uh, and utility with which large car pan surprises, CNR for treat this area of technology. And then in terms of societal impacts , um, you know, a really interesting trend.
Uh, that we spend a chunk of time thinking about , uh, is AI and ML, which is of course not new. And I'm sure I won't be the only person you speak to this year that matches. And so this is a trend , uh, for me, there's a couple of reasons why , uh, we love to open on this topic. I'm one of which is [00:39:00] selfish. Um, AI and ML projects require.
Well organized , um, clean data, which is often the barrier too, and the largest piece of work in the AI and ML projects. And so if you're in the business of turning iron or in stainless steel or turning , uh, siloed scruffy , uh, raw materials, data into useful AI and ML. Data and allowing teams to do that and accelerated rate.
And this is a great megatrend for you, and that's certainly the case with medallion, but societaly getting around swans from your question there. I think we will always read in the popular press about people's fear of AI and ML. Robotics and other automation technologies taking humans out of the loop and mass unemployment and all that sort of stuff, which I think if you look back through history is not what happens as the world becomes more efficient and doesn't have to do things that are later considered menial, but previously we had to be skilled at doing [00:40:00] then that allows humans as it were and businesses to focus on higher order problems.
Um , uh, and to use the things only they can do and alarm machines do for them. And I think that has potentially really beneficial , uh, societal , uh, effects looking, you know, five to 10 years out. So those would be the things I pick up.
Steve Hamm: No. No, that's great. Hey, where are you? Excuse me. Where are you talking from today?
Matthew Scullion: I'm talking from my home. Like many of us were , uh, in mid January, 2021. And so I know many of us are , um, in , uh, some degree of shelter in place or a lockdown. Um, I live at, uh , uh, in Manchester, UK. Uh, in more normal times, I split my time between the us and the UK, where is our, um , uh, our last year, 2020, of course , uh, most of us couldn't travel.
Um, and so, yeah. So I'm speaking to you from my , uh, my little study at home I'm in South [00:41:00] Manchester, UK, such to the wonders of modern technology.
Steve Hamm: No, that's , that's, that's great to hear. Cause I know, I mean, normally you're , you're, you're one of those guys you're split between Denver and Manchester and of course doing a lot of other traveling in a regular year and maybe you'll get back to that, but you know, it's a, it's a busy life being a CEO and you're spread thin.
You mentioned you're back at home and I know you have a couple of daughters and I know that you like to ride horses with them. So if you could talk a little bit about just, you know, how do you balance, you know, in your, nor in a normal year, how do you achieve work-life balance and how do you get that quality time with your kids and, and a little bit about the horses too.
Matthew Scullion: Oh, thanks for that question, Steve. That's lovely to be able to talk about it. So yeah. How do you get balance , uh, when Helen's relief against a busy job? Uh, first of all, if I may say, Hey, a CEO is a busy. Job and lots of other jobs are super busy as well. I always think of it as a complete privilege to be a CEO of a company like Matilda.
And I [00:42:00] get to work with a team of several hundred, really bright, smart, aligned people that are working towards the same shared goal. And that's a. Massive privilege. So it's never a chore, the being in the work and, and that is the case outside the walls of Italian as well. Uh, again, a complete privilege to get to work with the team at snowflake and our hundreds of joint customers.
Uh, you right though , uh, you do have to work or work-life balance , uh, someone. Once gave me a brilliant piece of advice, which was , uh, we all have three priorities in life, work, family and yourself, and you need to look after all three, um , uh, because if one of them falters, it affects the other two. Uh, so I always tried to be purposeful around it when I'm traveling, I'll do , uh, FaceTimes with the kids at bedtime and read them stories and things like that, which is nice.
But you're right when we're at home. One thing we enjoy , uh, is getting out on the horses. Um, I should say I've actually not been out on a horse for a couple of others because I rather dramatically fell [00:43:00] off going over it earlier in the , uh, but I'll be back on soon. In fact, I was out riding last week , uh, and it's something I've done since I was a kid.
I paused , um, kind of getting distracted by my twenties and thirties. Um , uh, but uh, in, in that spirit of , um, looking after work family and myself, I thought, gosh, I need something physical that I enjoy. And that's healthy. Got back into horseback, riding off the back of that. And then to my complete delight, my two little girls who are currently , uh, nine and five , uh, then are riding ponies.
Uh, they look great doing it as well because their other big hobby is ballet. And so that posture is awesome and far better than mine. And I'm only slightly jealous of it. But yeah. Thanks for asking.
Steve Hamm: Yeah, I can envision it. That sounds like a lot of fun. Hey Matthew, I just want to thank you so much for joining us today. I really found our talk to be fascinating, especially when you spoke about, you know, the importance of technology being built for the cloud, and also the idea that, that your [00:44:00] technology one, you know, one version of it was built specifically for snowflake and how important that is.
And. And , uh, also when you talked about universe, those universal connectors that you're able to package up for customers, or they're able to package up for themselves. I think that's , um, another great , uh, thing to look forward to. A lot of people are going to be interested in that. So I want to thank you again for your time today.
It's it's just great talking to you.
Matthew Scullion: Steve it's um , uh, Our pleasure and privilege. Great to talk to you again, really appreciate the opportunity to, uh , uh, to share some more stories with you on the podcast. I look forward to next time.