How Low-Code Empowers Collaboration with Michael Berthold, CEO and Co-Founder of KNIME

How would you describe right now the state of low code data science and how do data teams leverage it today?

So low code data science, I mean, it is used in different ways, and I think we'll dive into that a little bit later as well. But fundamentally the idea is to get data science out of the hands of programmers, or at least make it easier so that people don't have to write lines of code for each and every little thing they want to do with their data.

Okay, great. And maybe how do you see it now, the current state, how widely applied is it, how widely adopted is it as a tool within any modern organization today?

I would claim that a lot of people are using some bit and piece of low code data science for some aspects of what they're doing their daily work. But fundamentally it's really about is the low code aspect really only kind of like a wizard or an easier way to create code, or is it really a tool in itself? And I think that makes the big difference. And I think that's also where KNIME differentiates itself from quite a number of other low code tools.

Over the past few years, a lot of the data science workflow has shifted. We've seen a much bigger emphasis, for example, from going from experimentation more to deployment. Given that you're building tooling that really focuses on the data science workflow, I'd love to see from your perspective, how has the data science workflow shifted over the past few years? And where do you see local data science fitting into that new evolving workflow?

Yeah, that's a very good point. Right when we started doing that, data science was essentially done by small teams in the corner of an organization. There was like the geeks. They were sitting in the little cubicles, nobody talked to them, problems were thrown over the wall. They addressed them, came back with answers. The answers were usually the wrong ones. Nobody really cared about the answers because they were actually interested in different questions. And what really happened behind the scenes didn't really matter. So people were sitting there and writing different types of code. There was space and R&SQL or whatever was sitting out there. And low code really has allowed people to start working with data, making sense of data without having to go through all of these technological complexities. So I think that's really something that. I mean, there's this democratizing of data science that everybody keeps talking about. And I think local tools are fundamental for that one. Because there's this nice quote of someone who said, I don't want to teach my students half a year of programming before they can actually start working with data, I want to make sure they get started doing interesting stuff from the get go. And the other piece I think is that there's a bit of this field of spreadsheet kind of wrangling, data wrangling, whatever you call this, and this idea of data science where it's really about machine learning and hardcore statistics, those types of activities have merged a lot more so that you now want to get the people that are actually used to working with data and making sense of data, working with the spreadsheets and doing really sophisticated things in Excel or some other spreadsheet. You also enable them to gradually improve their skillset around the machine learning aspects of it. How can I build a predictive model those types of things? And I think that's where low code allows people to get in touch with different types of technologies a lot easier.

You mentioned something here on the democratization, that's something that we really think about as well. How to make data science accessible to the masses within the organization. I think if you're able to unlock that, you're able to unlock a lot of the value of data science. How do you see this democratization playing out now with low code data science tools and how has it changed maybe the dynamics and priorities within the data function? You mentioned that merging aspect, right? So maybe expand on that a bit more.

So what we see is that KNIME is often now also being used as part of the upskilling initiatives in organizations. So it's really this idea of everybody who wants to make sense of data. I mean, they need to be willing to invest a little bit of an effort, but at the end of the day, you're doing complicated things, but has a tool at the right level of complexity that allows them to get started. Right. And in the sense that's kind of, you're used to writing macros in Excel, but you have a really hard time automating that one. So low code data science allows you to easier automate those types of things. On the other side, you have people that come from the machine learning field, they have a really hard time merging data sets, doing all of the data wrangling because they may not be SQL experts, but they can now use a low code tool to do that part of their activities as well. That kind of brings all of these different groups together and allows them to collaborate within the same environment. I think that's also a huge strength of low code that you kind of, it's almost like you get the SQL expert and the R R expert and the Python expert and the JavaScript expert, they can collaborate within the same environment. That's one of the powers of low code as well.

Yeah, and I love that collaboration angle. And there's one thing that you also mentioned kind of that in the past, data science workflows where a lot of teams, businesses would come with a request, they would throw it over the fence, come back with the wrong answer. Like you had that siloing effect. Do you see now that the data team's time is unlocked, work on more higher level strategic objectives rather than doing analysis that no one ends up reading anyways. Because the bit business user now is empowered as well to fish for themselves and find answers for themselves.

Part of that is true, right? I mean, at the end of the day, if you always compare it to. If you don't know what a regression line does, I can make it as easy as possible for you to create one. You will not be able to interpret the results. Right. At the end of the day, you still need to understand what those methods all do, but you don't necessarily need to understand how to make them do it right. How is it done? I don't need to know how these coefficients of the regression line were actually found, but I need to know what they mean. So yes, I think it makes it easier to get that into people's hands, but I wouldn't say you can just now. Everybody's empowered to run complicated analyses. I think you still need to train people up so that they understand what they're doing.