Is low-code spatial AI possible? With Grant Case from Dataiku

Is point’n’click end-to-end low-code spatial AI… possible? What about *no code*? Spoiler alert: the answers are yes and yes. Knowledge of development patterns and code syntax and how many words you can type per minute are no longer barriers to entry for most of us. It’s time to get our hands dirty with spatial data!

This episode features Grant Case, the Vice President of Sales Engineering at Dataiku for the Australia Pacific Japan region.

AB and Grant discuss Dataiku’s AI platform and its capabilities in handling various data types, including structured, unstructured, and spatial data. Grant highlights Dataiku’s ability to cater to different user personas, from low-code and no-code users to pro-coders, through its intuitive interface and integration with open-source libraries.

On a wider note, we explore the advancements in large language models (LLMs) and their impact on data analysis, particularly in the spatial domain. Grant shares examples of how Dataiku leverages LLMs and digital twinning to enhance data understanding and decision-making processes. The conversation also touches on the role of Chief Data Officers, data governance challenges, and the trade-offs between building custom solutions and leveraging existing tools.

Connect with Grant on LinkedIn at: https://www.linkedin.com/in/analyticseverywhere

We’re also publishing this episode on YouTube, if you’d like to watch along in full living colour: https://youtu.be/1EU042y4_7A

Chapters

05:17 – Dataiku’s AI Platform and User Personas

Grant explains Dataiku’s AI platform, which caters to different user personas, from low-code and no-code users to pro-coders. The platform aims to bring these diverse users together across multiple technologies, allowing them to work in their preferred manner. Dataiku has been recognized as a leader in the Gartner Magic Quadrant for its completeness of vision, particularly in catering to low-code and no-code users.

10:16 – Advancements in Large Language Models (LLMs)

The conversation shifts to the advancements in large language models (LLMs) and their impact on data analysis. Grant discusses how LLMs have opened up new possibilities for unstructured data use cases, such as natural language processing (NLP) and spatial analysis. He provides examples of how LLMs can assist in tasks like understanding business locations and mapping data.

22:36 – Digital Twinning and Spatial Data Analysis

Grant highlights the concept of digital twinning, which involves creating virtual replicas of physical systems or environments. He discusses how digital twinning can be applied to various domains, such as disaster recovery, infrastructure planning, and manufacturing. Grant also shares examples of how Dataiku leverages LLMs and computer vision for spatial data analysis and decision-making.

35:45 – Open-Source Integration and Deployment Options

The discussion touches on Dataiku’s integration with open-source libraries and its deployment options. Grant emphasizes Dataiku’s ethos of being open to both proprietary and open-source technologies, allowing customers to choose the best solution for their needs. Dataiku supports cloud, on-premises, and hybrid deployment models to cater to different organizational requirements.

31:15 – Data Governance and the Role of Chief Data Officers

AB and Grant discuss the challenges of data governance and the role of Chief Data Officers (CDOs) in organizations. Grant acknowledges the ongoing struggle with data quality and governance, highlighting the importance of proving the value of data and AI initiatives to secure a seat at the executive table.

36:36 – Build vs. Buy: Leveraging Existing Solutions

The conversation explores the trade-offs between building custom solutions and leveraging existing tools. Grant advocates for evaluating whether a solution provides a competitive advantage or solves a unique problem before investing in building it from scratch. He emphasizes the importance of focusing on value-adding activities rather than reinventing the wheel for solved problems.

45:29 – Future Developments and Retrieval Augmented Generation (RAG)

Grant shares his thoughts on future developments in the AI and data analytics space, including the concept of Retrieval Augmented Generation (RAG). RAG involves combining LLMs with an organization’s own data to provide more contextualized and relevant responses. While RAG offers a way to quickly derive value, Grant acknowledges its limitations and sees it as a waypoint rather than the final solution.

Transcript and Links

AB
Well g’day, and welcome back to SPAITIAL. This is Episode 24, coming to you after, yes, a minor hiatus again. Apologies, I do often have things called ‘work’ and out-of-town-isms. Apologies, but we’re back on a regular schedule with this episode and one booked in for next week and the following week.

With me today I have the great pleasure of chatting once again, not with SPAITIAL, but back in old-school territory here with Grant Case. Grant Case is Vice President, Sales Engineering at Dataiku, Australia, Pacific, Japan.

Oh look, your title is long and varied. I’ll let you introduce yourself. Grant, welcome to SPAITIAL.

Grant
Thanks, Andrew. Hi, everybody. I’m Grant Case. I am the Regional Vice President for Sales Engineering here at Dataiku. For myself, I work in the Sales Engineering / Solution Engineering space, where I spend most of my time with clients across the region, but particularly here in ANZ, where we talk to organizations, both large and small, in and around analytics and AI.

I’ve been in with Dataiku for the last six years, but I’ve been doing everything, analytics, AI. Before we were calling it AI, it was all statistics, right, Andrew?

AB
It’s just math in the end. It’s just math.

Grant
It’s just math. It’s ones and zeros, but I’ve been doing it for the past 20 years across multiple different industries and quite a bit of that. Spending time within different domains, whether we’re talking about NLP, we’re talking about just machine learning, but also GIS has always been a very interesting background and interesting set of projects that I’ve worked with.

So happy to be aboard.

AB
So six years. Six years at Dataiku. I must say at the outset, I’m going to do the Australian way. I mean, you do the North American data.

Grant
I come from Queensland, right? Northern Queensland?

AB
VERY far north Queensland. But obviously from the US. For the record, Aussies versus Americans, you know, the ‘data versus data’. So we (we Aussies) have problems talking about data, the character in Star Trek versus data. That’s always wrong. But at the same time, we can, we can figure out the difference between ‘routing’ and ‘route’, which is nice. So we, we lose on one tech term, but we gain on the other. So that’s exactly, it’s a net neutral.

So that’s nice. Six years at Dataiku. I think I was chatting to you pretty much the month that you started. This is back before COVID BC and before the current sort of situation. You were, look, Dataiku was one of those tools – is one of those tools – that for me is still pure magic to sort of, you know, Vaguely quote Arthur C. Clark, you know, technology that’s in just indiscernible from magic is just, you know, a joy to use. And that’s really what it is. It’s a walk-up tool that does everything data related.

And yes, ‘everything’ is a big claim, but it really is. I’ve been describing to people with the catchy non catchphrase of it’s the no code, low code and pro code. It sort of doesn’t lock you out of doing the hard way if you want or doing a low code, which is the title of this episode – and a topic we will definitely come back to.

So if we do manage to low code is the graphical point click, no less powerful, but certainly if you need to do something quickly, look that goes to that goes to that, but the, uh, thing that really blew my mind and you’re probably going to blow my mind even more cause I haven’t caught up with the Dataiku world for a year probably — is the no code just press a button and have everything done for you? Do you want to give us a rundown on where Dataiku is and where it sits? What the other, yeah.

Grant
I’m gonna put you right in front of a client because you did a great job of it. So as a platform, we look at organizations today and try and understand that there are different levels of maturity, the different personas.

So anyone from just someone who just needs to consume the dashboard all the way up to someone like yourself, Andrew, that’s getting in, messing, fine tuning on different NLP algorithms, they all need to work together, right?

So Dataiku is the AI platform to bring all of those individuals together across multiple different technologies. So nobody, everybody works the way that’s best for them. Obviously, just from a pure number standpoint, we have more low code and no code users.

That’s just the way of the world, right? But the need is for everyone to be able to access. So yeah, so we’ve actually just announced as a leader in the Gartner Magic Quadrant this past year and are a few months ago and most, we are the furthest along on completeness of vision in many ways because of what the title of this episode is talking about those low code and no code users and how do you bring some of the sophisticated things that we were doing, you and I were maybe doing five years ago, maybe even two years ago in code. And now it’s run of the bill, anyone can do it.

AB 05:59
And