Episode 024 - Is low-code Spatial AI possible? With Grant Case from Dataiku - VIDEO EDITION

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:   / analyticseverywhere  


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.