“The more that you read, the more things you will know. The more that you learn, the more places youʼll go.”

– Dr. Seuss

Resources

Find customer stories, webinars, blog articles and other useful docs and downloads

FME Guide

Your guide to FME is our user friendly Introduction to the FME Platform. Say goodbye to data silos and hello to fully connected, trustworthy and secure data with FME. Make the most of FME with the help of Locus. your complete deployment, training and support Partner.

FME Flow for the Automation Win

Automation is key to improving stakeholder and customer experiences by streamlining mundane tasks and boosting productivity. FME Flow, in tandem with FME Form, offers scalable solutions to handle growing data complexities. Learn how FME Flow automates, scales, and manages your data workflows, ensuring efficient and effective data integration across your organisation.

Does AI Hallucinate about Electric Sheep?

AI is revolutionising our world, but we must scrutinize its outputs just as we do with human advice. LLMs like ChatGPT can hallucinate, inventing information with alarming confidence, reminding us to always question and verify their responses.

Introducing the Locus FME Utility Meet-Up

Join the FME Utility Meet-Up a new user group dedicated to fostering collaboration and knowledge sharing among FME users n the utility sector across New Zealand and Australia.

IBM’s TechXchange: Socks, WatsonX.AI and FME

Explore the convergence of AI innovation and data management at IBM’s TechXchange event in Auckland. Discover the transformative capabilities of IBM watsonx.ai, alongside FME’s prowess in data integration. Dive into the future of AI connectors and uncover insights into IBM’s silent dominance in technology, all while embracing the allure of IBM socks.

Finding the Balance in Big Data vs Small Data

inspired by the rise of Small Data trends, this blog explores the evolving relationship and balance between Big Data and Small Data. Defining Small Data as human-comprehensible information providing context to Big Data, the article highlights the challenges of interpreting large datasets and biases in analysis. Ultimately, it advocates for a balanced approach, using both..

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