How Will Martin Carniglia Elevate Good Moose’s Data Strategy?

Good Moose has taken a pivotal leap forward with the appointment of Martin Carniglia as Head of Analytics & Data Science. This move signals the agency’s resolution to embed more sophisticated data-centric approaches into its growth strategies. Carniglia brings a wealth of experience to the table, having significantly impacted data-driven marketing at both R/GA and Havas Group. His primary focus will be to oversee and enhance the analytics framework — refining data automation and honing measurement techniques to ensure that campaign performance can be rigorously assessed and optimized.

For Good Moose’s diverse client base, which includes major players like Capital One and Mercado Libre, Carniglia’s expertise promises a new era of insights and precision. By advancing predictive modeling capabilities, he aims to not only interpret current data trends but also forecast future market movements, providing clients with a competitive edge. This forward-thinking approach is expected to bolster client performance, driving sustainable growth through informed decisions and actions.

Strategic Growth Through Data

Good Moose has taken its operations up a notch by bringing on board Martin Carniglia as Head of Analytics & Data Science, demonstrating a commitment to data-driven growth. Carniglia’s previous successes at R/GA and Havas Group will now enrich Good Moose’s data initiatives. One of his key roles includes refining the agency’s analytics system, improving data automation, and tightening measurement methods to optimize campaign outcomes.

This strategic hire denotes a new chapter of advanced insights for clients like Capital One and Mercado Libre. Carniglia will work on enhancing predictive models to not just understand but also anticipate market shifts, offering a strategic advantage. His integration into the team is poised to amplify client success, fostering sustained progress through data-led strategy. With Carniglia’s expertise, Good Moose stands to transform how campaign performance is evaluated, benefiting from a more precise and future-facing analytical approach.

Explore more

What If Data Engineers Stopped Fighting Fires?

The global push toward artificial intelligence has placed an unprecedented demand on the architects of modern data infrastructure, yet a silent crisis of inefficiency often traps these crucial experts in a relentless cycle of reactive problem-solving. Data engineers, the individuals tasked with building and maintaining the digital pipelines that fuel every major business initiative, are increasingly bogged down by the

What Is Shaping the Future of Data Engineering?

Beyond the Pipeline: Data Engineering’s Strategic Evolution Data engineering has quietly evolved from a back-office function focused on building simple data pipelines into the strategic backbone of the modern enterprise. Once defined by Extract, Transform, Load (ETL) jobs that moved data into rigid warehouses, the field is now at the epicenter of innovation, powering everything from real-time analytics and AI-driven

Trend Analysis: Agentic AI Infrastructure

From dazzling demonstrations of autonomous task completion to the ambitious roadmaps of enterprise software, Agentic AI promises a fundamental revolution in how humans interact with technology. This wave of innovation, however, is revealing a critical vulnerability hidden beneath the surface of sophisticated models and clever prompt design: the data infrastructure that powers these autonomous systems. An emerging trend is now

Embedded Finance and BaaS – Review

The checkout button on a favorite shopping app and the instant payment to a gig worker are no longer simple transactions; they are the visible endpoints of a profound architectural shift remaking the financial industry from the inside out. The rise of Embedded Finance and Banking-as-a-Service (BaaS) represents a significant advancement in the financial services sector. This review will explore

Trend Analysis: Embedded Finance

Financial services are quietly dissolving into the digital fabric of everyday life, becoming an invisible yet essential component of non-financial applications from ride-sharing platforms to retail loyalty programs. This integration represents far more than a simple convenience; it is a fundamental re-architecting of the financial industry. At its core, this shift is transforming bank balance sheets from static pools of