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Paolo Platter

Speaker

Paolo Platter

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talks

CTO & Co-founder of Agile Lab, & Product Manager - Witboost Agile Lab

After earning a master’s degree in Telecommunications Engineering at the Politecnico di Torino in Italy, Paolo focused on innovative distributed technologies, achieving top-level certifications and building a notable curriculum in software development.

He is the co-founder (2014) and CTO of Agile Lab, a fast-growing company focused on scalable technologies, Big Data, and AI in production. Within the organization, Paolo explores emerging technologies, evaluates new concepts and technological solutions, and leads Operations and Architectures. Over the years, he has been involved in very challenging Big Data projects across Europe with top enterprise companies.

In 2017, Paolo also began collaborating as a software mentor with the European Innovation Academy, a recognized leader in tech entrepreneurship education.

Bio from: Big Data LDN 2024

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This talk will explore a platform strategy that emphasizes the decentralization of data and analytics, aiming to achieve an optimal balance between autonomy and governance, thereby increasing iteration and innovation speed while ensuring compliance with regulations. Attendees will learn how to support the entire data product lifecycle, enabling teams to operate independently while adhering to governance and architectural standards. 

The discussion will highlight the following key areas:

1. Autonomy and Innovation: How decentralized data platforms empower teams to innovate faster by reducing dependencies and bottlenecks. Examples of successful implementations will be provided, illustrating how autonomy can lead to increased iteration and innovation speed.

2. Governance and Compliance: Strategies for maintaining robust governance frameworks that ensure data quality, security, and compliance with regulations such as GDPR and HIPAA. The talk will cover tools and best practices for monitoring and enforcing compliance in a decentralized environment.

3. Data Product lifecycle: A comprehensive approach to supporting the data product lifecycle, from data product prototyping to the data product operations, monitoring and change management. 

4. Adoption: Real-world scenarios where organizations have navigated the trade-offs between autonomy and governance, creating the right condition for platform adoption.