Data BasementJul 06, 2020
Guest Episode: The A-List Podcast w/ Anurag (LLM Privacy and Security)
In this episode from the A-List Podcast Anurag and I discuss the data privacy and security intricacies of large language models. Check out other episodes from his podcast which focuses on Fraud, Compliance, and Machine learning here https://open.spotify.com/show/4VzKE07GR8k34v0ofkHyQx.
Featured: Machine Learning & Marketing
In this episode, we talk to Alex Makarski, engineer-turned-marketer, founder of clickmakers.io, and co-founder of measurebit.com a marketing analytics consultancy. In this episode we cover the evolution of machine learning in the marketing industry, the changes in new technologies such as Large Language Models and bringing, and the need for companies to capture and maximize the value of their data.
Building a Data Product: The Dream Team
In this episode, we'll be sharing tips on how to attract and hire the best data analytics professionals, as well as how to retain them once they're on your team. We'll cover topics such as the key skills and qualities to look for in candidates, ways to keep your team engaged and motivated, and strategies for fostering a positive work culture. Whether you're a hiring manager or a data analytics professional looking to advance your career, this episode has something for everyone. Tune in to learn how to build and maintain a top-notch data analytics team.
Building a Data Product: Data Assets
In this episode of Data Basement, we delve into the world of data assets and explore their importance for businesses and organizations. We'll define data assets and discuss their characteristics, including clean vs dirty data and processed vs raw data. We'll also cover the different types of data assets and how their value can vary, including data that becomes more valuable the more you collect and the longer you keep it, data that stops being valuable when you stop collecting it, and data that is always valuable. Tune in to learn more about how data assets can help inform decision-making, provide a competitive advantage, and drive business success.
Building a Data Product: ELI5 Data Strategy
A data strategy is a plan that outlines how an organization will collect, store, use, and govern data. It includes the goals and objectives for the organization's data usage, as well as the resources and processes needed to achieve those goals. A data strategy helps an organization to align its data initiatives with its business goals and to make informed decisions about how to best use data to drive value.
Building a Data Product: Analytics
In this episode, we explore the exciting world of building analytics products. We'll discuss the different challenges and opportunities that come with working with data, and how to approach them in a way that maximizes impact. We'll also cover key considerations for building data products, such as choosing the right data sources, cleaning and preprocessing data, and building effective algorithms and models. Whether you're a data scientist, software engineer, or simply someone interested in working with data, this episode has something for you. Tune in to learn more about building data products!
Building a Data Product: A guide to Data Privacy
In this episode of Data Basement, we're discussing the crucial role that product managers play in ensuring data privacy for their companies and users. With the increasing importance of data protection regulations like the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), product managers must understand how to handle and protect sensitive customer information. here we discuss the importance of incorporating privacy by design into product development, creating clear and concise privacy policies, and managing user consent.
Whether you're new to the field of product management or a seasoned pro, this episode will provide valuable insights on how to tackle the challenges of data privacy. Tune in to learn more!
Featured: Data Processing in Python
In this episode we interview Tiago Rodrigues Antao, to learn more about how and why Python has taken the data processing world by storm. We will also learn some of the language's limitations, examples of how to address them, and how these could help minimize the commercial costs of your application and potentially your code's effect on the environment. You can learn more about this topic in his new book High-Performance Python for Data Analytics.
Featured: Data Strategy - What it is and what it's not.
In this epsisode, Randy Lariar, a Management Consultant at EY, talks to us about data trends, the importance of a data strategy, and its different elements. We get into concepts like data management, regulatory implications and business strategy. You can find Randy on LinkedIn https://www.linkedin.com/in/lariar/episode
Featured: Confidential Computing
In this episode, I interview David Sturzenegger, Head of Product at Decentriq. One of the biggest challenges of machine learning is having customers trust that their data is being handled safely. During this conversation, you will learn about confidential computing and how it can help develop analytics products in consumer products, healthcare, and beyond.
Featured: The data of cyber insurance
In this episode I talk to Tom Buoniello from Binary Edge who explains some of the complexities of cybersecurity insurance underwriting, interesting use cases for threat intelligence data and how to complement not replace your cybersecurity strategy with cybersecurity insurance.
Featured: Spark In Action - The Analytics Operating System
In this episode, Jean Georges Perrin, Software Architect and IBM Champion talks to us about the benefits of Spark for data analysis. We also go into the motivation for writing his new book Spark in Action which allows developers to get the benefits of Apache Spark in Java and without having to learn the intricacies of Scala.
Featured: Event processing and Analytics via PubSub
Featured: An all-in-one Database
In this episode we interview Fintan Quill Director of Engineering at Shakti and talk about data analysis for time-series, machine learning, big data architecture and much more.