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The Hyper-personalization Data and Privacy Debate?-?Solved

Tullio Siragusa

The Hyper-personalization Data and Privacy Debate?-?Solved. Data is what we share with the websites we visit. In this podcast recap blog post, we will discuss how websites store data, what goes on with the data that we share, and how we can have an option to control our data to have personalized experiences.

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The Rise in Data Privacy and Its Impact on Your Brand

Brunner

Only a few short years ago, the general public learned companies were collecting and selling data with every click consumers made. In 2018, Facebook CEO Mark Zuckerberg was called to address Congress about a data-sharing scandal with Cambridge Analytica, a company that bought data from Facebook to target political advertising.

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How Data Collaboration Platforms Can Help Companies Build Better AI

Harvard Business Review

There are three immediate challenges for companies that want to train fine-tuned AI models: 1) they require extensive, high-quality data — a scarce resource for many enterprises, 2) third-party AI models can include problematic biases, and 3) training fine-tuned models with users’ personal data may result in privacy violations.

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What Do You Do When Customers Want Privacy And Businesses Need Data?

The Human Factor

The way businesses are using data is being analyzed by the very people it is taken from – customers. Although it’s healthy for shoppers, it’s bad for the businesses that have gotten used to the insights they glean from cultivating big data. 56% plan on sharing less in the future. Stop Being Weird! You don’t mean it.

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Post-Pandemic eCommerce Growth: Leverage Product Data, Market Research & Shopping Trends

Speaker: Phil Irvine, VP & Director of Audience Intelligence

To accomplish this, organizations have traditionally leaned into historical customer and product data to predict how to engage with their current and future customers in a personalized manner. When you couple that with fluid data privacy changes, this creates an even fuzzier foundation to develop forward-looking marketing strategies.

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How the Cloud Is Changing Data Science

Harvard Business Review

Cloud tools and technologies are influencing the future of data science work in two key areas: scaling resources and improving workforce agility. If organizations want to make use of these capabilities, though, they also need to develop strong data security and privacy frameworks when operating in a cloud environment.

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Innovation on Steroids: Next Generation AI-Powered Phases and Gates

Leapfrogging

By harnessing the power of AI, organizations are able to process vast amounts of data, identify patterns, and make more informed decisions at every phase of the innovation process. AI offers the ability to process vast amounts of data, identify patterns, and provide insights that are beyond the capability of human analysis.

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LLMOps for Your Data: Best Practices to Ensure Safety, Quality, and Cost

Speaker: Shreya Rajpal, Co-Founder and CEO at Guardrails AI & Travis Addair, Co-Founder and CTO at Predibase

However, productionizing LLMs comes with a unique set of challenges such as model brittleness, total cost of ownership, data governance and privacy, and the need for consistent, accurate outputs.