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Beyond the hype: Do you really need an LLM for your data?

CIO Business Intelligence

The hype around large language models (LLMs) is undeniable. They promise to revolutionize how we interact with data, generating human-quality text, understanding natural language and transforming data in ways we never thought possible. Tableau, Qlik and Power BI can handle interactive dashboards and visualizations.

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How to Set AI Goals

O'Reilly on Data

This in turn would increase the platform’s value for users and thus increase engagement, which would result in more eyes to see and interact with ads, which would mean better ROI on ad spend for customers, which would then achieve the goal of increased revenue and customer retention (for business stakeholders).

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Structural Evolutions in Data

O'Reilly on Data

” Each step has been a twist on “what if we could write code to interact with a tamper-resistant ledger in real-time?” While data scientists were no longer handling Hadoop-sized workloads, they were trying to build predictive models on a different kind of “large” dataset: so-called “unstructured data.”

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InfoTribes, Reality Brokers

O'Reilly on Data

Content creators and content consumers are connected, share information, and develop mental models of the world, along with shared or distinct realities, based on the information they consume. Online spaces are novel forms of community: people who haven’t met and may never meet in real life interacting in cyberspace.

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Bridging the Gap: How ‘Data in Place’ and ‘Data in Use’ Define Complete Data Observability

DataKitchen

Data in Use pertains explicitly to how data is actively employed in business intelligence tools, predictive models, visualization platforms, and even during export or reverse ETL processes. There are multiple locations where problems can happen in a data and analytic system. What is Data in Use?

Testing 169
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The quest for high-quality data

O'Reilly on Data

For example, a complex sophisticated model for finding duplicates or matching schema is the least of our worries if we cannot even enumerate all possible pairs that need to be checked. An important paradigm for solving both these problems is the concept of data programming.

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Catching Feels

Insight

While most of these signals are implicitly communicated during human-to-human interaction, we do not have a method for quantifying feeling and mood through individual behavioral signals expressed on the digital platform. Prediction models An Exploratory Data Analysis showed improved performance was dependent on gender and emotion.