Remove Data Models Remove Data Silos Remove Information
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Data Intelligence empowers informed decisions

Pickl AI

In the realm of Data Intelligence, the blog demystifies its significance, components, and distinctions from Data Information, Artificial Intelligence, and Data Analysis. Data Intelligence emerges as the indispensable force steering businesses towards informed and strategic decision-making. These insights?

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The Evolution of Customer Data Modeling: From Static Profiles to Dynamic Customer 360

phData

Introduction: The Customer Data Modeling Dilemma You know, that thing we’ve been doing for years, trying to capture the essence of our customers in neat little profile boxes? For years, we’ve been obsessed with creating these grand, top-down customer data models. Yeah, that one.

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Data Integrity: The Foundation for Trustworthy AI/ML Outcomes and Confident Business Decisions

ODSC - Open Data Science

As critical data flows across an organization from various business applications, data silos become a big issue. The data silos, missing data, and errors make data management tedious and time-consuming, and they’re barriers to ensuring the accuracy and consistency of your data before it is usable by AI/ML.

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When and How to Use Multi-fact Relationships in Tableau

Tableau

Spencer Czapiewski July 25, 2024 - 5:54pm Thomas Nhan Director, Product Management, Tableau Lari McEdward Technical Writer, Tableau Expand your data modeling and analysis with Multi-fact Relationships, available with Tableau 2024.2. You may have heard of Multi-fact Relationships informally referred to as “shared dimensions.”

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Tableau and dbt Labs: Strategic Partnership and Integration

Tableau

Spencer Czapiewski October 7, 2024 - 9:59pm Madeline Lee Product Manager, Technology Partners Enabling teams to make trusted, data-driven decisions has become increasingly complex due to the proliferation of data, technologies, and tools. Tableau Pulse: Tableau Pulse metrics can be directly connected to dbt models and metrics.

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On Privacy and Personalization in Federated Learning: A Retrospective on the US/UK PETs Challenge

ML @ CMU

Unfortunately, while this data contains a wealth of useful information for disease forecasting, the data itself may be highly sensitive and stored in disparate locations (e.g., learning across large networks of devices such as mobile phones), the area of cross-silo FL (e.g., of people became infected in the final week.

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The Data Architect’s Role in Data Governance

Alation

They collaborate with IT professionals, business stakeholders, and data analysts to design effective data infrastructure aligned with the organization’s goals. Their broad range of responsibilities include: Design and implement data architecture. Maintain data models and documentation.