Tableau

OCTOBER 7, 2024

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.

Tableau 137

Tableau Data Quality Analytics Analytics 137

ML @ CMU

MAY 12, 2023

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., In this post we discuss our research on federated learning , which aims to tackle this challenge by performing decentralized learning across private data silos.

Data Silos 130

Data Silos Algorithm ML ML 130

phData

SEPTEMBER 27, 2024

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.

Data Modeling 52

Data Modeling Data Models Apache Kafka Data Lakes 52

Tableau

APRIL 18, 2022

What if the problem isn’t in the volume of data, but rather where it is located—and how hard it is to gather? Nine out of 10 IT leaders report that these disconnects, or data silos, create significant business challenges.* Data modeling. Data preparation. Loss of visibility after data leaves EDW.

Tableau 101

Tableau Data Quality Analytics Analytics 101

Tableau

APRIL 18, 2022

What if the problem isn’t in the volume of data, but rather where it is located—and how hard it is to gather? Nine out of 10 IT leaders report that these disconnects, or data silos, create significant business challenges.* Data modeling. Data preparation. Loss of visibility after data leaves EDW.

Tableau 98

Tableau Data Quality Analytics Analytics 98

Alation

MAY 31, 2023

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.

Data Governance 52

Data Governance Data Quality Data Silos Data Modeling 52

IBM Journey to AI blog

SEPTEMBER 5, 2024

Start small by setting measurable goals and assigning ownership of data domains. Establishing standardized definitions and control measures builds a solid foundation that evolves as the framework matures. Define roles and responsibilities A successful data governance framework requires clearly defined roles and responsibilities.

Data Governance 75

Data Governance Data Quality DataOps Artificial Intelligence 75