Remove Business Intelligence Remove Internet of Things Remove IoT Remove Structured Data
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Glossary of Digital Terminology for Career Relevance

Rocket-Powered Data Science

Autonomous Vehicles: Self-driving (guided without a human), informed by data streaming from many sensors (cameras, radar, LIDAR), and makes decisions and actions based on computer vision algorithms (ML and AI models for people, things, traffic signs,…). 2) Gbit/sec Internet. (3) Examples: Cars, Trucks, Taxis. Industry 4.0

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8 data strategy mistakes to avoid

CIO Business Intelligence

With nearly 800 locations, RaceTrac handles a substantial volume of data, encompassing 260 million transactions annually, alongside data feeds from store cameras and internet of things (IoT) devices embedded in fuel pumps.

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Transforming Big Data into Actionable Intelligence

Sisense

Attempting to learn more about the role of big data (here taken to datasets of high volume, velocity, and variety) within business intelligence today, can sometimes create more confusion than it alleviates, as vital terms are used interchangeably instead of distinctly. displaying BI insights for human users).

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How gaming companies can use Amazon Redshift Serverless to build scalable analytical applications faster and easier

AWS Big Data

It includes business intelligence (BI) users, canned and interactive reports, dashboards, data science workloads, Internet of Things (IoT), web apps, and third-party data consumers. Popular consumption entities in many organizations are queries, reports, and data science workloads.

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11 dark secrets of data management

CIO Business Intelligence

Text by itself doesn’t have much structure to begin with, but when you’ve got a pile of text written by hundreds or thousands of employees over dozens of years, then whatever structure there is might be even weaker. Even structured data is often unstructured.

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Building Better Data Models to Unlock Next-Level Intelligence

Sisense

We’re going to nerd out for a minute and dig into the evolving architecture of Sisense to illustrate some elements of the data modeling process: Historically, the data modeling process that Sisense recommended was to structure data mainly to support the BI and analytics capabilities/users. Dig into AI.