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Delivering Low-latency Analytics Products for Business Success

Rocket-Powered Data Science

The results showed that (among those surveyed) approximately 90% of enterprise analytics applications are being built on tabular data. The ease with which such structured data can be stored, understood, indexed, searched, accessed, and incorporated into business models could explain this high percentage.

Analytics 166
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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,…). Examples: Cars, Trucks, Taxis. See [link]. Industry 4.0 2) Connected cars.

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Building Better Data Models to Unlock Next-Level Intelligence

Sisense

You can’t talk about data analytics without talking about data modeling. The reasons for this are simple: Before you can start analyzing data, huge datasets like data lakes must be modeled or transformed to be usable. Building the right data model is an important part of your data strategy.

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8 data strategy mistakes to avoid

CIO Business Intelligence

Data silos result in inconsistencies and operational inefficiencies, says John Williams, executive director of enterprise data and advanced analytics at RaceTrac, an operator of convenience stores. Their large language models have poor or dirty data. Data cleansing tools are one way to address the problem, Johnson says.

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11 dark secrets of data management

CIO Business Intelligence

For example, they may not be easy to apply or simple to comprehend but thanks to bench scientists and mathematicians alike, companies now have a range of logistical frameworks for analyzing data and coming to conclusions. More importantly, we also have statistical models that draw error bars that delineate the limits of our analysis.

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Using Artificial Intelligence to Make Sense of IoT Data

BizAcuity

There is a coherent overlap between the Internet of Things and Artificial Intelligence. IoT is basically an exchange of data or information in a connected or interconnected environment. Data is only useful when it is actionable for which it needs to be supplemented with context and creativity. Future of IoT is AI.

IoT 56
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Transforming Big Data into Actionable Intelligence

Sisense

Looking at the diagram, we see that Business Intelligence (BI) is a collection of analytical methods applied to big data to surface actionable intelligence by identifying patterns in voluminous data. As we move from right to left in the diagram, from big data to BI, we notice that unstructured data transforms into structured data.