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Feeding America turns to data to feed the hungry

CIO Business Intelligence

We coordinate donations from manufacturers, retailers, grocers. We didn’t have basic things like a data warehouse. We want to be a data-first organization, and to really drive impact through insights, you need a centralized place to store and analyze the data.”. We source a lot of food.

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With a zero-ETL approach, AWS is helping builders realize near-real-time analytics

AWS Big Data

Another example of AWS’s investment in zero-ETL is providing the ability to query a variety of data sources without having to worry about data movement. Data analysts and data engineers can use familiar SQL commands to join data across several data sources for quick analysis, and store the results in Amazon S3 for subsequent use.

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Straumann Group is transforming dentistry with data, AI

CIO Business Intelligence

My vision is that I can give the keys to my businesses to manage their data and run their data on their own, as opposed to the Data & Tech team being at the center and helping them out,” says Iyengar, director of Data & Tech at Straumann Group North America.

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Week in the Life of an Analyst at Gartner US IT Symposium (virtual) 2021

Andrew White

Manufacturer (process or discrete) 8. Lakehouse (data warehouse and data lake working together) 8. Data Literacy, training, coordination, collaboration 8. Data Management Infrastructure/Data Fabric 5. Data Integration tactics 4. Financial Services 4. Healthcare 4. Higher Ed. Logistics 1.

IT 52
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Fast Provisioning of data through Data Virtualization in the Era of ever-increasing Data Fluidity

Data Virtualization

The way products are getting manufactured is being transformed with automation, robotics, and. We are in the midst of a significant transformation in each and every sphere of business. We are witnessing an Industrial 4.0 revolution across the industrial sectors.

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10 key roles for AI success

CIO Business Intelligence

Because of this, only a small percentage of your AI team will work on data science efforts, he says. The rest of the team will identify the problem being solved, help explain the data, help organize the data, integrate the output into another production system, or present the data in a presentation-ready manner.”.

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Top Graph Use Cases and Enterprise Applications (with Real World Examples)

Ontotext

As such, most large financial organizations have moved their data to a data lake or a data warehouse to understand and manage financial risk in one place. Yet, the biggest challenge for risk analysis continues to suffer from lack of a scalable way of understanding how data is interrelated.