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10 Examples of How Big Data in Logistics Can Transform The Supply Chain

datapine

You can use big data analytics in logistics, for instance, to optimize routing, improve factory processes, and create razor-sharp efficiency across the entire supply chain. The big data market is expected to exceed $68 billion in value by 2025 , a testament to its growing value and necessity across industries.

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Buy Your Embedded Analytics and Empower Your End-Users With the Right Data

Jet Global

These costs are not always visible when companies plan for their analytics offering but can significantly impact production, scale, and the speed of bringing analytics to market. The challenge is collecting all that data into one place and making it understandable. Following along as we see why buying is better.

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Quantitative and Qualitative Data: A Vital Combination

Sisense

Let’s consider the differences between the two, and why they’re both important to the success of data-driven organizations. Digging into quantitative data. This is quantitative data. It’s “hard,” structured data that answers questions such as “how many?” First, data isn’t created in a uniform, consistent format.

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And the winners are…. Congratulations to the Sixth Annual Data Impact Awards winners

Cloudera

Toshiba Memory’s ability to apply machine learning on petabytes of sensor and apparatus data enabled detection of small defects and inspection of all products instead of a sampling inspection. Voya Financial prevented millions of dollars of fraudulent transactions by deploying predictive analytic capabilities on Cloudera.

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The Gartner 2021 Leadership Vision for Data & Analytics Leaders Webinar Q&A

Andrew White

On January 4th I had the pleasure of hosting a webinar. It was titled, The Gartner 2021 Leadership Vision for Data & Analytics Leaders. This was for the Chief Data Officer, or head of data and analytics. How does CDO overlap with Market Research functions? This is not how it works though.

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How to choose the best AI platform

IBM Big Data Hub

Major cloud infrastructure providers such as IBM, Amazon AWS, Microsoft Azure and Google Cloud have expanded the market by adding AI platforms to their offerings. Generative AI capabilities Content generator: Generative AI refers to deep-learning models that can generate text, images and other content based on the data they were trained on.