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

datapine

Winkenbach said that his data showed that “deliveries in big cities are almost always improved by creating multi-tiered systems with smaller distribution centers spread out in several neighborhoods, or simply pre-designated parking spots in garages or lots where smaller vehicles can take packages the rest of the way.”

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

Jet Global

Market Drivers and Current Trends Organizations are increasing focus on the potential value within big data, seeking to better understand their customers and improve their products. The challenge is collecting all that data into one place and making it understandable. Need their analytics to scale reliably with their app or software.

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Quantitative and Qualitative Data: A Vital Combination

Sisense

These techniques allow you to: See trends and relationships among factors so you can identify operational areas that can be optimized Compare your data against hypotheses and assumptions to show how decisions might affect your organization Anticipate risk and uncertainty via mathematically modeling.

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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. But we also know not all data is equal, and not all data is equally valuable.

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How to choose the best AI platform

IBM Big Data Hub

Will it be implemented on-premises or hosted using a cloud platform? Enhanced security Open source packages are frequently used by data scientists, application developers and data engineers, but they can pose a security risk to companies. Is it intended for internal team use or to be accessible to external customers?