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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. Did you know?

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Your Ultimate Guide To Modern KPI Reports In The Digital Age – Examples & Templates

datapine

Fortunately, in the digital age, a wealth of invaluable KPIs exist to assist growth in a number of areas from marketing and accounts to customer service, fulfillment, and beyond. Also, explore our guide to KPI management and learn from a host of helpful best practices. 3) Consider your data sources. click to enlarge**.

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Customizing Personal Lines Insurance with Location Data

Cloudera

I have been researching more about how we can use the new data from those devices to design more innovative insurance products while being aware that these should all be contingent upon customer opt-in. I recently attended one of Majesco’s excellent webinars hosted by Denise Garth, Chief Strategy Officer.

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Fivetran Modern Data Stack Conference 2023: Key Takeaways

Alation

We had a great time meeting with customers and demonstrating how a data intelligence platform delivers visibility across the data stack with demos. So, how can a data catalog support the critical project of building data pipelines? Mitesh Shah , VP of Product Marketing, took the stage to answer this question.

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Big Data Options Spark Confusion On Web Design Options For SMEs

Smart Data Collective

Last summer, Big Data Analytics News discussed the benefits of using big data in web design. Many of the benefits of big data are outstanding. However, some people have been misled into believing that big data enables them to create high quality websites without any experience. This is simply not the case.

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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. Previously, Greentube faced numerous constraints created by data silos and lack of real-time analytic capabilities.