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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.”

Big Data 275
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A summary of Gartner’s recent DataOps-driven data engineering best practices article

DataKitchen

Overview of Gartner’s data engineering enhancements article To set the stage for Gartner’s recommendations, let’s give an example of a new Data Engineering Manager, Marcus, who faces a whole host of challenges to succeed in his new role: Marcus has a problem. are more efficient in prioritizing data delivery demands.”

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Accelerate Insight with Proactive Data Governance Practices

Cloudera

Becoming a data-driven organization is not exactly getting any easier. Businesses are flooded with ever more data. Although it is true that more data enables more insight, the effort needed to separate the wheat from the chaff grows exponentially. Know what data you have. Better governance for better outcomes.

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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. Always Mindful of Privacy.

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How to build a successful hybrid cloud strategy

IBM Big Data Hub

Private cloud infrastructure is a dedicated cloud infrastructure operated solely for a single organization, either on-premises or hosted by a third party. Developer productivity : Enable DevOps and other teams to collaborate with greater agility and velocity. shifting legacy apps to the cloud).

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Four Ways Telcos Can Realize Data-Driven Transformation

Cloudera

To do this, telcos must reimagine their approach to data architecture: transitioning from legacy, siloed data architectures to a modern data architecture—anchored by a data platform able to integrate data across on-premises and cloud environments, and the network edge.

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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.