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8 data strategy mistakes to avoid

CIO Business Intelligence

Organizations can’t afford to mess up their data strategies, because too much is at stake in the digital economy. How enterprises gather, store, cleanse, access, and secure their data can be a major factor in their ability to meet corporate goals. Here are some data strategy mistakes IT leaders would be wise to avoid.

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

datapine

In an interview with the Wall Street Journal, Matthias Winkenbach , director of MIT’s Megacity Logistics Lab, details how last-mile analytics are yielding useful data. After parking nearby, the delivery man’s phone GPS continues to stream data to the UPS center, giving a constant account of how long the delivery is taking.

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The 10 biggest issues IT faces today

CIO Business Intelligence

“Those things aren’t necessarily new, but they’re more important now than ever before,” says Hamit, a member of the Emerging Trends Working Group with the IT governance association ISACA. 1 priority within the CIO function is cybersecurity strategies, up from the second spot in 2021. Maturing the enterprise cloud strategy.

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Reference guide to build inventory management and forecasting solutions on AWS

AWS Big Data

Such a solution should use the latest technologies, including Internet of Things (IoT) sensors, cloud computing, and machine learning (ML), to provide accurate, timely, and actionable data. In the inventory management and forecasting solution, AWS Glue is recommended for data transformation.

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Gain insights from historical location data using Amazon Location Service and AWS analytics services

AWS Big Data

The solution consists of the following interfaces: IoT or mobile application – A mobile application or an Internet of Things (IoT) device allows the tracking of a company vehicle while it is in use and transmits its current location securely to the data ingestion layer in AWS. The ingestion approach is not in scope of this post.

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Smart Factories: Artificial Intelligence and Automation for Reduced OPEX in Manufacturing

DataRobot Blog

This “revolution” stems from breakthrough advancements in artificial intelligence, robotics, and the Internet of Things (IoT). This is currently a widespread strategy across the industry where we are seeing companies move from reactive to predictive inventory management and capacity planning. Factory Monitoring?—?

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Migrate from Amazon Kinesis Data Analytics for SQL Applications to Amazon Kinesis Data Analytics Studio

AWS Big Data

Kinesis Data Analytics for Apache Flink In our example, we perform the following actions on the streaming data: Connect to an Amazon Kinesis Data Streams data stream. View the stream data. Transform and enrich the data. Manipulate the data with Python.