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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

Table of Contents 1) Benefits Of Big Data In Logistics 2) 10 Big Data In Logistics Use Cases Big data is revolutionizing many fields of business, and logistics analytics is no exception. The complex and ever-evolving nature of logistics makes it an essential use case for big data applications. Did you know?

Big Data 275
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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. Data ingestion and storage Retail businesses have event-driven data that requires action from downstream processes.

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Building Better Data Models to Unlock Next-Level Intelligence

Sisense

You can’t talk about data analytics without talking about data modeling. The reasons for this are simple: Before you can start analyzing data, huge datasets like data lakes must be modeled or transformed to be usable. Building the right data model is an important part of your data strategy.

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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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Agent Swarms – an evolutionary leap in intelligent automation

CIO Business Intelligence

The Agent Swarm evolution has been propelled by advancements in computing, artificial intelligence (AI), machine learning (ML), and the Internet of Things (IoT). This flexibility renders agent assemblies an essential element in contemporary automation strategies. AI, ML Decision-Making Layer Make decisions based on insights.

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