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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.

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A Data Prediction for 2025

DataKitchen

What will the world of data tools be like at the end of 2025? The crazy idea is that data teams are beyond the boom decade of “spending extravagance” and need to focus on doing more with less. What will exist at the end of 2025? Central IT Data Teams focus on standards, compliance, and cost reduction.

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

DataKitchen

Productivity does not come through the fingers of each data engineer; it comes from building a system around those engineers that allows them to run production with minimal errors and move things into production quickly, with low risk, so they can focus on making their customers successful. Summary: 10x your data engineering game.

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13 Analytics & Business Intelligence Examples Illustrating The Value of BI

datapine

Digital data, by its very nature, paints a clear, concise, and panoramic picture of a number of vital areas of business performance, offering a window of insight that often leads to creating an enhanced business intelligence strategy and, ultimately, an ongoing commercial success. billion , growing at a CAGR of 26.98% from 2016.

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How to choose the best AI platform

IBM Big Data Hub

.” When observing its potential impact within industry, McKinsey Global Institute estimates that in just the manufacturing sector, emerging technologies that use AI will by 2025 add as much as USD 3.7 Visual modeling: Combine visual data science with open source libraries and notebook-based interfaces on a unified data and AI studio.

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The Gartner 2021 Leadership Vision for Data & Analytics Leaders Webinar Q&A

Andrew White

As such any Data and Analytics strategy needs to incorporate data sovereignty as per of its D&A governance program. Coding skills – SQL, Python or application familiarity – ETL & visualization? measuring value, prioritizing (where to start), and data literacy? Governance. Product Management.