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6 ways generative AI can optimize asset management

IBM Big Data Hub

Using a hybrid AI or machine learning (ML) model, you can train it on enterprise and published data, including newly acquired assets and sites. Through interactive dialog, it can generate visual analytics and promptly deliver content to your team. They require job plans and work instructions for asset failures and repairs.

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The Power of Graph Databases, Linked Data, and Graph Algorithms

Rocket-Powered Data Science

I wrote an extensive piece on the power of graph databases, linked data, graph algorithms, and various significant graph analytics applications. Now, for the first time, the full unabridged (and unedited) version of my initial contribution as the Foreword for the book is published here. The campaign looks like a failure.

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The Failed Promises of Digital Transformation and What to Do About It

Ontotext

Graph technologies refer to a different way of storing and analyzing data that enables answering questions from data that were previously impossible. ” (See “Market Guide for Graph Database Management Systems”, Published 30 August 2022, by Merv Adrian and Afraz Jaffri).

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Ingest, transform, and deliver events published by Amazon Security Lake to Amazon OpenSearch Service

AWS Big Data

When it comes to near-real-time analysis of data as it arrives in Security Lake and responding to security events your company cares about, Amazon OpenSearch Service provides the necessary tooling to help you make sense of the data found in Security Lake. You can use the visualizations after you start importing data.

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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. Build components that are idempotent on data.

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7 famous analytics and AI disasters

CIO Business Intelligence

Organizations across every industry have been and continue to invest heavily in data and analytics. But like oil, data and analytics have their dark side. Here are a handful of high-profile analytics and AI blunders from the past decade to illustrate what can go wrong. Target analytics violated privacy.

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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.