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AzureML and CRISP-DM – a Framework to help the Business Intelligence professional move to AI

Jen Stirrup

Azure ML can become a part of the data ecosystem in an organization, but this requires a mindshift from working with Business Intelligence to more advanced analytics. How can we can adopt a mindshift from Business Intelligence to advanced analytics using Azure ML? AI vs ML vs Data Science vs Business Intelligence.

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How to use foundation models and trusted governance to manage AI workflow risk

IBM Big Data Hub

It includes processes that trace and document the origin of data, models and associated metadata and pipelines for audits. Foundation models: The power of curated datasets Foundation models , also known as “transformers,” are modern, large-scale AI models trained on large amounts of raw, unlabeled data.

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What is data analytics? Analyzing and managing data for decisions

CIO Business Intelligence

Data analytics draws from a range of disciplines — including computer programming, mathematics, and statistics — to perform analysis on data in an effort to describe, predict, and improve performance. What are the four types of data analytics? In business analytics, this is the purview of business intelligence (BI).

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What is business analytics? Using data to improve business outcomes

CIO Business Intelligence

Business analytics is the practical application of statistical analysis and technologies on business data to identify and anticipate trends and predict business outcomes. What are the benefits of business analytics? What is the difference between business analytics and business intelligence?

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Making OT-IT integration a reality with new data architectures and generative AI

CIO Business Intelligence

A number of industry leaders are already experimenting with advanced AI use cases, including Denso, a leading mobility supplier that develops advanced technology and components for nearly every vehicle make and model on the road today. Denso uses AI to verify the structuring of unstructured data from across its organisation.

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

datapine

The rise of SaaS business intelligence tools is answering that need, providing a dynamic vessel for presenting and interacting with essential insights in a way that is digestible and accessible. The future is bright for logistics companies that are willing to take advantage of big data. Now’s the time to strike.

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The Ultimate Guide to Modern Data Quality Management (DQM) For An Effective Data Quality Control Driven by The Right Metrics

datapine

Therefore, there are several roles that need to be filled, including: DQM Program Manager: The program manager role should be filled by a high-level leader who accepts the responsibility of general oversight for business intelligence initiatives. The program manager should lead the vision for quality data and ROI.