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How to accelerate your data monetization strategy with data products and AI

IBM Journey to AI blog

Data monetization strategy: Managing data as a product Every organization has the potential to monetize their data; for many organizations, it is an untapped resource for new capabilities. But few organizations have made the strategic shift to managing “data as a product.”

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Must Know Differences Between Data Science and Data Analytics

Pickl AI

Summary: The difference between Data Science and Data Analytics lies in their approachData Science uses AI and Machine Learning for predictions, while Data Analytics focuses on analysing past trends. Data Science requires advanced coding, whereas Data Analytics relies on statistical methods.

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Understanding the Synergy Between Artificial Intelligence & Data Science

Pickl AI

Summary: The blog explores the synergy between Artificial Intelligence (AI) and Data Science, highlighting their complementary roles in Data Analysis and intelligent decision-making. This article explores how AI and Data Science complement each other, highlighting their combined impact and potential.

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How to choose the best AI platform

IBM Journey to AI blog

.” 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 AI technology is quickly proving to be a critical component of business intelligence within organizations across industries.

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

IBM Journey to AI blog

It encompasses risk management and regulatory compliance and guides how AI is managed within an organization. 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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Financial Data & AI: The Future of Business Intelligence

Defined.ai blog

It can quickly process large amounts of data, precisely identifying patterns and insights humans might overlook. Businesses can transform raw numbers into actionable insights by applying AI. For instance, an AI model can predict future sales based on past data, helping businesses plan better.

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Exploring the AI and data capabilities of watsonx

IBM Journey to AI blog

With watsonx.ai, businesses can effectively train, validate, tune and deploy AI models with confidence and at scale across their enterprise. IBM watsonx.ai: enterprise-ready next-generation studio bringing together traditional machine learning (ML) and new generative AI capabilities powered by foundation models.