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External Data Supports More Accurate Planning

David Menninger's Analyst Perspectives

Enterprises do not operate in a vacuum, and things happening outside an organizations walls directly impact performance. So, it is essential to incorporate external data in forecasting, planning and budgeting, especially for predictive analytics and machine learning to support artificial intelligence.

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Rapidminer Platform Supports Entire Data Science Lifecycle

David Menninger's Analyst Perspectives

Rapidminer is a visual enterprise data science platform that includes data extraction, data mining, deep learning, artificial intelligence and machine learning (AI/ML) and predictive analytics. It can support AI/ML processes with data preparation, model validation, results visualization and model optimization.

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The key to operational AI: Modern data architecture

CIO Business Intelligence

From customer service chatbots to marketing teams analyzing call center data, the majority of enterprises—about 90% according to recent data —have begun exploring AI. Today, enterprises are leveraging various types of AI to achieve their goals. Learn more about how Cloudera can support your enterprise AI journey here.

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Assisted Predictive Modeling Guide Users Through the Maze

Smarten

What is Assisted Predictive Modeling? Assisted Predictive Modeling is a great way to provide support for your users and your organization. Yes, plug n’ play predictive analysis must truly be plug and play! Predictive analysis does not have to be tortuous or confusing.

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Top 10 Data Innovation Trends During 2020

Rocket-Powered Data Science

Customer purchase patterns, supply chain, inventory, and logistics represent just a few domains where we see new and emergent behaviors, responses, and outcomes represented in our data and in our predictive models. This is critical in our massively data-sharing world and enterprises. will look like).

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Predictive Analytics: 4 Primary Aspects of Predictive Analytics

Smart Data Collective

Predictive analytics, sometimes referred to as big data analytics, relies on aspects of data mining as well as algorithms to develop predictive models. These predictive models can be used by enterprise marketers to more effectively develop predictions of future user behaviors based on the sourced historical data.

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The quest for high-quality data

O'Reilly on Data

Even with advances in building robust models, the reality is that noisy data and incomplete data remain the biggest hurdles to effective end-to-end solutions. The problem is even more magnified in the case of structured enterprise data. Machine learning applications rely on three main components: models, data, and compute.