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Three Types of Actionable Business Analytics Not Called Predictive or Prescriptive

Rocket-Powered Data Science

In the enterprise, sentinel analytics is most timely and beneficial when applied to real-time, dynamic data streams and time-critical decisions. The analytics triage is critical, to avoid alarm fatigue (sending too many unimportant alerts) and to avoid underreporting of important actionable events. Pay attention!

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Data science vs data analytics: Unpacking the differences

IBM Big Data Hub

Overview: Data science vs data analytics Think of data science as the overarching umbrella that covers a wide range of tasks performed to find patterns in large datasets, structure data for use, train machine learning models and develop artificial intelligence (AI) applications.

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What is data analytics? Analyzing and managing data for decisions

CIO Business Intelligence

The chief aim of data analytics is to apply statistical analysis and technologies on data to find trends and solve problems. Data analytics has become increasingly important in the enterprise as a means for analyzing and shaping business processes and improving decision-making and business results.

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What is business analytics? Using data to improve business outcomes

CIO Business Intelligence

Business analytics and business intelligence (BI) serve similar purposes and are often used as interchangeable terms, but BI can be considered a subset of business analytics. Whereas BI studies historical data to guide business decision-making, business analytics is about looking forward. Business analytics techniques.

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Python for Business: Optimize Pre-Processing Data for Decision-Making

Smart Data Collective

The rise of machine learning and the use of Artificial Intelligence gradually increases the requirement of data processing. That’s because the machine learning projects go through and process a lot of data, and that data should come in the specified format to make it easier for the AI to catch and process.

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Improve Underwriting Using Data and Analytics

Cloudera

In this post, I’ll explore opportunities to enhance risk assessment and underwriting, especially in personal lines and small and medium-sized enterprises. To me, this means that by applying more data, analytics, and machine learning to reduce manual efforts helps you work smarter. Step two: expand machine learning and AI.

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AIOps reimagines hybrid multicloud platform operations

IBM Big Data Hub

Today, most enterprises use services from more than one Cloud Service Provider (CSP). IT is a critical part of every enterprise today, and even a small service outage directly affects the top line. The AIOps engine is focused on addressing four key things: Descriptive analytics to show what happened in an environment.