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What is data analytics? Analyzing and managing data for decisions

CIO Business Intelligence

To ensure robust analysis, data analytics teams leverage a range of data management techniques, including data mining, data cleansing, data transformation, data modeling, and more. What are the four types of data analytics? In business analytics, this is the purview of business intelligence (BI). Data analytics examples.

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AIOps reimagines hybrid multicloud platform operations

IBM Big Data Hub

The AIOps engine is focused on addressing four key things: Descriptive analytics to show what happened in an environment. Predictive analytics to show what will happen next. Prescriptive analytics to show how to achieve or prevent the prediction. Diagnostics to show why it happened.

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Five Steps for Building a Successful BI Strategy

Sisense

Originating with Gartner, this chart includes the analytic features needed for a full analytics strategy, and what our AI team believe to be the absolute future of analytics – Cognitive Analytics. . In order to know where to go, you must first find yourself on this chart.

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How Freudenberg Home and Cleaning Solutions Makes Better Decisions with Enterprise-Wide Planning

CIO Business Intelligence

Shifting descriptive analytics to predictive analytics is a huge undertaking for most companies in their digital transformation.

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Incorporating Artificial Intelligence for Businesses : The Modern Approach to Data Analytics

BizAcuity

Enterprise Artificial intelligence (AI) is a common jargon used to refer to how an organization integrates artificial intelligence (AI) into its infrastructure to drive digital transformation. Artificial Intelligence Analytics.

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Decide to Decide Digitally: New Forrester Research

Decision Management Solutions

Conflating BPM and Digital Decisioning will not help you succeed with either or with the digital transformations they make possible. We often walk clients up a simple analytic sophistication curve: Model an operational decision and automate it using business rules based on policies, regulations and best practices.

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The Data Behind Tokyo 2020: The Evolution of the Olympic Games

Sisense

Descriptive analytics also help them understand the number of athletes and workers required to support that specific competition or sport. “We get access, post-Games, to the ticket data to analyze any patterns in terms of incidents and responses.”.