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What is data analytics? Analyzing and managing data for decisions

CIO Business Intelligence

What are the four types of data analytics? More specifically: Descriptive analytics uses historical and current data from multiple sources to describe the present state, or a specified historical state, by identifying trends and patterns. 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 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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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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Improve Underwriting Using Data and Analytics

Cloudera

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. Here too, I recommend an evolutionary, stepped approach for advancing your capabilities while learning as you go.

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Are You Getting The Most Out Of Your Marketing Data?

Smart Data Collective

It’s great to know what your customers have already done – what campaigns engage them and which they ignore, what they’ve already purchased, and so forth – but if you really want to outperform the competition, you need to think predictively. In recent years, though, there’s been significant growth in the use of predictive analytics.

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AIOps reimagines hybrid multicloud platform operations

IBM Big Data Hub

Specifically, AIOps uses big data, analytics, and machine learning capabilities to do the following: Collect and aggregate the huge and ever-increasing volumes of operations data generated by multiple IT infrastructure components, applications and performance-monitoring tools. Predictive analytics to show what will happen next.

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Disrupt and Innovate in a Data-Driven World

Cloudera

The private sector already very successfully uses data analytics and machine learning not only to realise efficiency gains but also – even more importantly – to create completely new services and business models. Gain improved intelligence on operating context and needs through expanded use of descriptive analytics techniques.