Remove Descriptive Analytics Remove Machine Learning Remove Risk
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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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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).

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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.

Analytics 102
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What Is The Difference Between Business Intelligence And Analytics?

datapine

While BI tells you what has happened in the past and what is happening now (descriptive analytics), BA tells you what will happen in the future (predictive analytics). Descriptive analytics : As its name suggests, this analysis method is used to describe and summarize the main characteristics found on a dataset.

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Decide to Decide Digitally: New Forrester Research

Decision Management Solutions

DDPs accomplish this by providing a suite of capabilities that enable business subject-matter experts to define decision logic, incorporate data-driven decision intelligence technologies such as machine learning (ML), govern change, and deploy digital decisions within business applications.

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Themes and Conferences per Pacoid, Episode 10

Domino Data Lab

Secondly, I talked backstage with Michelle, who got into the field by working on machine learning projects, though recently she led data infrastructure supporting data science teams. Just doing machine learning is not enough, and sometimes not even necessary.”. First off, her slides are fantastic! Nick Elprin.

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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.