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What is data analytics? Analyzing and managing data for decisions

CIO Business Intelligence

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. Predictive analytics is often considered a type of “advanced analytics,” and frequently depends on machine learning and/or deep learning.

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What Leaders Want: Shifting to AI-Driven Healthcare

DataRobot Blog

The main themes emerging from our conversations cover data integration, security and humility, strategy, and workforce development: Join siloed data together to create longitudinal, ready-to-analyze datasets. The push to predictive and prescriptive analytics requires strategy and C-Suite ownership.

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Machine Learning and AI Underpin Predictive Analytics to Achieve Clinical Breakthroughs

Cloudera

For those asking big questions, in the case of healthcare, an incredible amount of insight remains hidden away in troves of clinical notes, EHR data, medical images, and omics data. To arrive at quality data, organizations are spending significant levels of effort on data integration, visualization, and deployment activities.

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Seven Steps to Success for Predictive Analytics in Financial Services

Birst BI

Every day, these companies pose questions such as: Will this new client provide a good return on investment, relative to the potential risk? Is this existing client a termination risk? A well-designed credit scoring algorithm will properly predict both the low- and high-risk customers. Automate the data processing sequence.

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10 Best Big Data Analytics Tools You Need To Know in 2023

FineReport

Predictive Analytics assesses the probability of a specific occurrence in the future, such as early warning systems, fraud detection, preventative maintenance applications, and forecasting. Prescriptive Analytics provides precise recommendations to respond to the query, “What should I do if ‘x’ occurs?”

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Four starting points to transform your organization into a data-driven enterprise

IBM Big Data Hub

This capability will provide data users with visibility into origin, transformations, and destination of data as it is used to build products. The result is more useful data for decision-making, less hassle and better compliance. Data integration. Start a trial. Start a trial. AI governance.

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Create an end-to-end data strategy for Customer 360 on AWS

AWS Big Data

Data ingestion You have to build ingestion pipelines based on factors like types of data sources (on-premises data stores, files, SaaS applications, third-party data), and flow of data (unbounded streams or batch data). Enrichment typically involves adding demographic, behavioral, and geolocation data.