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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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Straumann Group is transforming dentistry with data, AI

CIO Business Intelligence

Selling the value of data transformation Iyengar and his team are 18 months into a three- to five-year journey that started by building out the data layer — corralling data sources such as ERP, CRM, and legacy databases into data warehouses for structured data and data lakes for unstructured data.

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Trending Technologies for BI & Financial Planning and AnalysisMaking AI Real (Part 2)

Jedox

Now, we will take a deeper look into AI, Machine learning and other trending technologies and the evolution of data analytics from descriptive to prescriptive. Analytic Evolution in Enterprise Performance Management. This is known as prescriptive analytics. advanced analytics.

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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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What is the Future of Business Intelligence in the Coming Year?

Smart Data Collective

The current BI trends show that in the future, the BI software will be more accessible, so that even non-techie workers will rely on data insights in their working routine. Prescriptive Analytics. Advantage: unpaired control over data. . This shows why self-service BI is on the rise. QlickSense.

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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

Automate the data processing sequence. With connectivity, data integration and the predictive algorithm in place, schedule the entire process to update on a daily or more frequent basis. Having the most recent data from all sources ensures the predictive model will generate the most accurate predictions.