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The 4 Common Challenges of Predictive Analytics Solutions

Jet Global

Predictive analytics is a branch of analytics that uses historical data, machine learning, and Artificial Intelligence (AI) to help users act preemptively. Predictive analytics answers this question: “What is most likely to happen based on my current data, and what can I do to change that outcome?”

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The Benefits, Challenges and Risks of Predictive Analytics for Your Application

Jet Global

In this modern, turbulent market, predictive analytics has become a key feature for analytics software customers. Predictive analytics refers to the use of historical data, machine learning, and artificial intelligence to predict what will happen in the future.

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Using Business Intelligence in Demand Forecasting

Jet Global

With major advances being made in artificial intelligence and machine learning, businesses are investing heavily in advanced analytics to get ahead of the competition and increase their bottom line. We’ll explain what it is, how it works, and ways to start using demand forecasting with business intelligence software.

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Using Business Intelligence in Demand Forecasting

Jet Global

With major advances being made in artificial intelligence and machine learning, businesses are investing heavily in advanced analytics to get ahead of the competition and increase their bottom line. We’ll explain what it is, how it works, and ways to start using demand forecasting with business intelligence software.

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Digital Transformation is a Data Journey From Edge to Insight

Cloudera

Most of what is written though has to do with the enabling technology platforms (cloud or edge or point solutions like data warehouses) or use cases that are driving these benefits (predictive analytics applied to preventive maintenance, financial institution’s fraud detection, or predictive health monitoring as examples) not the underlying data.

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What are decision support systems? Sifting data for better business decisions

CIO Business Intelligence

A DSS leverages a combination of raw data, documents, personal knowledge, and/or business models to help users make decisions. The data sources used by a DSS could include relational data sources, cubes, data warehouses, electronic health records (EHRs), revenue projections, sales projections, and more.

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Biggest Trends in Data Visualization Taking Shape in 2022

Smart Data Collective

Some solutions provide read and write access to any type of source and information, advanced integration, security capabilities and metadata management that help achieve virtual and high-performance Data Services in real-time, cache or batch mode. How does Data Virtualization complement Data Warehousing and SOA Architectures?