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Case Study: Fitness Company Drives Growth With a Powerful Data Warehouse Solution

CDW Research Hub

By implementing a full complement of IBM Analytics solutions, and integrating IBM Cognos Analytics with the client’s Salesforce CRM solution, the client gained deeper insights into its customers. empowering franchisees to use data for business decision-making, and. The integration of the Cognos environment with.

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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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Data science vs data analytics: Unpacking the differences

IBM Big Data Hub

Though you may encounter the terms “data science” and “data analytics” being used interchangeably in conversations or online, they refer to two distinctly different concepts. Meanwhile, data analytics is the act of examining datasets to extract value and find answers to specific questions.

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Business Intelligence vs Data Science vs Data Analytics

FineReport

Definition: BI vs Data Science vs Data Analytics. Business Intelligence describes the process of using modern data warehouse technology, data analysis and processing technology, data mining, and data display technology for visualizing, analyzing data, and delivering insightful information.

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Data Visualization and Visual Analytics: Seeing the World of Data

Sisense

Data is usually visualized in a pictorial or graphical form such as charts, graphs, lists, maps, and comprehensive dashboards that combine these multiple formats. Data visualization is used to make the consuming, interpreting, and understanding data as simple as possible, and to make it easier to derive insights from data.

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Data science vs. machine learning: What’s the difference?

IBM Big Data Hub

It uses advanced tools to look at raw data, gather a data set, process it, and develop insights to create meaning. Areas making up the data science field include mining, statistics, data analytics, data modeling, machine learning modeling and programming.

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Seven Steps to Success for Predictive Analytics in Financial Services

Birst BI

The foundation of predictive analytics is based on probabilities. To generate accurate probabilities of future behavior, predictive analytics combine historical data from any number of applications with statistical algorithms. Add the predictive logic to the data model.