Remove Dashboards Remove Metrics Remove Online Analytical Processing Remove Presentation
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The Enterprise AI Revolution Starts with BI

Jet Global

But the vast reams of data generated daily are presenting a new problem for businesses—what data matters? The optimized data warehouse isn’t simply a number of relational databases cobbled together, however—it’s built on modern data storage structures such as the Online Analytical Processing (or OLAP) cubes.

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What’s the Difference Between Business Intelligence and Business Analytics?

Sisense

BI lets you apply chosen metrics to potentially huge, unstructured datasets, and covers querying, data mining , online analytical processing ( OLAP ), and reporting as well as business performance monitoring, predictive and prescriptive analytics. See an example: Explore Dashboard. Confused yet?

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The Future of AI in the Enterprise

Jet Global

But the vast reams of data generated on a daily basis are presenting a new problem for businesses—what data matters? The optimized data warehouse isn’t simply a number of relational databases cobbled together, however—it’s built on modern data storage structures such as the Online Analytical Processing (or OLAP) cubes.

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The Future of AI in the Enterprise

Jet Global

But the vast reams of data generated on a daily basis are presenting a new problem for businesses—what data matters? The optimized data warehouse isn’t simply a number of relational databases cobbled together, however—it’s built on modern data storage structures such as the Online Analytical Processing (or OLAP) cubes.

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What is business intelligence? Transforming data into business insights

CIO Business Intelligence

BI tools access and analyze data sets and present analytical findings in reports, summaries, dashboards, graphs, charts, and maps to provide users with detailed intelligence about the state of the business. Business intelligence examples Reporting is a central facet of BI and the dashboard is perhaps the archetypical BI tool.

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Unleashing the power of Presto: The Uber case study

IBM Big Data Hub

As a result, they continue to expand their use cases to include ETL, data science , data exploration, online analytical processing (OLAP), data lake analytics and federated queries. It can ingest data from offline batch data sources (such as Hadoop and flat files) as well as online data sources (such as Kafka).

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