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How OLAP and AI can enable better business

IBM Big Data Hub

Online analytical processing (OLAP) database systems and artificial intelligence (AI) complement each other and can help enhance data analysis and decision-making when used in tandem. As AI techniques continue to evolve, innovative applications in the OLAP domain are anticipated.

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Simplify Online Analytical Processing (OLAP) queries in Amazon Redshift using new SQL constructs such as ROLLUP, CUBE, and GROUPING SETS

AWS Big Data

Solution overview Online Analytical Processing (OLAP) is an effective tool for today’s data and business analysts. An analyst can use OLAP aggregations to analyze buying patterns by grouping customers by demographic, geographic, and psychographic data, and then summarizing the data to look for trends.

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Best Reporting Tools List You Can’t Miss in 2020

FineReport

An excellent reporting tool will let you gather information conveniently and to have a comprehensive view of your business. The former is more professional in report making, presentation, and printing, while the latter can make OLAP and predict analysis thanks to the BI capabilities. As reporting software, it does not support OLAP.

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Business Intelligence vs. Reporting: Finding Your Bread and Butter

Jet Global

Also known as “analytics,” BI looks at more expansive data relationships, perhaps even between multiple systems that collect data (such as CRM and GP), and identifies trends that can inform strategic business decisions and objectives that will improve overall performance across the entire operation. BI is macro.

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Accelerating revenue growth with real-time analytics: Poshmark’s journey

AWS Big Data

The two main approaches organizations employ to increase revenue are to expand geographically to enter new markets and to increase market share within a market by improving customer experience (CX). Improving CX is a well-known guideline to attract and retain customers and thereby increase the market share.

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What Is Embedded Analytics?

Jet Global

Section 2: Embedded Analytics: No Longer a Want but a Need Section 3: How to be Successful with Embedded Analytics Section 4: Embedded Analytics: Build versus Buy Section 5: Evaluating an Embedded Analytics Solution Section 6: Go-to-Market Best Practices Section 7: The Future of Embedded Analytics Section 1: What are Embedded Analytics?