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What Role Does Data Mining Play for Business Intelligence?

Jet Global

Data drives everything in the business world, from manufacturing to supply chain logistics to retail sales to customer experience to post-sale marketing and beyond, data holds the secrets to making processes more efficient, production costs cheaper, profit margins higher and marketing campaigns more effective.

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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. Such analysis can provide insight into customer preferences and behavior, which can be used to inform marketing strategies and product development.

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Financial Intelligence vs. Business Intelligence: What’s the Difference?

Jet Global

CRM software has gone through a similar transformation, starting with sales force automation, and more recently evolving into a new breed of products that support digital marketing campaigns through email, social media, and online advertising.

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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. Choosing the Right Tech. If it’s all just semantics, why does this matter?

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Unlocking Data Storage: The Traditional Data Warehouse vs. Cloud Data Warehouse

Sisense

As the number of cloud data warehouse options on the market grows, niche players will rise and fall in every industry, with companies choosing this or that cloud option based on its ability to handle their data uniquely well. The primary differentiator is the data workload they serve.

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Navigating Data Entities, BYOD, and Data Lakes in Microsoft Dynamics

Jet Global

Data warehouses gained momentum back in the early 1990s as companies dealing with growing volumes of data were seeking ways to make analytics faster and more accessible. Online analytical processing (OLAP), which enabled users to quickly and easily view data along different dimensions, was coming of age.

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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. Improved problem-solving : OLAP provides insights into the root causes of problems, enabling businesses to address issues more effectively.

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