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The Future of AI in the Enterprise

Jet Global

While the technology behind enabling computers to simulate human thought has been developing, at times slowly, over the past half-century, the cost of implementation, readily available access to cloud computing, and practical business use cases are primed to help AI make a dramatic impact in the enterprise over the next few years.

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

Sisense

The traditional data warehouses solved the problem of processing and synthesizing large data volumes, but they presented new challenges for the analytics process. Cloud data warehouses took the benefits of the cloud and applied them to data warehouses — bringing massive parallel processing to data teams of all sizes.

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The Future of AI in the Enterprise

Jet Global

While the technology behind enabling computers to simulate human thought has been developing, at times slowly, over the past half-century, the cost of implementation, readily available access to cloud computing, and practical business use cases are primed to help AI make a dramatic impact in the enterprise over the next few years.

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What is business intelligence? Transforming data into business insights

CIO Business Intelligence

Benefits of BI BI helps business decision-makers get the information they need to make informed decisions. The potential use cases for BI extend beyond the typical business performance metrics of improved sales and reduced costs.

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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. Identifying best practices and benefits In the realm of OLAP, AI’s role is increasingly important.

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Data Model Development Using Jinja

Sisense

Data warehouses provide a consolidated, multidimensional view of data along with online analytical processing ( OLAP ) tools. OLAP tools help in the interactive and effective processing of data in a multidimensional space. The benefits of DBT with Jinja. DBT + Sisense: A powerful combination.

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How to Build a Performant Data Warehouse in Redshift

Sisense

First, we’ll dive into the two types of databases: OLAP (Online Analytical Processing) and OLTP (Online Transaction Processing). This approach made sense during a time in which the cost of storage was high, so normalizing tables reduced the total footprint. So let’s dive in! OLTP vs OLAP.