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Innovative data integration in 2024: Pioneering the future of data integration

CIO Business Intelligence

In the age of big data, where information is generated at an unprecedented rate, the ability to integrate and manage diverse data sources has become a critical business imperative. Traditional data integration methods are often cumbersome, time-consuming, and unable to keep up with the rapidly evolving data landscape.

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5 modern challenges in data integration and how CIOs can overcome them

CIO Business Intelligence

That’s just one of the many ways to define the uncontrollable volume of data and the challenge it poses for enterprises if they don’t adhere to advanced integration tech. As well as why data in silos is a threat that demands a separate discussion. This post handpicks various challenges for existing integration solutions.

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Enterprise Data Integration: Better Data, Smarter Decisions

Sisense

The first step towards doing that is to bring all your organization’s data: all your disparate datasets, wherever they live (on-prem and in a variety of cloud sources, no doubt) into an enterprise BI tool. All this data needs to come together in one place in order for the organization to reap the benefits of an enterprise BI tool.

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Salesforce debuts Zero Copy Partner Network to ease data integration

CIO Business Intelligence

Zero-copy integration eliminates the need for manual data movement, preserving data lineage and enabling centralized control fat the data source. Currently, Data Cloud leverages live SQL queries to access data from external data platforms via zero copy. CRM Systems, Data Management, Salesforce.com

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Data Virtualization: The Essential Tool for Security and Governance Manage Diverse Data Sources from a Single Point of Control

Corinium

This brief explains how data virtualization, an advanced data integration and data management approach, enables unprecedented control over security and governance. In addition, data virtualization enables companies to access data in real time while optimizing costs and ROI.

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8 data strategy mistakes to avoid

CIO Business Intelligence

Organizations can’t afford to mess up their data strategies, because too much is at stake in the digital economy. How enterprises gather, store, cleanse, access, and secure their data can be a major factor in their ability to meet corporate goals. Here are some data strategy mistakes IT leaders would be wise to avoid.

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Data governance in the age of generative AI

AWS Big Data

Data is your generative AI differentiator, and a successful generative AI implementation depends on a robust data strategy incorporating a comprehensive data governance approach. Data governance is a critical building block across all these approaches, and we see two emerging areas of focus.