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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

The growing volume of data is a concern, as 20% of enterprises surveyed by IDG are drawing from 1000 or more sources to feed their analytics systems. Data integration needs an overhaul, which can only be achieved by considering the following gaps. Heterogeneous sources produce data sets of different formats and structures.

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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. Ground generative AI.

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8 data strategy mistakes to avoid

CIO Business Intelligence

“Similar to disaster recovery, business continuity, and information security, data strategy needs to be well thought out and defined to inform the rest, while providing a foundation from which to build a strong business.” Overlooking these data resources is a big mistake. It will not be something they can ignore.

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How to successfully integrate data in a hybrid environment

CIO Business Intelligence

In today’s data-driven world, the ability to seamlessly integrate structured and unstructured data in a hybrid cloud environment is critical for organizations seeking to harness the full potential of their data assets.

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Straumann Group is transforming dentistry with data, AI

CIO Business Intelligence

The Basel, Switzerland-based company, which operates in more than 100 countries, has petabytes of data, including highly structured customer data, data about treatments and lab requests, operational data, and a massive, growing volume of unstructured data, particularly imaging data.

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5 Ways Data Modeling Is Critical to Data Governance

erwin

They also face increasing regulatory pressure because of global data regulations , such as the European Union’s General Data Protection Regulation (GDPR) and the new California Consumer Privacy Act (CCPA), that went into effect last week on Jan. Data modeling captures how the business uses data and provides context to the data source.