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Data Teams and Their Types of Data Journeys

DataKitchen

Data Teams and Their Types of Data Journeys In the rapidly evolving landscape of data management and analytics, data teams face various challenges ranging from data ingestion to end-to-end observability. It explores why DataKitchen’s ‘Data Journeys’ capability can solve these challenges.

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Back to the Financial Regulatory Future

Cloudera

Cultural shift and technology adoption: Traditional banks and insurance companies must adapt to the emergence of fintech firms and changing business models. Financial institutions must demonstrate robust risk accountability and governance, as well as maintain consumer protections.

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Constructing A Digital Transformation Strategy: Putting the Data in Digital Transformation

erwin

Part Two of the Digital Transformation Journey … In our last blog on driving digital transformation , we explored how enterprise architecture (EA) and business process (BP) modeling are pivotal factors in a viable digital transformation strategy. Constructing A Digital Transformation Strategy: Data Enablement.

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Usability and Connecting Threads: How Data Fabric Makes Sense Out of Disparate Data

Ontotext

After all, when businesses lack domain context, and unified semantics hinder data usage within the organization, a data fabric approach can be a game-changer. Major goals of data fabric include: Create smart semantic data integration and engineering: with governed access to improve findability and comprehensibility of data.

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Analytics is changing. How are you keeping pace?

CIO Business Intelligence

Migration works best by considering the guardrails and processes needed to collect data, store it with the appropriate security and governance models, and then accelerate innovation,” Toner said. AWS doesn’t recommend that organizations try to completely re-create its on-premises environment in the cloud.

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How to choose the best AI platform

IBM Big Data Hub

Artificial intelligence platforms enable individuals to create, evaluate, implement and update machine learning (ML) and deep learning models in a more scalable way. AI platform tools enable knowledge workers to analyze data, formulate predictions and execute tasks with greater speed and precision than they can manually.

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Amazon Redshift announcements at AWS re:Invent 2023 to enable analytics on all your data

AWS Big Data

These announcements drive forward the AWS Zero-ETL vision to unify all your data, enabling you to better maximize the value of your data with comprehensive analytics and ML capabilities, and innovate faster with secure data collaboration within and across organizations.