AWS Data Quality Data Silos 
AWS Machine Learning Blog
APRIL 3, 2025
At the heart of this transformation is the OMRON Data & Analytics Platform (ODAP), an innovative initiative designed to revolutionize how the company harnesses its data assets. The robust security features provided by Amazon S3, including encryption and durability, were used to provide data protection.
NOVEMBER 22, 2024
It serves as the hub for defining and enforcing data governance policies, data cataloging, data lineage tracking, and managing data access controls across the organization. Data lake account (producer) – There can be one or more data lake accounts within the organization.
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IBM Journey to AI blog
MARCH 14, 2024
Businesses face significant hurdles when preparing data for artificial intelligence (AI) applications. The existence of data silos and duplication, alongside apprehensions regarding data quality, presents a multifaceted environment for organizations to manage.
Precisely
FEBRUARY 9, 2024
They’re where the world’s transactional data originates – and because that essential data can’t remain siloed, organizations are undertaking modernization initiatives to provide access to mainframe data in the cloud. That approach assumes that good data quality will be self-sustaining.
Precisely
MAY 16, 2024
Watch the webinar AI You Can Trust Watch this webinar and see how we explore organizational challenges in maintaining data integrity for AI applications and real-world use cases showcasing the transformative impact of high-integrity data on AI success. Fuel your AI applications with trusted data to power reliable results.
Precisely
MARCH 4, 2024
How it’s done : An AI recommender system is a sophisticated technology that leverages AI and vast amounts of user data – like past preferences, behaviors, and interactions – to suggest tailored products, content, or services. You need to break down data silos and integrate critical data from all relevant sources.
IBM Journey to AI blog
MAY 15, 2024
Organizations require reliable data for robust AI models and accurate insights, yet the current technology landscape presents unparalleled data quality challenges. This situation will exacerbate data silos, increase costs and complicate the governance of AI and data workloads.
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